1st International ConferenceonBusiness and Technological AdvancementinIndustrial Revolution 4.0(ICoBTA-IR4.0)
2023

Industrial Revolution 4.0:
Challenges for a Sustainable Future

e-proceedings Book3 – 4 October 2023
Riverside Majestic Hotel,
Kuching, Sarawak, Malaysia

e-proceedings book ICoBTA-IR4.0

e-Proceedings Book
1st International ConferenceonBusiness and Technological AdvancementinIndustrial Revolution 4.0(ICoBTA-IR4.0)
2023

Copyright © by Universiti Teknologi MARA Cawangan Sarawak
Dec 2023

All rights reserved. No parts of this publication may be reproduced, distributed,
or transmitted in any form or by any means, including photocopying, recording,or other electronic or mechanical methods, without prior written permission of
the publisher, except in the case of brief quotations embodied in critical reviews
and certain other non-commercial uses permitted by copyright law. Authors are
responsible both ethically and juridically. For permission requests, write to the
publisher at the address below.
e-Proceedings Booke ISBN 978-629-98951-1-4
Universiti Teknologi MARA Cawangan Sarawak
Jalan Meranek 94300 Kota Samarahan, Sarawak, Malaysia.
Tel: +6082677200 / +6082775500
Fex: +6082677300
https://sarawak.uitm.edu.my
http://www.icobta.net/

e-proceedings book ICoBTA-IR4.0

ADVISOR
Professor Dato Dr Hj Jamil Hj Hamali

ERASMUS+ IND4.0 PROJECT COORDINATOR
Associate Professor Dr Margaret Chan Kit Yok

EDITORIAL BOARD
Chief Editor
Ling Siew Ching

Committee
Dr Tuh Moi Hua
Dr Lee Siaw Li
Dr Christina Eu Nguk Ling
Shirley Sinatra Gran
Aimi Nuraida Ali Hassan
Agnes Ayang Anak Kenyang
Lai Tze Wee
Noormuthaah Mohamad Ali Adaha

COVER DESIGNER
Dr Mohd Syahiran Abd Latifiii

e-proceedings book ICoBTA-IR4.0

Preface

PREFACEThe emergence of the Fourth Industrial Revolution in the past few decades haschanged the technology, industries, and societal patterns and processes in the 21st
century due to increasing interconnectivity and smart automation. Thus, the
ERASMUS+ IND4.0 with 15 partners’ consortium co-funded by the ERAMUS+ Key
Action 2 Capacity Building in Higher Education aim is to provide practical solutions
so as to help close the gap between industry needs/expectations and educational
system outputs in relation to Industry 4.0 in the target countries. In this context, theconsortium has transferred significant know-how from the European Educational
System, which the partner country Institutions in Asia used to design an advanced,
practical-based MSc Curriculum for Industry 4.0. This is to build the capacity ofHigher Education Institutions in the partner countries, improve the level ofcompetencies and skills offered, and address the absence of a similar programme.
A final conference, the 1st INTERNATIONAL CONFERENCE OF BUSINESS ANDTECHNOLOGICAL ADVANCES IN INDUSTRY REVOLUTION 4.0, organised aspart of the dissemination and exploitation work package, showcased all partners’
contributions to its success. The theme was Industrial Revolution 4.0: Challenges fora Sustainable Future. The objectives were:
1. To disseminate the curriculum development of higher education in Industrial 4.0.
2. To interact and share information on the latest developments of Industry 4.0.
3. To deliberate recent innovations, trends, and concerns in theory and practice aswell as practical challenges encountered, and solutions adopted in Smart
Industries and the Fourth Industrial Revolution.
4. Discuss challenges imposed by cultural, legacy and socio-political factors for
Industry 4.0.
The e-proceedings Book documented the 17 papers presented during theconference. The papers were all peer-reviewed. The Editorial Board would like tothank the authors of the papers, the organising committee, the scientific committee
and the reviewers for their work with the conference and this book of proceedings.
This book of proceeding is only available as an e-publication and can be found at
the following link: http://www.icobta.net/
To cite this book of proceedings: Name, N. and Name, N. (2023). Title of the paper.
In e-Proceedings Book of 1st International Conference of Business andTechnological Advances in Industrial Revolution 4.0. Kuching, Sarawak, Malaysia(pp. xx-yy). Retrieved from http://www.icobta.net/

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Table of Contents

Table of Contents:

INTRODUCTORY SPEECH
Associate Professor Dr Margaret Chan Kit Yok
Project Coordinator of ERASMUS+ InD4.0 on Capacity Building in Higher Education
Universiti Teknologi Mara, Sarawak Branch

WELCOMING ADDRESS

Professor Dato Dr Hj Jamil Hj Hamali
Rector, Universiti Teknologi Mara, Sarawak Branch

KEYNOTE SPEECH OF THE RIGHT HONOURABLE
Datuk Patinggi Dr Haji Abang Abdul Rahman Zohari bin Tun Datuk Abang Haji
Openg
Premier of Sarawak

SESSION 1: IND4.0 IN BUSINESS
Development of Islamic Finance in Industry 4.0 Era: Challenges, Criticism andOpportunities
Maizaitulaidawati Md Husin, Nor Aiza Mohd Zamil, Haliyana Khalid
Universiti Teknologi Malaysia, Malaysia

1Enhancing Employee Experience in Expenditure Enquiry Management of Global Service
Centre Through Chatbot: An Action Research in Multinational Company
Maria Ahmad, Fauziah Sh. Ahmad
Universiti Teknologi Malaysia, Malaysia

3

Bridging Local Womenpreneurs' knowledge and Application Toward SME businessdigitalisation
Jasmine David, Rozita @ Uji Mohammed, Agnes Paulus Jidwin, Dewi Tajuddin,
Jacqueline Koh Siew Len
Universiti Teknologi MARA Sabah, Malaysia

22

SESSION 2: IND4.0 IN PERVASIVE HEALTH
Impact Analysis of Clahe and Um Based Image Quality Improvement on Diabetic
Retinopathy Disease Classification
Nurul Muthmainnah Sabrini, Kahlil Muchtar, Maulisa Oktiana, Roslidar Roslidar,
Wildan Mumtaz, Novi Maulina
Universitas Syiah Kuala, Indonesia

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SESSION 3: ECOSYSTEM AND INFRASTRUCTURE EFFICIENCY(ARTIFICIAL INTELLIGENCE, INTERNET OF THINGS (IOT))
A Framework for Non-Homogeneous Data Generation in Federated Machine Learning
Daniel Mauricio Jimenez Gutierrez, Aris Anagnostopoulos, Ioannis
Chatzigiannakis, Andrea Vitaletti
Sapienza University of Rome, Italy

32

Development and Implementation of Smart IoT-Based Aquascape Monitoring System
Muhamad Amjad Mohd Azmi, Ghulam Fizza, Haidawati Nasir
Universiti Kuala Lumpur, Malaysia

36

SESSION 4: IND4.0 IN AGRICULTURE
A Preliminary Results of a Deep Learning Model for Classifying Watermelon Sweetness
Through Field Spot Detection
Maya Fitria, Muhammad Hafez Al-Assad, Yudi Candra, Silvia Roza, Rahmad
DawoodUniversitas Syiah Kuala, Indonesia

43

LEEAF: Olive Leaf Infection Detection Using Artificial Intelligence
Themistoklis Sarantakos, Daniel Mauricio Jimenez Gutierrez, Dimitrios Amaxilatis,
Ioannis Chatzigiannakis
Spark Works Ltd, Galway, Ireland & Sapienza University of Rome, Italy

57

Image Detection Using YOLOv7 Algorithm to Detect the Moulted Crabs
Mohammad Affaiq Aini, Lee Beng Yong, Yong Yueh Tiam, Leong Siow Hoo, Khong
Heng Yen, Firdaus Abdullah, Sharifah Raina Manaf, Chua Tze Nan
Universiti Teknologi MARA Sarawak Branch, Malaysia

61

Measuring Leaf Area Using AutoCad
Seraphina ak Dominic, Margaret Chan Kit Yok, Siraj Munir bin Mohammad
Universiti Teknologi MARA Sarawak Branch, Malaysia

62

SESSION 5: IND4.0 IN EDUCATIONvi

A Short Course on IoT
Ioannis Chatzigiannakis, Andrea Vitaletti
Sapienza University of Rome, Italy

68

Sector Skills Gaps Findings in Industrial 4.0 (IR4.0)
Margaret Chan Kit Yok, Ling Siew Ching
Universiti Teknologi MARA Sarawak Branch, Malaysia

75

A Roadmap to Industrial Revolution 4.0: Preparing Students’ Sustainable Skills via
STEM Education
Ling Siew Eng, Azlina Bujang, Mohd Syahiran Abdul Latif
Universiti Teknologi MARA Sarawak Branch, Malaysia

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SESSION 6: OPERATIONS AND PERFORMANCE
Impact of Embedded-TCP Key Exchange for Fragmented Segments
Suherman, Fahmi, Ali H. Rambe, Emerson P. Sinulingga, Marwan Al-Akaidi
Universitas Sumatera Utara, Medan, Indonesia & Al-Farahidi University, Baghdad,
Iraq

84

Universal Low Power Wireless Sensor Node with Energy Harvesting
Emerson P. Sinulingga, Suherman, Dani Gunawan, Tigor H. Nasution, Dwi Budi
Prasetyo, Abdul Risyal Nasaution
Universitas Sumatera Utara, Medan, Indonesia & Mandike Instrument, Medan,
Indonesia

88

Rural Internet Access Distribution Using Regression Model
Suherman, Ali H. Rambe, Tigor H. Nasution, Alvin C. Pardede, Marwan Al- Akaidi
Universitas Sumatera Utara, Medan, Indonesia & Al-Farahidi University, Baghdad,
Iraq

93

Keynote Presentation
Empowering Educators: An MSc Curriculum for Industry 4.0
Vassiliadis Bill, Kameas Achilles, Panagiotakopoulos Theodor, and Stefani
Antonia
Hellenic Open University, DAISSy Group, School of Science and Technology, Greece

100

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Introductory Speech

INTRODUCTORY SPEECH
by
Associate Professor Dr Margaret Chan Kit Yok
Project Coordinator of ERASMUS+ InD4.0 on Capacity Building in Higher Education
Universiti Teknologi MARA, Sarawak Branch, Malaysia

As we are coming to the conﬂuence of ICCMEH 2023 and ICoBTA 2023, one is coming
to a close, and another is about to begin, it is indeed diﬃcult to address this forum.
The easy way out is to express my gratitude to all of you for being here to make bothconferences happen.
It has been an arduous journey since we came together in 2019 as partners of theConsortiums of the ERASMUS+ Key Action 2: Capacity Building in Higher Education
project on A new Master Course in Applied Computational Fluid Dynamics (APPLY) and
Master Degree in Industry 4.0 (InD4.0) co-funded by the ERASMUS Programme of theEuropean Union. Time ﬂies so fast, and we are almost at the end of our projects.
Between the time we started and today, I have met great friends. I hope Kuching has
met your expectations better than your imagination that we have impressed on you
when we were at your countries. But this is not the forum to go down the memorylane. Both conferences are the final work task that we are meeting face-to-facethroughout the 4 years project time frame – sharing best practices that we havedeveloped not just among our member institutions but also participants from otherinstitutions so that they can be brought into the mainstream of the output of the
projects in one way or another.
Allow me a couple of minutes to express my sincere gratitude to
The Right Honourable
Datuk Patinggi Dr Haji Abang Abdul Rahman Zohari bin Tun Datuk Abang Haji Openg
Premier of Sarawak
for accepting our invitation to grace the occasion. As with any organising chair, it
brings great significance as it is the show of his support of our work andacknowledgement of the contributions towards the aspiration of the Sarawak
Government towards a digital and green economy by 2030.
Thus, a big thank you to
The Honourable Dato Sri Professor Dr. Sim Kui Hian,
Deputy Premier Minister and Minister for Public Health, Housing and Local
Governmentviii

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Introductory Speech

for agreeing to be here with us. As UiTM's current ERASMUS+ project on the ASEAN
Smart Cities Network, which started this 1 July 2023, we can connect with you.
I would also take this opportune time to express my profound thanks to Prof Dato Dr
Haji Jamil, my advisor, my contemporary and my rock. His trust in me has enabled me
to travel worldwide to bring UiTM globally, networking and building with academic
peers. This is one of the evidence today with the presence of the internationaldelegates.
There are many people from the international, national and local committees whom I
owe tremendously to being given the honour to be on this podium and whom I cannot
mention individually. You have helped put things together relentlessly andcontributed towards the conferences. My apologies to all the participants for any
shortcomings. It is not because of them, but it is due to my overlooking of tasks thatneed to be paid attention to.
To the distinguished keynote speakers, paper presenters, and panellists in the roundtable discussion and panel discussions, I would like to express my gratitude for all
your contributions to these events to make both a success. This would not have beenpossible without the support of every one of you. Please rest assured that if you arehere and have been chosen for the event or are volunteering to be here, there is areason, i.e., due to your mutual passion. Your passion for a common goal helps us
bring you all together and unite the energy in realising these goals – whether
individually or in a group. We all need each other to fulfil our common goals, theimperatives of bridging divides, fostering collaboration and ensuring that education
remains relevant, accessible, and forward-looking, and that is why it makes our
resolution even stronger. These conferences should be a platform to continue tomake that progress and network. We all need to keep listening and learning and,
importantly, take action to meet education's changing needs and challenges in thecapacity of the next generations.
To the media, I thank you for being here today because you play a very important rolein the dissemination to the public of what we are doing, an obligation of theconsortiums towards the ERASMUS+ Programme. To the hotel management, Thankyou for providing us with a wonderful atmosphere.
To end it all, I sincerely appreciate your attention today.

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Welcome Address

WELCOME ADDRESS
byProfessor Dato Dr Hj Jamil Hj Hamali
Rector, Universiti Teknologi Mara Sarawak, Malaysia
It is my great pleasure, with immersed pride and honour, to welcome you to this uniqueamalgamation of two conferences: the International Conference on Computational Methods
in Engineering Health Sciences (ICCMEH) 2023 and the inaugural International Conferenceof Business and Technological Advances in Industry Revolution 4.0 (ICoBTA-IR4.0) 2023 inKuching with Universiti Teknologi MARA as the host.
I wish to extend our sincere gratitude to
The Right Honourable
Datuk Patinggi Dr Haji Abang Abdul Rahman Zohari bin Tun Datuk Abang Haji
Openg
Premier of Sarawak
for honouring us with his acceptance to officiate on these two occasions, the closing of
ICCMEH 2023 and the opening of the ICobTA 2023. The right honourable premier is a man ofvision, seizing new opportunities in every downturn caused by the COVID-19 pandemic. The
formulation and implementation of the Sarawak Post-COVID-19 Development Strategy 2030
under his leadership are to ensure Sarawak emerging as a stronger region and embarking ona full-fledged transformation capitalising on the global megatrends. The Sarawak
Government envisions that by 2030, Sarawak will be a thriving society driven by data andinnovation, enjoying economic prosperity, social inclusivity, and a sustainable environment.
The focus is on developing six economic sectors as drivers of growth for Sarawak, namely
manufacturing, agriculture, tourism, forestry, mining, and social services, through a digitaland green economy with renewable energy development. His initiative in transition towards
the hydrogen economy has compelled the hydrogen modules and research programmes tobe introduced at universities in Sarawak to equip future engineers to specialise in thisindustry.
With a very heavy schedule, he has deputised.
The Honourable Dato Sri Professor Dr. Sim Kui Hian,
Deputy Premier Minister and Minister for Public Health, Housing and Local
Government.
I honour your presence despite your heavy schedule. We cannot thank you enough for gracing
the events. Your support and engagement add immerse value and will make our forum a
resounding success.

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Welcome Address

As mentioned earlier by Prof. Ir. Dr. Hj. Kamarul Arifin, ICCMEH is a collaborative effort of the
3 higher institutions from India, Japan and Malaysia and 2 partner institutions from
ERASMUS+ project APPLY in Malaysia. While we heard from Associate Prof Dr Margaret,
ICobTA 2023 is an inaugural conference related to ERASMUS+ Project Industrial 4.0 in which
UiTM is the project coordinator. I am proud to share with you all that UiTM Sarawak is apartner of 7 consortiums with ERASMUS+ Key Action 2 Capacity Building in the Field ofHigher Education with various projects related to the digital economy since 2015. The
transnational proposals were carefully made in line with the needs of all the Asian Partners’
countries from the public authorities and industry perspective. The educational challenges
lie in what makes them so interested in finding employees with the potential to manage broad
solutions and complex ecosystems in the pursuit of the digital and green economy. UiTM
Sarawak has completed two projects in Blended Learning led by Aix Marseille University,
France and Food Processing and Innovation led by Universiti Teknologi Malaysia. Today, weare coming to the end of another two projects, APPLY, led by Chiang Mai University andIndustrial Revolution 4.0, led by UiTM. Three more are still ongoing: Family Business
Management (FAB) led by the National University of Management, Cambodia, Portable Video
Conferencing Toolkits led by Danmarks Tekniseke Universitet, Denmarks and this year, weare starting with Smart City ASEAN Learning Network led by Universiti Malaya. Indeed, we
have benefited tremendously in offering new Master Programmes and Professional Courses
developed with the transnational effort with continuous collaboration in students andAcademic staff mobility programmes and research activities.
I have spoken a lot, but before I leave this podium, I must acknowledge the support of theSarawak Government and the ERASMUS+ Programme of the European Union, which is cofunding the projects and conferences. In addition, I would like to extend my deepest gratitude
to the entire organising committee working tirelessly behind the scenes. To all of theparticipants, thank you for being here today. I am looking forward to the keynote address of
the RIGHT Honourable Datuk Patinggi Dr Haji Abang Abdul Rahman Zohari Bin Tun Datuk
Abang Haji Openg, Premier of Sarawak and also The Honourable Dato Sri
Professor Dr. Sim Kui Hian, Deputy Premier Minister.

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Keynote Speech

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KEYNOTE SPEECHbyThe Right Honourable
Datuk Patinggi Dr Haji Abang Abdul Rahman Zohari bin Tun Datuk Abang Haji Openg
Premier of Sarawak
delivered by
The Honourable Dato Sri Professor Dr. Sim Kui Hian
Deputy Premier Minister
Minister for Public Health, Housing and Local Government
SarawakAs the Premier of Sarawak, it is both an honour and a privilege to address such a
distinguished gathering of academia, researchers, industry players, policymakers andstudents at the confluence of two conferences co-funded by the ERASMUS+ Programme of
the European Union: the official closing event of the International Conference on
Computational Methods in Engineering Health Sciences 2023 and the official opening of 1stInternational Conference of Business and Technological Advances in Industry Revolution 4.0.
The unique amalgamation of these two events, as informed by the organisers, is because
UiTM is a partner of the 2 consortiums of the ERASMUS+ programme for the projects: Applied
Computational Fluid Dynamics (APPLY) and Industrial Revolution 4.0 (InD4.0). I, therefore,
extend a heartfelt welcome to all participants from across the globe.
With the advent of the Internet of Things (IoT), greater automation and artificial intelligence,
connectivity between machines is the focus of Industry 4.0. Incorporating Computational
Fluid Dynamics (CFD), an integral component of Industry 4.0 technologies, plays a pivotal rolein the digital economy, significantly bolstering engineers' capacity through simulation toattain precise results in a wide array of engineering studies and design optimisations. Theyempower engineers to fine-tune resource allocation, minimise waste, and foster
sustainability. This technology facilitates the optimisation of designs to enhance product orsystem performance and aids in identifying areas necessitating adjustments prior tooperation and manufacturing. By synergising CFD and Industry 4.0 technologies, the industry
can craft innovative solutions that set them apart, enabling them to differentiate themselvesfrom competitors by elevating their operational efficiency, flexibility, responsiveness, andcost-effectiveness. This resonates with the Sarawak government’s agenda to develop newtechnologies and solutions to transform into a digital and green economy for common
regional prosperity. I note that the Master of Science Programmes developed under the
ERASMUS+ programs concentrating on Industrial Revolution 4.0 and Master of Science on
Applied Computational Fluid Dynamics (CFD) are already being offered as pilots in the Asian
Higher Institution partners.
I also understand that you are here today with the two (2) final conferences as a wrap-up to
your projects to share the successful execution experiences and discuss issues and ideas
relating to the Fourth Industrial Revolution and its technologies synergising with CFD. I extend
my sincere gratitude for choosing Kuching as the conference’s destination. Kuching needs
no introduction for its Malaysian hospitality and the unique diversity of the culture of theSarawak people with the pristine flora and fauna.

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Keynote Speech

I want to highlight where you are in the technological term. Kuching is the capital city ofSarawak, a region within Malaysia celebrating its 60th Independence with significant
milestones towards achieving a sustainable digital and green economy in 2030. Embarking
on the exciting journey of growth and transformation infused with a dynamic growth mindset
embracing learning and evolution, this is the forum that can bring inspiration in seeking green
solutions with a responsive education system focused on the preparation of more productive,
effective, and highly skilled human resource that contribute to the productivity anddevelopment of the economy in a sustainable way.
Sarawak’s big leap into the digital and green economy with the Post Covid-19 Development
Strategy 2030 heralding for Kuching with Smart City Status by 2025 will benefit from the two
ERASMUS projects as the objectives had been formulated to provide practical solutions to
help close the gap between industry needs/ expectations and educational system outputs.
The Mid Term Review of the 12th Malaysia Plan released by the Ministry of Economy on 11
September 2023 calls for the establishment of a new science technology, engineering, andmathematics (STEM) curriculum to be introduced to students to embrace the Fourth
Industrial Revolution (IR4.0) aligning with the national aspiration and niche areas, IR5.0 isalready advancing. That is a big education challenge.
The Sarawak government, too, aspires to seek the transformation of education to ensure afuture-ready generation. Leveraging technology, we welcome pioneering initiatives todemocratise education, ensuring accessibility and quality. Indeed, the overarching themes of
ICCMEH and ICoBTA-IR4.0 resonate with Sarawak's endeavours, emphasising the symbiotic
relationship between education and technology. We envisage a state where the confluenceof education and technology acts as a catalyst for socio-economic transformation, ensuring
a brighter future for our upcoming generations. Sarawak Government's commitment to theseobjectives is unwavering, and your conferences will serve as a legacy of our collective
resolve.
I applaud UiTM Sarawak for accepting to be the host. I wish to convey my deepest gratitude
to all contributors, organisers, and participants. Your dedication and passion are the lifebloodof such events, ensuring their success and impact. The Sarawak Government stands withyou, not just as a supporter but as a collaborator, in our shared journey towards educational
excellence. Together, we envision a brighter future and actively sculpt it.
I congratulate the successful inauguration of the International Conference on Computational
Methods in Engineering Health Sciences (ICCMEH) 2023 and, therefore, officially close theconference. I am also honoured to officially open the 1st International Conference ofBusiness and Technological Advances in Industry Revolution 4.0 (ICoBTA-IR4.0) 2023. Here's
to a successful, enlightening, and productive ICoBTA-IR4.0 2023!
To the international delegates, visit Beautiful Sarawak after your hard work. Firm a lasting
friendship with any local delegates to enjoy our Sarawak’s hospitality.
Thank you.

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Development of Islamic Finance in Industry 4.0 Era

Development of Islamic Finance in Industry 4.0 Era: Challenges,
Criticism and Opportunities

Maizaitulaidawati Md Husin 1, Nor Aiza Mohd Zamil 2 and Haliyana Khalid 3
1,2,3

Azman Hashim International Business School, University Teknologi Malaysia, Kuala Lumpur,
Malaysia
1

maizaitulaidawati@gmail.com; 2noraizamz@utm.my; 3haliyana@utm.my

EXTENDED ABSTRACT
Industry 4.0 represents a transformative paradigm shift in the world of manufacturing andproduction. Coined as the Fourth Industrial Revolution, Industry 4.0 integrates cutting-edge
technologies to create a connected, intelligent, and highly automated industrial ecosystem. Thisrevolution is marked by the fusion of digital technologies, data analytics, the Internet of Things(IoT), artificial intelligence (AI), and advanced robotics to optimise processes, enhance
productivity, and redefine how products are designed, manufactured, and delivered. At its core,
Industry 4.0 seeks to establish a seamless interplay between physical systems and digitaltechnologies, enabling a level of automation and intelligence that was once deemed futuristic. The
concept represents a departure from traditional manufacturing approaches, emphasising theimportance of interconnected systems, real-time data exchange, and decentralised decision-making.
By integrating smart sensors and devices, machinery can self-monitor, self-optimise, andcommunicate with other components within the production chain. Key elements of Industry 4.0
include using big data analytics to derive actionable insights, implementing cyber-physical systems
that bridge the gap between the physical and digital realms, and deploying advanced technologies
like machine learning and augmented reality to enhance operational efficiency. This transformation
impacts manufacturing processes and extends its influence across the entire value chain, fromsupply chain management and logistics to customer engagement and product lifecycle management.
The convergence of Industry 4.0 and Islamic finance marks a transformative intersection
where advanced technologies harmonise with Islamic economics and finance principles.
Simultaneously, Islamic finance adheres to principles rooted in ethical and Sharia-compliant
practices, emphasising risk-sharing, social justice, and economic well-being. In the context ofIndustry 4.0, the principles of Islamic finance provide a unique lens through which to assess the
ethical implications of advanced technologies. The interconnectedness of cyber-physical systems
and the intelligent automation characteristic of Industry 4.0 align with the Islamic principles of
efficiency, transparency, and equitable wealth distribution. As manufacturing processes become
increasingly digitised and automated, the synergy between technological advancements and Islamic
finance principles offers a pathway for sustainable and socially responsible economic development.
Islamic finance, with its prohibiting usury (riba) and emphasis on fair and ethicaltransactions, encourages a financial landscape prioritising social welfare and economic justice.
This alignment with ethical considerations is particularly relevant in the context of Industry 4.0,
where the potential for disruptive technologies raises questions about their impact on employment,social equity, and the ethical use of data. Moreover, Industry 4.0 technologies can be harnessed toenhance the efficiency and transparency of Islamic finance operations. Blockchain, for example, canbe employed to ensure the integrity of financial transactions. At the same time, AI and data analytics
can streamline risk management processes and decision-making by Sharia principles. Theintersection of Industry 4.0 and Islamic finance holds promise for fostering sustainable andinclusive economic growth. By leveraging the ethical foundations of Islamic finance, Industry 4.0

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can be steered towards addressing societal challenges, reducing economic disparities, andpromoting responsible innovation. This synthesis offers a unique opportunity for industries toembrace technological advancements while upholding principles that prioritise ethical, sustainable,
and socially responsible business practices.
As Industry 4.0 gains momentum globally, industries are experiencing a paradigm shift in theirapproach to production, resource allocation, and business models. The potential benefits are vast,
promising increased flexibility, customization, and responsiveness to market demands. However,with this revolution comes the need to carefully consider cybersecurity, data privacy, andworkforce readiness to navigate the challenges posed by this unprecedented convergence oftechnology and industry. In essence, Industry 4.0 marks a pivotal moment in the evolution of
manufacturing, propelling businesses into a new era of innovation, efficiency, andcompetitiveness. The objective of this paper is twofold. First, this paper examines the developmentof Islamic finance within the context of the Industry 4.0 era. Second, the challenges, criticism andopportunities of applying Industry 4.0 in Islamic finance are presented. This paper examined thechallenges, criticisms and opportunities towards the Islamic finance industry through interviews
with a sample of nine (9) chief technology/information officers of Islamic banks in Malaysia. The
sample was drawn from the number of registered Islamic banks listed by the Bank Negara
Malaysia. According to the Bank Negara Malaysia (2023), 17 registered Islamic banks wereoperating in Malaysia. Some banks were randomly contacted through email to secure permission
to conduct interviews. Only 9 Islamic banks agreed to participate in the interview. Prior to theinterview session, respondents were informed that their participation was anonymous andvoluntary. Further, they were informed that their responses would only be used for this research.
Once the respondents gave their consent, a series of questions were asked. A minimum of 55
minutes and 90 minutes were spent to complete the interview sessions. Interviews were conductedfrom January until July 2023.
This paper found that utilising advanced technologies has opened up new opportunities for thegrowth and expansion of Islamic finance. Digitisation and automation have enhanced theefficiency, transparency, and accessibility of Islamic financial services, facilitating smoothertransactions and reducing operational costs. Smart contracts and blockchain technology haveprovided a secure and immutable platform for Islamic finance transactions, addressing trust andverification issues. Furthermore, Industry 4.0 technologies have enabled the development ofinnovative Islamic financial products and services. Fintech startups and established institutions
leverage data analytics and artificial intelligence algorithms to create customised solutions for
Islamic finance customers' unique needs. Robo-advisory platforms have emerged, offering
automated, Shariah-compliant investment advice based on individual risk profiles.
This paper also found that criticism of Industry 4.0 in the Islamic finance industry primarily
revolves around concerns regarding the compatibility of advanced technologies and the principlesof Shariah law. Further, challenges remain in harmonising the principles of Islamic finance withthe rapidly evolving technological landscape. Ensuring compliance with Shariah law, particularly
in developing complex financial instruments, requires ongoing collaboration between Islamicscholars and technologists. Cybersecurity and data privacy concerns must be addressed to build
trust in digital Islamic finance.
Overall, the development of Islamic finance in the Industry 4.0 era presents both opportunities andchallenges. Despite the criticism, as technology continues to evolve, the industry must adapt andinnovate to leverage the full potential of Industry 4.0 while upholding the core principles ofIslamic finance. The successful integration of technology and Islamic finance has the potential totransform the industry, expand its reach, and create inclusive financial systems aligned with
Islamic values.

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Enhancing Employee Experience in Expenditure

Enhancing Employee Experience in Expenditure Enquiry
Management of Global Service Centre Through Chatbot
Implementation in Multinational Company
Maria Ahmad1 and Fauziah Sh. Ahmad2
1 ,2

Azman Hashim International Business School, Universiti Teknologi Malaysia, Malaysia
1mariaameina@graduate.utm.my; 2fsa@utm.my*Corresponding Author

ABSTRACT
Enquiry management is one of the major processes in global service centre management. It
addresses global employees’ questions and provides support to remove obstacles in performing
their day-to-day jobs and operations. The enquiry management process and tools have evolved
from a manual process to a digitised process using supporting tools. It is also has evolved fromguided service to self-service enquiry channels. Today, employees adopted to search for theanswers themselves with tools such as knowledge-based enquiry (Frequently Asked Questions,
How-to Questions, etc) and chatbot enquiry. A chatbot is a software application that aims to mimic
human conversation through text or voice interactions, typically placed on an online platform. In
Company X, the Travel and Expenses (T&E) department has adopted knowledge-based enquiry
management as a self-service enquiry channel to resolve general & simple enquiries, with a service
desk support system as a guided-service enquiry channel for complex enquiries. There are
identified gaps with the current setup, as end users still go for guided-service enquiry channels
when they have simple enquiries that could have been resolved self-service. Corporate faces issues
transforming the enquiry management from guided service to self-service resolution. When
adopting the self-service resolution, it is pertinent to ensure process efficiency, including headcountreduction, improved enquiry resolution time, and better employee satisfaction in resolving theirenquiries. Implementing a chatbot could help to improve the adoption of self-service resolution
further. However, corporations must execute properly to ensure successful chatbot implementationto achieve the objectives. This action research focuses on how chatbot implementation can improvethe enquiry management process efficiency, which is expected to reduce the percentage of querytickets raised by end users through the guided-service support channels that self-service supportchannels could have addressed. The study uses mixed-method research methodology (qualitativeand quantitative), collecting data in terms of the percentage of query tickets raised by end users
through the guided-service support channels that could have been addressed by self-service supportchannels before and after the chatbot implementation, chatbot usage data after the chatbot
implementation, and assess the user experience improvement in interviews.

Keywords: Chatbot; enquiry management; process improvement; self-service resolution adoption
INTRODUCTION
Research Introduction
Travel and expenses (T&E) are an integral aspect of corporate work within any organisation.
However, managing T&E can often be complex and time-consuming for employees, requiring
meticulous scrutiny of expenses, comprehensive documentation, and adherence to the company's
expenditure policies. Employees need to clearly understand which expenditures are reimbursable, as
these details are crucial components of the company's quarterly and annual financial reports. The

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process of filing expense claims has evolved significantly, transitioning from manual submissions
to nearly automated procedures, wherein employees are now required to provide supporting
evidence, such as photographs or scanned documents when submitting their expense reports.
Regardless of the specific system implemented within a corporation to oversee T&E,
employees must have easy access to information on how to submit claims and what expenses are
eligible for reimbursement. Navigating through extensive lists of knowledge articles related to T&E
can be a source of frustration for some employees, particularly if they are unsure about therelevance of the articles or if they cannot locate answers to their T&E-related inquiries. In suchcases, employees typically turn to the T&E team for assistance through available communication
channels, seeking further guidance and clarification.
Introducing a conversational chatbot represents a significant advancement in addressing thesechallenges. This chatbot is designed to provide end users instant access to relevant articles tailoredto their queries, delivering much faster responses than traditional human support through service
tickets. If the top three suggestions provided by the chatbot do not align with the employee's
question, the bot will prompt the employee to rephrase their inquiry, ensuring more accurate results.
The chatbot aims to replicate human interaction, enabling employees to obtain support for their
T&E-related questions as if they were conversing with the T&E department.
Case Company & Problem Diagnosis
The case company, known as "Company X" to maintain anonymity, is a global multinational
enterprise with a substantial workforce. Company X operates on a model built around businessservice centres, encompassing various departments providing essential services to otherorganisational divisions. Within this structure, the Travel and Expenses (T&E) department serves apivotal role. Company X has already integrated service desk support and ticketing tools as theprimary means for end users to raise inquiries and seek assistance. Furthermore, it has implemented
a Knowledge Management (KM) system and Knowledge Articles (KA), offering a knowledgebased search portal accessible to all employees across the company. These resources have been
instrumental in streamlining information retrieval and problem-solving.
In the current process, end users initiate their information search by accessing the central
repository within the company's knowledge search portal. They input their T&E-related keywords
or questions into the search bar, which triggers the company's knowledge search system to provide
a list of the top 30 search results from the repository. These results include Knowledge Article (KA)and catalogue item titles and descriptions. Users can navigate additional pages to view the next top
30 results. By default, all results are displayed without any filters applied. To enhance the user
experience, the search results can be filtered based on three categories: displaying all results,
showing only knowledge articles, or showcasing only service request catalogue items. The searchengine utilises the user's input keywords and location as inputs, processing this information to
identify articles that match the title, description, body, and metadata established during KA
creation. The search engine employs semantic search techniques powered by natural language
processing to identify matching keywords and arrange the search results in order of relevance. The
accuracy of these results hinges on both the technical configuration of the search engine, whichmust interpret the user's search intent and the quality of the content within the KAs provided to thesearch engine for processing.
However, the limitation of the current system is that it only accepts input from the search bar
and processes keywords solely from this employee input, yielding the top 30 search results. The
relevance of these results can vary significantly based on the keywords entered. If the search resultsalign with the user's request, they proceed to the respective KA to address their query. If not, users
may either rephrase their search keywords, continue scrolling through results until they locate theappropriate articles, or directly contact the support team through catalogue requests. This processposes certain challenges. It inundates end users with a potentially overwhelming amount of4

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information, risking information overload. It also relies on users' natural responses and reaction
times to search results, which can vary significantly among users with different demographics andbehaviours. In an era where rapid and precise information retrieval is critical to a positive user
experience, the current process falls short, potentially discouraging users from conducting factfinding missions and resorting to support ticket submissions instead. This shift away from selfservice can have negative implications, as it may result from tools perceived as overly complex orunhelpful. Ultimately, it could lead users to abandon self-service channels altogether, hindering the
adoption of these channels within the organisation.
Research Questions and Objectives
This research is structured to study on the below research questions (RQ):- RQ1: What is the current adoption rate of end users in the knowledge search portal versus
the T&E service desk in Company X?
- RQ2: What can chatbot improve in end-user experience?
- RQ3: Can knowledge-based chatbot tools improve the adaptation of self-service experiencein managing T&E queries?
- RQ4: What impact does chatbot have on improving end-user experience in T&E queries?
This research is conducted to achieve below research objectives (RO):- RO1: To assess the current adoption rate of end users in the self-service and guided service
channels.
- RO2: To assess the opportunity to improve end-user experience through the chatbot.
- RO3: To assess the new adoption rate of end users in the self-service channel vs the guided
service channel after chatbot implementation.
- RO4: To assess the impact of chatbots in improving end-user experience in self-service
channels.
Table 1
Summary of Research Questions, Research Instruments and Source of Data
RQ

Research Method

Research Instrument

Source of Data

RQ1

Quantitative•

Secondary Data Analysis

RQ2

Qualitative•

Focus Group Discussion

RQ3

Quantitative•

Secondary Data Analysis

Primary Data•
Focus Group Discussion: T&E Enquiry
Management Challenges & How Chatbot
can Improve End User Experience inEnquiry Management•
Interview Session: Chatbot experience,usability & continuous improvement

RQ4

Qualitative•

Interview

Secondary Data•
Percentage of queries ticket raised by
end users through the guided-service
support channels that could have beenaddressed by self-service supportchannels.

Literature Review
Chatbots have garnered significant attention as a potential game-changer in how people interact withdata and online services (Brandtzaeg & Folstad, 2017). These AI-driven entities act as digital agents,
serving as intuitive interfaces between users and data/service providers (Dale, 2016). Previous
studies have indicated that end users highly value the swift responses offered by chatbots,
particularly when compared to waiting for a human service desk response, especially during offhours (Folstad & Skjuve, 2019). The primary motivation for users to engage with chatbots forcustomer service lies in their ability to effectively address simple queries, contributing to an overall
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positive user experience. Even occasional inaccuracies or inadequate responses from chatbots may
not be detrimental to the user experience, provided an exit path to connect with humanrepresentatives is available.
Users are primarily attracted to chatbots for their ability to provide easily understandable
answers and assistance in locating online self-service resources. This aligns with other studies on
chatbot user experience (Telner, 2021), emphasising the importance of factors such as response
time, ease of navigation, visual appeal, vocabulary, guided conversation, content quality,
knowledge base, and error handling in determining overall satisfaction. To optimise user
experience, it's essential to ensure that chatbot responses are specific, relevant, accurate, wellstructured, concise, and employ understandable language and terminology. In a systematic review
of service chatbots (Mohamad Suhaili et al., 2021), two critical components emerge as vital for
chatbot success: Natural Language Processing (NLP) and Natural Language Generation (NLG).
NLP facilitates the comprehension of end-user feedback, while NLG is responsible for generating
appropriate responses in human-understandable language. Additionally, research on designing highquality chatbots (Johari & Nohuddin, 2021) highlights six pre-development features, including
functionality, efficiency, humanity, effectiveness, technical satisfaction, and ethics, as essentialconsiderations.
Moreover, a study on productive chatbot design (Jassen et al., 2022) outlines a process
covering preliminary considerations, use case determination, defining chatbot characteristics,
constructing dialogue trees, content development and training, prototype development, acceptancetesting, measuring added value, and post-implementation. These guiding principles may not apply
universally to every chatbot project, but they offer valuable insights for ensuring successful
implementation that caters to end users. Furthermore, understanding end-user attitudes toward
chatbots is crucial. Research on chatbot taxonomy (Gkinko & Elbanna, 2022) categorises users into
four types: Early quitters, Progressives, Pragmatics, and Persistents. These categories reflect
varying acceptance and engagement with chatbots, from outright rejection to active cooperation.
In a prior study (Brandtzaeg & Folstad, 2017), five distinct categories emerged for why
people engage with chatbots. The first category pertains to productivity, where chatbots are used forconvenience, speed, and assistance in accessing information. The second category centres onentertainment, where chatting with a chatbot is fun and engaging. The third category involves socialand relational purposes, with chatbots serving to alleviate loneliness or provide a sense of social
interaction. The fourth category relates to the novelty of chatbots, where users are curious to
explore their capabilities. Finally, the fifth category encompasses other motivations, including using
chatbots to discuss sensitive topics, as they provide automatic responses when human help isunavailable or as a default method for customer support.
Engaging with a chatbot often feels like interacting with a virtual agent that mimics human
responses. A study by Ruane et al. (2021) explored the ideal balance of personality traits to be
injected into chatbots for user acceptance. The study applied the Five Factor Model (FFM), which
includes openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism.
The research delved into extraversion vs. introversion and agreeableness vs. disagreeablenesslanguage cues. Findings revealed that users perceived different personalities in different chatbots.
Moreover, the perceived personality of a text-based chatbot significantly influenced user
experience, with most users favouring a formal and professional tone for its directness and userfriendliness.
End-user interactions with chatbots can also impact their overall experience. Nguyen et al.
(2022) investigated how different chatbot interfaces (e.g., menu-based vs. text-based) affectautonomy, cognitive effort, and user satisfaction during task completion. Results supported previous
findings regarding the impact of interface types on perceived autonomy, cognitive effort, perceived
competence, satisfaction in performance, and satisfaction in the system. Incorporating AI-based6e-proceedings book ICoBTA-IR4.0

Enhancing Employee Experience in Expenditurechatbot capabilities, such as Natural Language Processing (NLP), enhanced the user experience by
providing autonomy and efficient task completion.
Another study by Haugeland et al. (2022) explored how conversation types and interaction
mechanisms in chatbots for customer service affect human likeness and user experience. Fourchatbot types were examined: task-led with button interaction, task-led with free-text interaction,
topic-led with button interaction, and topic-led with free-text interaction. The study found that
topic-led conversations strengthened the perception of anthropomorphism and hedonic quality inchatbots. Users also preferred human-like conversations for exploring specific topics, and clever useof button interactions improved the overall chatbot experience.
Several chatbot implementations have been analysed in previous studies to assess theirimpact on end-user experience. Kushwaha et al. (2021) investigated AI-driven chatbots in B2B
enterprises and their impact on customer experience (CX). Their findings highlighted various
factors influencing CX, including perceived risk, brand trustworthiness, sensory appeal, flow
factors, touchpoint-specific factors, predictability, innovation, privacy, safety, transparency, andtrust (PSTT). Another study by Ngai et al. (2021) focused on knowledge-based chatbots forcustomer service, emphasising the importance of system design and components in providing
proactive and continuous system improvement. Challenges in technological and management
aspects were also identified, emphasising the need for proper planning and support.
Nguyen (2019) explored knowledge-based chatbots for customer support, highlighting theclose relationship between chatbots and knowledge bases. The study showed that unresolved
queries could be rerouted to human agents without negatively affecting the end-user experience.
Additionally, Kvale et al. (2021) conducted a customer satisfaction survey to assess user
experiences with chatbots. Their research demonstrated the significant impact of problem resolution
on user satisfaction, as well as the influence of the type of problem users sought help with on
satisfaction levels. Zhang et al. (2021) emphasised the importance of organisational factors in the
successful technology implementation of chatbots. They identified five critical factors: work andteam organisation, change management, competencies and competency acquisition, organisational
resources, and performance measurement. Proper team composition, effective change management,competency in AI trainers, resource availability, and performance measurement were essential for
successful chatbot implementation.
Research findings have revealed various factors contributing to successful chatbot
implementation and user experience. These factors include user motivations, chatbot personality
traits, user interfaces, functionality, knowledge bases, problem resolution, and organisational
considerations. Understanding these factors is essential for organisations that leverage chatbots
effectively and enhance user satisfaction. The factors of speed, convenience, and user-friendly
features are important in shaping end-user satisfaction with chatbots. Effective chatbot
functionality, including response mechanisms and human-like interactions, plays a pivotal role forend users to start using and continue using a chatbot. Additionally, understanding user attitudes
helps predict how different individuals will engage with chatbots as a self-service channel.

METHODOLOGY
Research Philosophy and Design
This action research adopts a pragmatic philosophical approach, which is rooted inpracticality and seeks to determine the value, truth, and meaning in real-life situations (Legg et al.,2021). Pragmatism stands in contrast to idealism, where idealism is centred around a set of abstract
ideas or high principles (Guyer et al., 2022). Pragmatism, in this context, is chosen because it aligns
with the examination of real-world situations, values, truths, and meanings, which are central to this7

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research, rather than following an idealistic theoretical path. For this study, the researcher will
employ a cross-sectional case study design incorporating a mixed-methods data-gathering
technique. This approach combines both primary and secondary data, as well as quantitative andqualitative data, to gather a comprehensive dataset that can validate the problem statement andassess the effectiveness of the proposed intervention strategy in addressing the identified problem.
By adopting this pragmatic approach and utilising mixed-methods data collection techniques, thisresearch aims to provide practical insights and solutions to enhance the T&E inquiry management
process and improve end-user experience within the company.
Research Instruments and Measures
The research uses secondary as quantitative data collection. The objective is to identify
quantitative data concerning the current percentage of queries raised by end users through guidedservice support channels that could have been effectively addressed through self-service supportchannels. This investigation will shed light on user behaviour trends, particularly in cases where
support tickets have been raised despite the availability of knowledge articles (KAs) that end users
could utilise for self-service. By analysing this quantitative data, the researcher aims to identify the
impact of a knowledge-based chatbot on the new adoption rate of end users in self-service channels
vs guided service channels before and after chatbot implementation.
The research uses focus group discussions (FGDs) as a vital component of qualitative datacollection. The FGDs serve the purpose of understanding the challenges related to T&E inquiry
management within the company and how this study can address these challenges by enhancing
end-user experience and usability through a knowledge-based chatbot. The FGDs will be structured
into three sections: Section A covers the background of FGD participants, Section B delves into
T&E Enquiry Management Challenges, and Section C focuses on Knowledge-Based Chatbot
Implementation Strategies. The following topics will guide the discussions during the FGD
sessions:
Table 2Focus Group Discussion Input in Formulating of Chatbot Functionality
Focus Group Topics

Baseline of Discussion

Functionality
Interpret command accurately.
Flexible in interpreting knowledge.
Number of service available in the chatbot.
Sustainability.
Accuracy.
Interoperability.
Compliance.
Security.

To ensure our chatbot could achieve most favouredfunctionality to gain end user satisfaction by
integrating with the existing backend setup. This is tomake sure our current business and IT support
structure to maintain the sustainability of chatbot interms of maintainability and continuous improvement.
This is also to ensure this study follow the security
and compliance setup in Company X.

Efficiency
Ease of use.
Quick replies to vs free text.
Available always.
Accessibility.
Time behaviour.
Resource.
Utilisation.
Strong to manipulate data input by users.

To ensure our chatbot can perform much morecompared to existing channels. As Company X
operates as business service centres with cross timezone support, this will further support the model tohave 24/7 accessibility and support.

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Humanity
Realness of robot.
Create an enjoyable interaction.
Convey personalities.
Able to maintain the theme of the discussion.
Able to respond to specific inquiries.
Able to mimic human personality.
Able to provide correct response to user’s request.

To ensure our chatbot can make it feel seamless tothe end user in getting support, as in talking to almost
real humans with natural language processing, justlike how humans will process in giving feedback to
end user questions.

EffectivenessConstrue statements and commands correctly.
Linguistic precision.
Accurate task execution.
Consist of vast area of knowledge.
Ability to solve simple task request.

To ensure our chatbot is smart enough to cater end
user queries, from simple to complex combinations ofqueries. With global support, to also be mindful on
how to manage end user language choices in asking
questions, as well as searching results in thelanguage spoken by end users.

Technical satisfaction
Ability to express feeling.
Provide entertainment and enjoyment to user.
Conveying emotional information through tones andexpression.

To ensure our chatbot is engaging in providing
response, able to understand end user situations andgive appropriate feedback to manage end user
emotion, just like how service desk humans could be
empathetic in responding to questions.

EthicsUser’s privacy protection.
Consistency.
Training the bot with knowledge and cultural ethics.

To ensure our chatbot follows the ethical, risk andcompliance guidelines of the company, in what kindof information that could and could not be provided
depending on the level of sensitivity of theinformation.

Reference: Johari and Nohuddin (2021)

Additionally, interviews will be conducted as part of the qualitative data collection process.
The interviews aim to analyse the end-user experience using the chatbot implemented for T&E
inquiry management. The interview sessions will consist of Section A, which covers the
background of interviewee participants, and Section B, which delves into T&E End User
Experience in Using Chatbot. The following interview questions and objectives were formulated
from the literature review study:- To assess end-user usability of chatbot.
- To assess end-user information processing in getting answers to their queries.
- To assess the chatbot's capabilities in resolving end-user queries.
- To assess the chatbot interaction in resolving end-user queries.
- To assess the end user’s willingness to switch to the new channel.
- To assess the end user resistance to switching to the new channel compared to an existing
channel.
- To assess the end-user adaptability in switching to new technology.
- To assess the chatbot's end-to-end support in solving end-user queries.
- To assess the end user’s confidence in the new technology.
Proposed Intervention
With the analysis of the current setup, it becomes evident that end users are encountering
challenges in efficiently accessing the most pertinent knowledge articles for their common "HowTo" queries, especially those relevant to their specific work locations. The existing setup presents
the top 30 search results, inundating end users with excessive information. Consequently, some end
users resort to raising support tickets for "How-to" assistance even when the necessary knowledge
articles are available, resulting in unnecessary delays as they await support team responses, whereas
self-service could have resolved their issues.

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Furthermore, a portion of users has become habituated to guided-service support, preferring
to pose questions to a human agent rather than proactively searching for answers themselves.
Additionally, there's a lack of clarity among end users regarding the ticket-raising process and thecorrect procedures for soliciting further assistance. These challenges collectively underscore theneed for intervention. Implementing a knowledge-based chatbot is envisioned to significantly
enhance the end-user experience. It aims to streamline and simplify information presentation,effectively addressing end-user inquiries and bridging the identified gaps.
Action Research Intervention
Figure 1
Action Research Cycle on Enhancing Employee Experience in Expenditure Enquiry Management of Global
Service Centre Through Chatbot Implementation in Multinational Company

Figure 2
Action Research Cycle10e-proceedings book ICoBTA-IR4.0

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Input
Business Research
The initial phase of this action research involves problem identification, which is a criticalfoundation for the subsequent research journey. This process commences with an initial meeting withthe case company, where the research proposal is introduced and discussed. During this meeting, theprimary objective is to identify and define the specific problem or challenge the research aims toaddress within the organisational context. This phase sets the stage for the entire research endeavour
by pinpointing the areas where improvement and innovation are required.
Following the problem identification phase, a comprehensive literature review is conducted.
This step entails an exhaustive examination of existing theories and prior case studies related to theresearch topic. By delving into the academic and practical knowledge already available, the researchgains valuable insights into the broader context of the problem.
Once the literature review is complete, the research enters the information-gathering phase.
This stage involves engaging in discussions and seeking confirmation from various organisational
business stakeholders. These stakeholders are individuals from the T&E enquiry management teamand the company's chatbot implementation team who possess relevant insights and expertise. These
interactions aim to collaboratively refine the research activities, ensuring that they align with theidentified problem and are designed to yield actionable outcomes.
Transformation
Initiation: Data Collection and Analysis
Pre-implementation data collection's primary focus is on problem diagnosis and strategy
development. Firstly, the research performs analysis of secondary data. This is to comprehensively
understand the existing situation by delving into quantitative data. This involves the examination of
the current percentage of support queries raised by end users through guided-service channels, which
could have been readily addressed through self-service avenues. This analysis aids in deciphering
user behaviour patterns, particularly when they submit support tickets that already havecorresponding knowledge articles for self-service. The insights drawn will guide improvements in
information display within search results and the management of knowledge articles (KAs) toenhance their visibility as top search results.
Next, the research performs Focus Group Discussion (FGD) with stakeholders. This is togather primary qualitative data; the research engages in focused discussions with key stakeholders
involved in the implementation process. This includes team members responsible for executing theintervention. During these FGDs, the research aims to gain insights into the end-user journey,tracking their actions within the T&E service desk support and KA content management team. By
understanding the current workflow and identifying potential gaps, the participants brainstormed
ways in which chatbot implementation could address these challenges. Moreover, the participants
strive to uncover strategies for enhancing the end-user experience, as well as pre-emptively
addressing any anticipated challenges post-chatbot implementation. Participants in these FGDs arechosen via judgmental sampling based on their technical and business process backgrounds, ensuring
their insights are valuable for successful implementation.

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Table 3Focus Group Discussion Participants Profiling
No

Participant/DesignationRole in T&E Enquiry Management

1

Person A

Business process owner (BPO) in defining and overseeing T&E enquiry
management process & procedures globally in Company X. Participate inproject discussion planning & execution for T&E chatbot.

2

Person B

Subject Matter Expert (SME) in customer engagement for T&E, maintainthe knowledge library management for the company knowledge searchportal, collect end user feedback and improve enquiry management topics
globally in Company X. Participate in project discussion planning &execution for T&E chatbot.

Planning: Development PlanAt this stage, the researcher collaborates closely with the chatbot implementation team to
meticulously chart out the course of action for the development and deployment of the chatbot. This
phase is informed by the valuable data collected during the initiation stage, which serves as thefoundation for decision-making and strategy development. The team engages in in-depth discussions
to outline the precise business requirements for the chatbot's functionality. This entails thoroughly
examining what the chatbot needs to achieve, the problems it should address, and the specific tasks itshould perform to enhance the end-user experience in the T&E enquiry management process. These
discussions are critical in ensuring the chatbot aligns seamlessly with the organisation's and user's
needs. Simultaneously, the team delves into the intervention strategy and evaluates the feasibility of
proposed solutions. This involves a careful assessment of whether the identified strategy is not only
viable but also capable of effectively addressing the identified problem. Feasibility studies help inmaking informed decisions about the direction the project should take. Furthermore, meticulous
planning is carried out for the different stages of the project. Tasks are identified, prioritised, andscheduled in accordance with the required timeline. Resource allocation and management are also
key components of this planning phase. Ensuring that the right resources, whether they are human,
technological, or financial, are available when needed is crucial for the successful execution of the
project.
Execution, Monitoring & Control: Action Implementation
Following the meticulously aligned implementation plans forged through collaboration withbusiness requesters and the technical project team, the chatbot implementation team proceeds toexecute the project activities. This crucial phase encompasses a series of coordinated steps, beginningwith the development of the chatbot itself. This involves the chatbot's design, coding, andconfiguration, transforming it from concept to reality. Following the development phase, rigorous
testing procedures are carried out to ensure the chatbot's functionality, accuracy, and reliability.
Testing is critical in guaranteeing that the chatbot operates seamlessly and delivers the intended user
experience. Any issues or glitches are identified, addressed, and rectified during this phase to ensure
the chatbot's effectiveness.
Simultaneously, the change management plan is put into action. This entails communicating
and preparing end users and support teams for the forthcoming transition to the knowledge-based
chatbot. Change management involves training, clear communication of the benefits of the chatbot to
end users, and addressing any concerns or resistance that may arise during the adoption anddeployment stage. Finally, the knowledge-based chatbot is ready for deployment after thoroughpreparation and testing. This culminates in the chatbot's go-live phase, where it becomes available toend users for real-time support and assistance for T&E enquiries. The fully operational chatbot
contributes to enhanced end-user experiences in the T&E knowledge domain.

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Closing and Reflection: Post-Implementation Data
After implementing a chatbot, the focus shifts to evaluating the impact of the intervention on
end-user experience. Key data collection methods are from the end user's behaviour towards thechatbot. The first is through the interview sessions with end users. Qualitative data is collected
through interview sessions conducted with selected end users. The objective is to gauge theirexperiences with the chatbot, evaluate its usability, and identify opportunities for continuous
improvement. These interviews help determine if end users find it easy to adapt to the chatbot
technology for T&E queries, assess the quality of their experience in resolving queries via thechatbot, and pinpoint areas where further enhancements can be made. Participants for theseinterviews are selected through convenience sampling, prioritising accessibility.
Table 4
Interview Session Participants Profiling
No

Participant/DesignationRole in T&E Enquiry Management

1

Person C

Employee of Company X from IT department. A casual T&E end user used
T&E enquiry management to understand corporate credit card applicationand claim process, and how to use T&E expense system.

2

Person D

Employee of Company X from IT department. A casual T&E end user, used
T&E enquiry management to claim parking ticket expense and how to use
corporate credit card

Secondly is the secondary data analysis, comparing with the baseline data collected before theintervention. This step examines quantitative data on the percentage of support queries raised throughguided-service channels, which could have been resolved through self-service options. This analysis
tracks whether the chatbot implementation has led to an improved adoption rate of self-service
channels, reducing the percentage of queries that necessitate guided-service support.
OutputIn the short term, this study aims to assess the initial adoption of the chatbot as the first pointof contact for end users seeking support for their queries, in contrast to utilising the knowledge
search portal. Specifically, it will gauge the chatbot experience among a selected group of end
users, seeking to understand their comfort level and willingness to adapt to the chatbot as a primaryresource for T&E-related inquiries. The primary focus here is to evaluate the rate of adaptation to
the chatbot in the early stages of its implementation.
In the medium term, the study evaluates the chatbot's capability to deliver the expected levelof support. This involves measuring the chatbot's effectiveness in assisting end users to resolve
their issues through interactive conversations. Key metrics include the chatbot's success rate inresolving queries and its impact on increasing the adoption of self-service channels while
concurrently reducing the utilisation of T&E service desk tickets. This phase assesses thepracticality and efficiency of the chatbot in enhancing self-service adoption.
In the long term, the ultimate objective is to witness a substantial shift in end-user behaviour.
In this phase, the study envisions end users predominantly opting for the chatbot as their primaryself-service channel. The knowledge search portal becomes a secondary resource, and T&E service
desk tickets are reserved for exceptional cases where the chatbot or existing knowledge articles
cannot address the user's query. The long-term goal is to establish the chatbot as the go-to platform
for end users, marking a significant evolution in the support landscape.

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Logic Model
The Situation
In the current setup, the end users are not guided with the most relevant knowledge article for the most common“How To” queries applicable to their work location, as the current setup gives top 30 searches which gives end
users too much information. Some end users raise tickets asking for “How To” support in the already existing KA
as they could not find the correct KA, which wasted waiting time from the support team when the problem
resolution could be self-service. In other cases, end users also are not sure where to raise tickets and how
correctly to raise tickets to get further support.
Outputs

Inputs
1.

2.

3.

4.

Percentageof queries
ticket raisedby end users
through theguidedservice
supportchannels that
could beaddressed by
self-service
support.
Literature
reviewtheories andmodels
Focus group
discussion on
how thisstudy could
enhance end
userexperience &usability
throughchatbot.
Interview withselected end
users onchatbot
experience,usability &continuous
improvement

1.

2.

3.

4.

Outcome

Activities

Participation

Short Term

Problemdiagnosis:
Determine thecurrent adoption
rate of end user
in knowledge
search portal vsT&E service
desk.
Determine thefunctionality ofchatbot that could
improve in end
user experience.
Implementation
planning,
execution, andcontrol
Data analysis ofpost
implementation if
the knowledge
based chatbot
tool could
improve
adaptation ofself-service
experience inmanaging T&E
queries, and theimpact of chatbot
in improving end
user experiencein T&E queries

1.

1.

All company
users thatengage with
T&E
department
support
2. Target
focus group
discussion
participants
to formulate
implementat
ion planbased ontheidentified
problem.
3. Target groupinterview toassess theimpact ofintervention
in end user
experience.

2.

3.

User tousechatbot for
their firstpoint ofcontact togetsupport intheirqueriesinstead ofgoing totheknowledge
searchportal.
Goodchatbot
experienceIncreasein theadaptation
rate to theselfservice
channels.

1.

2.

3.

Mid Term

Long Term

Chatbot
can provide
expected
support toend users
for selfservice.
Increase
self-service
adoptionrate.
Decrease
the T&E
servicedesk tickets
adoptionrate.

1. Endusers
almos
t fully
adopt
chatb
ot as
their
primary selfservic
e
chann
el,
2. User
only
raise
T&E
service
desktickets
for
excep
tion
basis

Assumptions:
External Factors:
1. Enough project resources from the respective team to
1. Company X is currently undergoing majordeliver roles and responsibilities. Team members have all
global transformation.
the required skills, no change in teams.
2. Potential decision from company leadership
2. All relevant stakeholders will come to respective
on chatbot implementation, which themeetings for project progress. Full support fromchange may impact the implementation
respective stakeholders for new technology adoption to
strategy.
ensure successful go live.
3. Uncertainties may impact the execution and
3. Enough project budget to support deliverables of thetimeline of this project.
project.
4. All reports with needed fields already exist in thetechnical setup as secondary data to compare pre & post
results.

14

e-proceedings book ICoBTA-IR4.0

Enhancing Employee Experience in Expenditure

RESULTS AND DISCUSSIONS
Quantitative Data Collection and Discussion
The researcher collected pre-intervention qualitative data by analysing ticket volume from the
6 months prior to the chatbot implementation month. Out of the total tickets raised, 33.87% required
guided support, indicating complex inquiries that needed agent intervention. 26.12% were T&E
department-initiated, while 22.50% were simple how-to queries that could have been resolved
through the knowledge management portal. The remaining 17.51% were other tickets related tocross-functional resolutions, non-T&E-related topics, duplicate tickets, and more. Based on this data
analysis, there is a clear opportunity to adopt self-service channels to reduce the number of generaland simple ticket queries through the guided-service channel.
To conduct a meaningful analysis of post-intervention qualitative data, it is essential to collectand examine enough ticket volumes, preferably a six-month period following the implementation of
the chatbot. This extended timeframe is necessary to accumulate a sufficiently substantial dataset,
which is critical for conducting a thorough qualitative analysis aimed at understanding the impact of
the knowledge-based chatbot on end users' adoption rates of self-service channels as compared to
guided service channels before and after the chatbot's introduction. The research could not retrieve
significant quantitative post-implementation information due to the time limitation in concluding the
research.
Focus Group Discussion of Chatbot Implementation Strategy
During the preliminary focus group discussion preceding the implementation of chatbot
interventions, the research meticulously identified the tools employed by end users to seek support
within Company X. As employees, they are granted access to the Company X knowledge searchportal, a valuable resource enabling them to independently resolve their queries. For more intricate
issues, end users can create support tickets, obtaining guided assistance from the T&E team.
Additionally, they have the option to reach out for support via telephone. Furthermore, Company X
offers various channels for end users to provide feedback regarding the service they receive. The
T&E team diligently collects and analyses this invaluable feedback alongside ticket volume data,
employing a collaborative tool for quantitative data collection. The analysis unveiled a significant
opportunity for Company X to address 20% of straightforward inquiries via the existing self-service
channel. This achievement aligns with the primary research objective: to assess the current adoption
rate of the self-service channel relative to the guided-service channel represented by the T&E service
desk. A reduction in this percentage would signify a higher level of user adoption for the self-service
channel over the guided-service one.
Simultaneously, during the same focus group discussion, this research recognised several
challenges within Company X's T&E enquiry management process. Despite possessing a knowledge
library capable of handling most end users' queries through self-service, some users still preferalternative support channels. This preference leads to unnecessary waiting times for end users when
they can easily find answers. From the standpoint of the T&E team, the high volume of simple
tickets adds to their workload and prolongs response times for more complex issues. This results ininefficiencies for the T&E team, as these simple queries could be resolved independently. Moreover,dealing with numerous repetitive inquiries may reduce job satisfaction for the resolution team, as
they find themselves providing repetitive answers and performing tasks that don't significantly
contribute value. The root of this challenge lies in end-user resistance to change and their inclination
towards assisted service channels rather than self-service options. The influx of simple tickets createsa demand that exceeds Company X's limited T&E enquiry management resources, exacerbating
existing challenges. Consequently, this data collection effectively fulfils the second research
objective: assessing the opportunity to enhance end user experience through the implementation of achatbot.

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Enhancing Employee Experience in Expenditure

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Guided by insights gained from the focus group discussion, Company X aims to incorporate
several critical functionalities into their chatbot. This includes 24/7 availability, user-friendliness,
accessibility, instant responses, multilingual support, and cognitive abilities to comprehend andaccurately respond to a wide range of global or country-specific queries. Interoperability with theknowledge library for self-service and ticket creation for assisted service ensures a seamless user
experience. Sustainable self-learning capabilities will enable the chatbot to continuously enhance its
intelligence. Tools for analysing ways to optimize the chatbot's performance are also deemed crucial,
ensuring its effectiveness evolves over time. Additionally, the chatbot must adhere to the company's
policies on ethics and compliance while delivering standard customer service. While some nice-tohave features, such as cultural ethics awareness and humour, could personalise the chatbot's
interactions, the primary focus is on developing a proficient virtual assistant rather than creating avirtual best friend. To gauge the success of the chatbot implementation strategies in improving enduser experience in T&E enquiry management, specific key performance indicators have been
identified. Success will be defined by end users proactively choosing the chatbot as their preferredself-service channel. Additionally, the implementation will be considered successful if there is a
substantial reduction in the number of service tickets that could have been easily self-resolved.
Ultimately, the improved end-user experience and satisfaction with the T&E enquiry management
process will testify to the chatbot's effectiveness. This data collection aligns with the fourth research
objective: assessing the opportunity to enhance end-user experience through implementing a chatbot.
Interview Session of End User Experience in Using Chatbot
In subsequent interview sessions with end users following the implementation of chatbot
intervention, the research concentrated on evaluating the end-user experience when using the chatbot
compared to the existing knowledge search portal. The findings indicate a positive response from end
users regarding the chatbot's usability. Users reported that interactions with the chatbot werepleasant, resembling natural conversations with a person. Additionally, end users expressedsatisfaction with the quantity of information provided by the chatbot and found the arrangement
logical when retrieving answers. The chatbot's capabilities in resolving and assisting with end-user
queries were well-received. Users appreciated the chatbot's helpfulness, interactivity, and consistentdelivery of answers. Furthermore, the chatbot's non-verbal capabilities were positively acknowledged
for effectively guiding end users to resolve their queries. The research revealed that end users werewilling to switch to the chatbot as their new self-service channel for T&E enquiry management.
Resistance to transitioning from the existing channel to the chatbot was low, as users found thechatbot easier to use compared to the knowledge search portal. Moreover, end users reported positive
adaptability to the chatbot technology, finding it easy to learn. However, while the chatbot performed
well in providing correct answers to simple and direct questions, there were some limitations inhandling complex and specific queries. This resulted in end users expressing some uncertainty andlack of full confidence in the chatbot's ability to address more intricate issues. Overall, the datacollected from the interviews successfully achieves the research objective of assessing the impact of
the chatbot on improving the end-user experience in the self-service channel. The positive feedback
and willingness to adopt the chatbot as the preferred self-service option indicate its potential toenhance the support system.
During the same interview sessions, the research identified several areas of improvement in thecurrent chatbot capability implemented. Based on feedback from end users, valuable suggestions
were made to enhance the chatbot's performance. Firstly, end users suggested that the chatbot should
be smarter and better equipped to handle specific and complex questions. This improvement would
ensure the chatbot can provide accurate and relevant responses to a wider range of queries. Secondly,
it was recommended that the chatbot should be more personalised, human-like, and friendlier in its
interactions. Adding a touch of personality to the chatbot's responses can enhance the overall user
experience and make the interaction with the chatbot more enjoyable and engaging. Anotherimportant enhancement involves enabling the chatbot to identify situations where there are noavailable answers in the knowledge database for a particular question. In such cases, the chatbot
should be able to seamlessly route the user to the next level of support or direct them to appropriate
16e-proceedings book ICoBTA-IR4.0

Enhancing Employee Experience in Expenditure

resources for assistance. Lastly, end users suggested that the chatbot rephrase questions to validate its
understanding before providing answers. This step would ensure the chatbot interprets the user's
queries accurately and delivers more precise responses. Collectively, these insightful suggestions
from end users highlight the opportunities for further improving the chatbot's capabilities to enhancethe end-user experience. By implementing these enhancements, the chatbot can evolve into a more
efficient, effective, and user-friendly tool, providing better support and service to end users. This datacollection successfully achieves the research objective of assessing the potential areas for improving
the end-user experience through the chatbot's continued development and refinement.
Reflection
The choice of participants for focus group discussions and end-user interviews was strategic.
Focus group discussion participants, given their direct involvement with the topic, offered precise
insights. However, the end-user interviews were limited to just two participants due to stakeholder
unavailability, a constraint that hampers the comprehensiveness of the findings. To address thislimitation and obtain a more holistic understanding of end-user experiences, future interviews should
employ a more diverse participant selection process. This could involve considering participants'
frequency of engagement with enquiry management systems and identifying users according to thetypes described in the literature review. This broader approach will enrich research findings with awider array of perspectives, rendering them more robust and insightful.
In terms of research instruments, this research effectively utilised focus group discussions andinterviews to grasp challenges and explore intervention strategies through qualitative data analysis.
These methods yielded valuable insights. Additionally, the study also considered employing a global
questionnaire to gauge end-user acceptance of the chatbot on a larger scale. However, technicalcomplexities and limitations in survey distribution hindered this approach. Despite this setback, thefocus group discussions and interviews contributed substantial qualitative data, enriching the research.
Future investigations could explore alternative means of reaching a broader audience to enhance thesurvey's scope and validity.
The full desired chatbot functionality, unfortunately, could not be implemented due to technicalconstraints; the implementation team needs to prioritise the most feasible functionality and implement
them in a phased approach to ensure the chatbot can achieve maximised end-user experience aspossible and implement the rest of the functionality in the future enhancements. There were delays in
the live chatbot's actual launch, primarily due to technical issues related to handling unrecognised user
queries, even those pertaining to simple inquiries. Rigorous re-testing ensured the chatbot's efficacy inhandling end-user enquiries and providing intelligent responses. There were also several challenges,
including company transformation and technical delays, that have impacted the implementation
timeline. The complexities of decision-making within Company X during this transformation process
and the interdependencies among project teams contributed to these delays.
The data collection process was meticulously executed, aligning with the research's goals andobjectives. The chosen instruments and insights from prior literature reviews ensured the collected
data precisely addressed the research questions. During data analysis, the results mirrored theanticipated outcomes based on previous research, reinforcing the validity of the selected datacollection methods. This congruence between expected and actual results enhances the credibility ofthe study's conclusions, signifying that the research is on a sound trajectory. By drawing from existing
literature and employing appropriate data collection techniques, this research has laid a robust
foundation for the research project, instilling confidence in its potential to contribute significantly tothe field.

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CONCLUSIONS
Research Impact on PractiseThis study constitutes a significant theoretical contribution to the T&E department and thechatbot implementation team, shedding light on chatbot functionality and the factors influencing enduser adoption of this technology. It enriches our comprehension of implemented chatbot
functionality, end-user experiences with the newly introduced self-service support channel, and the
responses of end users as they transition to this self-service channel in comparison to existing support
methods for T&E enquiry management. Notably, within the realm of chatbot implementation andend-user experiences, prior studies have predominantly adopted applied research approaches. Thereexists a paucity of research employing action research techniques. This study bridges this gap in theaction research literature, focusing on implementing a knowledge-based chatbot to enhance end-user
experiences within a company's operational processes.
In practical terms, this research has made significant contributions to Company X by
elucidating the impact of chatbots on improving end-user experiences. The commitment of the T&E
department to implementing intervention strategies has yielded measurable enhancements in end-user
experiences within the T&E enquiry management process. Furthermore, this research has empowered
Company X with invaluable insights into effective strategies for developing high-quality chatbot
functionality to enhance end-user experiences in enquiry management. By improving the end-user
experience with swift and seamless responses for self-resolving T&E queries, this action research has
facilitated increased adoption of the self-service channel for managing T&E queries. Simultaneously,
it has alleviated the T&E department's bandwidth, enabling them to focus their efforts and response
times on resolving more specific and complex queries. The findings of this action research study holdthe potential to offer crucial insights for real-case implementations of service chatbots, particularly
for companies operating within business service centre models.
Research Limitations and Delimitations
The research project faced a significant constraint related to the implementation timeline. Due
to various factors, including company transformation and unforeseen delays, the originally planned
project timeline could not be adhered to. This timeline constraint impacted the ability to implement
and test the chatbot functionality as initially envisioned, and adjustments had to be made toaccommodate these changes.
External factors and the ongoing company transformation necessitated a downsizing of the
project team. This constraint affected the availability of resources, both human and technological,
required for the successful execution of the project. The reduced team size posed challenges in termsof workload distribution and may have impacted the overall efficiency of the project/
The implementation of the desired chatbot functionality encountered technical challenges.
These challenges included managing unrecognised utterances, particularly for simple queries.
Overcoming these technical hurdles was crucial to ensure the chatbot could effectively handle end
users' inquiries and provide intelligent responses. These technical constraints required additional timeand effort to resolve.
Another constraint was the availability of post-implementation quantitative data. Due to theproject timeline constraint, there was limited time for data collection after the chatbot
implementation. This limitation hindered the ability to gather a robust dataset for assessing the longterm impact of the chatbot on self-service adoption and T&E service desk ticket rates.
The research relied on end-user interviews to gather qualitative data on the end-user
experience with the chatbot. However, due to various constraints, including stakeholder availability18e-proceedings book ICoBTA-IR4.0

Enhancing Employee Experience in Expenditureand logistical challenges, the pool of end-user interviews was limited. This constraint may haveresulted in a lack of comprehensive qualitative data that could encompass the diverse taxonomy ofchatbot end users and their varying usage patterns in T&E enquiry management.
Research Future Recommendations
In future research, it's essential to consider the timing of end-user interviews morestrategically. As pointed out, chatbots require time to learn and improve their understanding of enduser utterances. Interviews should ideally be conducted after the chatbot has had sufficient time forself-learning and optimisation. This ensures that the chatbot is at its optimal level of intelligencecapable of handling both simple and complex queries effectively. Planning interviews at therecommended period post-chatbot implementation will yield more insightful feedback and a better
understanding of the chatbot's performance.
To gain a more comprehensive perspective on the end-user experience, future research should
aim to include a diversified set of end users. This diversity can encompass various aspects, such as
user behaviour and the frequency of engagement with T&E enquiry management. Consider
incorporating the four types of chatbot user taxonomy, including Early Quitters, Progressives,
Pragmatics, and Persistents, into future action research. This approach will help capture a broader
range of user experiences and preferences, contributing to a more well-rounded understanding of thechatbot's impact.
To assess the end-user experience more comprehensively, consider implementing satisfaction
surveys. These surveys can be designed to gather feedback from end users across different
demographic factors. Since the impact of the chatbot extends to global shared service centres in a
multinational company, variations in demographics may influence the user experience of the chatbot.
Surveys can help identify patterns, preferences, and areas for improvement that might be specific tocertain user groups.
Recognise that chatbot technology is not static; it evolves over time. Therefore, future researchshould include plans for continuous chatbot enhancements in subsequent cycles. End-user feedback
is invaluable for identifying areas that require improvement. By incorporating end-user feedback into
the development process, the chatbot's quality can be continuously enhanced. This, in turn, will leadto an improved end-user experience with the knowledge-based chatbot.
Research Conclusion
Action research possesses the potential to catalyse profound and positive transformations within
an organisation, delivering benefits to employees, end users seeking support, the T&E department
responsible for managing the enquiry management process, and the whole company through long-term
gains in cost efficiency and operational improvements. The successful implementation of our action
research at Company X owes much to the open-mindedness and enthusiasm displayed by both theresearch team and the chatbot implementation team, embracing new technology for processenhancement. We recommend that other companies employing enquiry management processes,
especially those grappling with high volumes of repetitive, mundane, and non-value-adding tasks,
seriously contemplate the implementation of automation through knowledge-based chatbots. It isimperative for action researchers to exercise careful consideration of the implementation's limitations,particularly external factors that may exert substantial influence over the implementation's overall
quality and effectiveness.
In summary, this research has made substantial progress in achieving three pivotal research
objectives. Firstly, it effectively assessed the baseline adoption rates among end users, scrutinisingboth the self-service and guided service channels. Secondly, the study meticulously evaluated thepotential for enhancing the end-user experience by integrating a chatbot. Lastly, the research
rigorously examined the impact of the chatbot on elevating the end-user experience within the self19

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service channel. These findings signify remarkable advancements in comprehending the intricacies ofthe support system and underscore the promising advantages of integrating chatbot technology to
streamline end-user interactions. Equipped with these accomplishments, the research is poised toembark on further investigations, leveraging the insights garnered thus far to enhance support services
and more adeptly cater to the diverse needs of end users. As a research methodology, action researchholds substantial promise for technology implementation researchers eager to investigate and resolveorganisational problems effectively while concurrently contributing relevant scholarly knowledge.

REFERENCES
Brandtzaeg, P. B., & Følstad, A. (2017). Why People Use Chatbots. Internet Science, 377–392.
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Dale, R. (2016) The Return of the Chatbots. Nat. Lang. Eng 22(5), 811-817
Følstad, A., & Skjuve, M. (2019). Chatbots for customer service. Proceedings of the 1st International
Conference on Conversational User Interfaces - CUI ’19. https://doi.org/10.1145/3342775.3342784
Gkinko, L., & Elbanna, A. (2022). The appropriation of conversational AI in the workplace: A taxonomy of AI
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Encyclopaedia of Philosophy. https://plato.stanford.edu/entries/idealism/
Haugeland, I. K. F., Følstad, A., Taylor, C., & Bjørkli, C. A. (2022). Understanding the user experience ofcustomer service chatbots: An experimental study of chatbot interaction design. International Journal of
Human-Computer Studies, 161, 102788. https://doi.org/10.1016/j.ijhcs.2022.102788
Janssen, A., Rodríguez Cardona, D., Passlick, J., & Breitner, M. H. (2022). How to Make Chatbots Productive –A user-oriented implementation framework. International Journal of Human-Computer Studies, 168,
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Johari, N. M., & Nohuddin, P. N. (2021). Developing A Good Quality Chatbot. Journal of Information System
and Technology Management, 6(22), 88–102. https://doi.org/10.35631/jistm.622008
Kushwaha, A. K., Kumar, P., & Kar, A. K. (2021). What impacts customer experience for B2B enterprises onusing AI-enabled chatbots? Insights from Big data analytics. Industrial Marketing Management, 98, 207–
221. https://doi.org/10.1016/j.indmarman.2021.08.011
Kvale, K., Freddi, E., Hodnebrog, S., Sell, O. A., & Følstad, A. (2021). Understanding the User Experience ofCustomer Service Chatbots: What Can We Learn from Customer Satisfaction Surveys? Chatbot Researchand Design, 205–218. https://doi.org/10.1007/978-3-030-68288-0_14
Legg, Catherine, & Hookway, C. (2021). Pragmatism (Stanford Encyclopedia of Philosophy). Standford
Encyclopaedia of Philosophy. https://plato.stanford.edu/entries/pragmatism/
Mohamad Suhaili, S., Salim, N., & Jambli, M. N. (2021). Service chatbots: A systematic review. Expert Systems
with Applications, 184, 115461. https://doi.org/10.1016/j.eswa.2021.115461
Ngai, E. W., Lee, M. C., Luo, M., Chan, P. S., & Liang, T. (2021). An intelligent knowledge-based chatbot forcustomer
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Nguyen, T. S. (2019). Potential effects of chatbot technology on customer support: A case study. Aalto
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Research and Design, 32–47. https://doi.org/10.1007/978-3-030-68288-0_3
Telner, J. (2021). Chatbot User Experience: Speed and Content Are King. Lecture Notes in Networks andSystems, 47–54. https://doi.org/10.1007/978-3-030-80624-8_6
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Bridging Local Womenpreneurs' Knowledge
and Application Toward SME Business Digitalisation

Bridging Local Womenpreneurs' Knowledge and Application
Toward SME Business Digitalisation
Jasmine David1, Rozita @ Uji Mohamed2, Agnes Paulus Jidwin3, Dewi Tajuddin4,
Jacqueline Koh Siew Len5
1,3

2,4,5

Fakulti Perakaunan, Universiti Teknologi MARA, Kota Kinabalu, Sabah, Malaysia
Fakulti Pengurusan dan Perniagaan, Universiti Teknologi MARA, Kota Kinabalu, Sabah, Malaysia
juspi330@uitm.edu.my, rozlim97@uitm.edu.my, agnes@uitm.edu.mydewi400@uitm.edu.my, jacqu807@uitm.edu.my

ABSTRACTBusiness digitalisation is a profound transformation of business involving the use of digitaltechnologies to optimise business processes, improve business productivity, and satisfy customers’
experience towards services or products offered. During the pandemic, the new business norm has
urged digitalisation to take place. However, there is an existence of a knowledge gap between men andwomen entrepreneurs when it comes to business digitalisation. This paper aims to study women
entrepreneurs’ knowledge of SME business digitalisation. The significant number of women involved
in SME is seen as vital to support the current mainstream agenda i.e., the Malaysia Digital Economy
Blueprint (MyDIGITAL), how the local womenpreneurs are knowledgeable and applying or engaging
with the digitalisation business, particularly digitalisation on their products, processes, andcommunications. Scarcity in research findings regarding local womenpreneurs’ engagement towards
business digitalisation is real. This gap has motivated such studies to be conducted immediately,
especially during a pandemic. This study applied a quantitative research method to achieve theobjectives. Questionnaires were conducted in data collection. The result of this study helps the relevantbodies, such as the policymaker and local government authorities to strategise better plans andprograms for small- medium enterprises (SMEs) in the future.
Keywords: Womenpreneurs; Digitalization; Knowledge gap; SME; Pandemic; Quantitative method22e-proceedings book ICoBTA-IR4.0

Impact Analysis of CLAHE and UM-Based Image Quality
Improvement on Diabetic Retinopathy Disease Classification

Impact Analysis of CLAHE and UM-Based Image Quality
Improvement on Diabetic Retinopathy Disease Classification
Nurul Muthmainnah Sabrini 1, Kahlil Muchtar 2*, Maulisa Oktiana 3, Roslidar Roslidar 4, Wildan
Mumtaz 5, and Novi Maulina 6
1,2,3,4,5
6

Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Aceh 23111, Indonesia
Doctorate Student of Doctoral Program in Medical Sciences, Faculty of Medicine, Universitas Syiah Kuala,
Banda Aceh, 23116, Indonesia
6
Medical Faculty, Universitas Syiah Kuala, Aceh 23111, Indonesia
1kahlil@usk.ac.id*Corresponding Author

ABSTRACT
Diabetic Retinopathy (DR) is the main cause of human visual impairment. DR will cause the patient
to experience gradual blindness. In the current clinical diagnosis, routine screening of diabetics isdone through fundus examination, which is the most effective way for early detection ofabnormalities. Therefore, a method that can facilitate and assist doctors in identifying DR throughretinal images is required to obtain accurate and precise analysis results. This study proposes amethod of improving image quality using contrast-limited Adaptive Histogram Equalization(CLAHE) and Unsharp Masking (UM) combined with a deep learning-based classification
algorithm of a convolutional neural network (CNN). The CLAHE and UM image results are fed into
CNN. After conducting research, the test results show that the CNN model can distinguish between
negative DR (normal) images and positive DR images (DR patients), and the application of image
enhancement based on CLAHE and UM has better accuracy in several models than the original
image. The results on the CLAHE images show that the method's average accuracy is very good,with 97.2% on ResNet-34, 94.8% on VGG-16, and 97.2% on EfficientNet-B0. The model accuracy
using the UM image is 96.3% on ResNet-34, 90.5% on VGG-16, and 94.8% on EfficientNet-B0. The
model performance is better when trained using CLAHE and UM images than the original image,with accuracy rates of ResNet-34, VGG-16, and EfficientNet-B0 being 96.9%, 96%, and 90%,
respectively. However, the VGG-16 model achieves better accuracy on the original image at 96%,in the CLAHE image at 94.8%, and in the UM image at 90.5%. Meanwhile, the ResNet-34 andEfficientNet-B0 models achieved higher accuracy using CLAHE images, 97.2% for ResNet-34 and97.2% for EfficientNet-B0.
Keywords: Diabetic Retinopathy; Image Enhancement; CLAHE, UM; Deep learning, CNN

INTRODUCTION
Diabetic retinopathy (DR) is a major cause of human visual impairment. Research by Muhammad
Shoaib, et al (Farooq et al., 2022) reported that about 285 million people in the world are affected bydiabetes, and one-third of them have symptoms of DR. This disease tends to attack patients aged 20
years and over. Based on epidemiological research in Europe, America, Asia, and Australia, it was also
reported that the number of people with DR will continue to increase from 100.8 million in 2010 to
154.9 million in 2030, with an estimated 30% of them at risk of blindness.
Diabetes Mellitus (DM) is a disease of the endocrine system diagnosed with abnormally highblood sugar levels and has rapid development around the world. Diabetes can cause increased
mortality, blindness, kidney failure, and decreased quality of life in sufferers. Many complications of23

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Quality Improvement on Diabetic Retinopathy Disease Classification

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DM disease usually develop gradually and can attack the body due to DM disease, such as diabetic
retinopathy (Hendriyawan & Saputro, 2021). DM has affected 463 million people worldwide,
projected to increase to 700 million by 2045 (Tsiknakis et al., 2021). It is estimated that 35% of people
with DR will increase with the age of people with DM (Math & Fatima, 2021).
DR begins by weakening the small capillaries in the retina area and leaking blood, which causes
thickening of the tissue in the retina, swelling, and more extensive bleeding. Features of DR include
microaneurysms, abnormal new blood vessels, leaking blood vessels, retinal swelling, and damaged
nerve tissue (Hayati et al., 2022). DR occurs when blood glucose levels damage blood vessels in theretina. The increase in blood glucose levels causes the arteries in the retina to weaken and leak into theeye, causing blurred vision. Then, the eye can lose vision due to the weak blood vessels forming shards
and flowing blood into the eye area (Kassani et al., 2019).
In current clinical diagnoses, routine screening of diabetics is done through fundus examination,
which is the most effective way to detect abnormalities early. Doctors still do it manually to identify
this disease, which takes time. Therefore, a method that can facilitate and assist doctors in identifying
DR through retinal images is required to obtain accurate and precise analysis results. Classificationand early detection of retinal disorders have always been a big and serious concern for research groups(Math & Fatima, 2021). Currently, the automatic classification of medical images through computer
vision techniques has been widely carried out to help in the early detection of diseases.
Mira Hayati et al. (2022) used the contrast-limited Adaptive Histogram Equalization (CLAHE)
method to improve image quality on DR images, integrated with the Deep Learning classification
algorithm. In this study, the result of the Convolutional Neural Network (CNN) model was comparedto the original and CLAHE images. The obtained results outperformed the best accuracy value,
particularly in the image with CLAHE image quality improvement, where the accuracy value obtained
was 91% for the VGG-16 model, 95% for the InceptionV3 model, and 97% for the EfficientNet model.
However, the accuracy of the original image is superior to the CLAHE image in the ResNet-34 model,
with an accuracy value of 95% for the original image and 84% for the CLAHE image. Thus, the
CLAHE method in the image enhancement process in this study shows a fairly good accuracy value.
Research conducted by Khairul Munadi et al. (2020) deep learning (DL) method was used tosee the effect of image enhancement on DL techniques in identifying Tuberculosis (TB) disease. The
image quality improvement algorithm aims to improve the image's overall characteristics. Unsharp
Masking (UM), High-Frequency Emphasis Filtering (HEF), and CLAHE image quality enhancement
techniques were used, and they were evaluated and tested on pre-trained ResNet and EfficientNet
models. In terms of accuracy, the UM method outperformed the highest accuracy value of 89.92%.
Regarding area under curve (AUC), the method proposed in this study achieved competitive resultswith a value of 94.8%. However, the impact of image quality enhancement on classification accuracy
has not been comprehensively investigated.
Therefore, this research proposes using deep learning techniques to classify two eyes classes as
DR (Positive DR) or normal (Negative DR). Furthermore, CLAHE and UM image quality
improvement methods will be applied to LAB colour channel variants, which will then be integrated
and compared using the VGG-16, ResNet-34, and EfficientNet architectures to build classification
models with the hope that the accuracy will be as accurate as previous studies or better.

METHODOLOGYLAB Colour Space
The LAB colour space (Lightness, A & B) describes all visible colours to the human eye and iscommonly used for vision. CIELAB is a three-dimensional colour model with the largest colourspace
based on human eye colour perception. This LAB colour space consists of 3 layers: the L∗ , a∗ , and b∗24

e-proceedings book ICoBTA-IR4.0

Impact Analysis of CLAHE and UM-Based Image Quality
Improvement on Diabetic Retinopathy Disease Classification

dimensions. The dimension describes the green-red colour type, where -a∗ (negative) indicates green
and +a∗ (positive) indicates red. The b∗ dimension describes the blue-yellow colour type, where -b∗(negative) indicates blue and +b∗ (positive) indicates yellow. The notation of L∗ = 0 (black), and L tothe asterisk operator equals 100 (white), representing reflected light that produces achromatic coloursof white, grey, and black. The L dimension that describes brightness can directly reflect the illustration
status of the image, which is the main quality indicator for the retinal images used in this study(Alamsyah & Pratama, 2019).
Contrast Limited Adaptive Histogram Equalization (CLAHE)
Contrast Limited Adaptive Histogram Equalization (CLAHE) is one of the image quality
improvement methods used in general image enhancement. CLAHE can overcome excessive contrast
enhancement in the Adaptive Histogram Equalization (AHE) method by providing a boundary value
to the histogram. This limit value is called the "Clip Limit", which states the maximum height limit ofa histogram. The CLAHE principle works by increasing the contrast value in an image locally (inspecific areas) such that previously invisible elements (hidden features) become visible. How to
calculate the clip limit of a histogram can be defined by equation (1). β states the clip limit value,
variable M states the region size, variable N states the grayscale value, α is the clip factor, which states
the addition of histogram limits with a value of 1-100, and S"#$ is the maximum allowed slope (Hayati
et al., 2022).
Figure 1
Difference of image enhanced through CLAHE and original image (Hayati et al., 2022)

β =

%
&

'

)1 + ()) (𝑆"#$ – 1)0(1)

Unsharp masking (UM)Unsharp masking (UM) is a commonly used technique in image sharpening enhancement
methods. The UM technique adds a scaled and high-filtered version of the image to the original image
by using a blurred or unsharp negative image to build a mask of the original image. The unsharp mask
is then merged with the original positive image, resulting in a sharper image than the original image.
The mathematical equation for the Unsharp Masking algorithm can be defined by equation (2), whichx(m, n) denotes the signal of the original image, y(m, n) denotes the signal of the enhanced image,z(m, n) is the enhancement operator, and λ(m, n) is the enhancement coefficient factor that used tocontrol the enhancement rate (Munadi et al., 2020).
y(m, n) = x(m, n) + λ(m, n) ∗ z(m, n)(2)

Deep learning
Deep learning is a branch of machine learning and artificial intelligence. Deep learning uses aneural network consisting of many layers (hidden layer), which is implemented on problems with large
data sets to classify commands and produce outputs. Deep learning uses the concept of a neural networkthat functions similarly to the human brain, allowing it to learn supervised learning or data features25

Impact Analysis of CLAHE and UM-Based Image
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automatically and draw conclusions like humans when analysing logical and sequential data structures(Math & Fatima, 2021).
Figure 2
Neural Network Structure (Hayati et al., 2022)

Convolutional Neural Network (CNN)The Convolutional Neural Network (CNN) is a part of the Artificial Neural Network that is usedin image classification and image processing. CNN is a type of Deep Neural Network, as it has a highnetwork depth and is widely used in image data. CNN has four main layers: the convolution layer,
pooling layer, activation function, and fully connected layer (Muslih & Rachmawanto, 2022).
1. Convolution Layer

The convolution layer consists of kernels that use an N-dimensional matrix to generate an output
feature map for the next layer. Each data entering the convolution layer will go through a convolution
process where each filter in the layer will be converted to the entire input data. The filters in this layer
have length, height, and thickness according to the input data channel.
2. Pooling Layer

The Pooling layer is the next step after the convolution layer operation. The Pooling layer isused to sub-sample the feature map by taking a larger-size feature map and shrinking it to a lower-sizefeature map. When the feature map is shrunk, the most dominant feature is retained in each poolingstep. Various pooling techniques are used in different pooling layers, such as max pooling, minpooling, and average pooling.
3. Fully Connected Layer

The fully connected layer is the last layer of the CNN. This part consists of neurons in layers
fully connected in one layer with the previous layer. The fully connected layer consists of the input
layer, hidden layer, and output layer.

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Impact Analysis of CLAHE and UM-Based Image Quality
Improvement on Diabetic Retinopathy Disease Classification

Figure 3
Neural Network Convolution Layer (Frangky Handono, Stevanus; Tri Anggraeny, Fetty, Rahmat, 2020)

RESULTS AND DISCUSSIONS
Dataset Condition
The source dataset for this study was taken from APTOS 2019 (APTOS, n.d.) The dataset
consists of two classes: the negative class (normal) and the positive class (DR patients). The dataset
contains 3,288, with 1,799 negative and 1,489 positive class images. The entire dataset image thengoes to CLAHE and UM pre-processing. Table 3.1 shows the appearance comparison of the original,
CLAHE, and UM image samples.
Table 1
Differences in Appearance of Original Image, CLAHE Image, and UM Image

Original Image

CLAHE Image

UM Image(a) Negative DR Image(b) Positive DR Image

CLAHE Implementation Result
This pre-processing method compares the accuracy level between the original image and theimage pre-processed with CLAHE. The original image in RGB colour space is used as the input image
for the CLAHE application process, which is then transferred to LAB colour space, and the results of
the CLAHE pre-process are converted back into RGB colour space. One of the goals of this study is
to obtain different visual characteristics that can affect the performance of deep learning networks byusing colour spaces other than RGB. The application of CLAHE in this study uses a clip limit of one
window tile with default values of (8,8). After CLAHE pre-processing, the resulting image shown in
Table 2, will become the new input for the architecture model proposed in this study.

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Impact Analysis of CLAHE and UM-Based Image
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Table 2Difference between Original Image and CLAHE Image
Negative DR

Positive DR

Original Image CLAHE Image Original Image CLAHE Image

(a)

(b)

(c)

(d)

UM Implementation Results
This method uses a smoothed positive image from the original image, which is then combinedwith a negative image to produce a sharper image than the original (Munadi et al., 2020). After UM
pre-processing, the resulting image becomes clearer with an increase in the sharpness of the image
edges, making the details of objects in the eye appear more real. Furthermore, the texture of the bloodvessels and disease spots on the retina of the eye, as well as blood clots in the positive image (DRpatient), appear clearer with a higher contrast level than the image without enhancement as shown in
Table 3. This enhanced image is used as the new input for the architecture model proposed in thisstudy.
Table 3Differences between Original Image and UM Image
Negative DR
Positive DR
Original
UM Image Original Image UM Image
Image

(a)

(b)

(c)

(d)

System Training Results
The dataset used comprises three types of datasets: original images, CLAHE images, and UM
images. Then, each dataset is divided into three parts: 70% for training data, 20% for validation data,
and 10% for test data, as shown in Table 4. Before carrying out the training process, the first stage isto design the classification model system with the ResNet-34, VGG-16, and Efficient Net architecturesand set their parameter as shown in Table 5. The training process of the three models for each dataset
yields evaluation parameters such as training time, training accuracy & loss accuracy, and validation
accuracy and loss, as shown in Table 6-8.

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Impact Analysis of CLAHE and UM-Based Image Quality
Improvement on Diabetic Retinopathy Disease Classification

Table 4
Details of Image Sharing
Data Type

Positive DR

Negative DR

Training Data

1.040

1.259

Validation Data

297

362

Test Data

149

178

Total

1.489

1.799

Table 5
Parameters Used in the Training Process
Parameters

ResNet-34

VGG-16

Efficient Net

Epoch

50

50

50

Batch Size

2

2

2

Loss Function

Cross-entropy

Cross-entropy

Cross-entropy

Optimizer

SGD

SGD

SGD

Momentum Optimizer

0.9

0.9

0.9

Learning rate optimizer

0.001

0.00001

0.01

Table 6
Accuracy and Validation Results on Original Image Training
Architecture
ResNet-34
VGG-16
EfficientNet- B0

Train Time
79 minutes 16 seconds
85 minutes 54 seconds
102minutes 4 seconds

Train Acc
0.99220.96130.9548Train Loss
0.03030.10670.1128Val Acc0.9772
0.97120.9363

Val Loss
0.06200.07620.0303

Val Acc
0.98630.97270.9848Val Loss
0.371
0.783
0.0587

Table 7
Accuracy and Validation Results on CLAHE Image Training
Architecture
ResNet-34
VGG-16
EfficientNet-B0

TrainTime60 minutes 49 seco nds
86 minutes 22 seconds
96 minutes 15 seconds

Train Acc
0.9974
0.94820.9839Train Loss
0.01280.13230.0421

Table 8Accuracy and Validation Results on UM Image Training
Architecture
ResNet-34
VGG-16
EfficientNet- B0

Train Time
57 minutes 38 seconds
72 minutes 59 seconds
74minutes 17 seconds

Train Acc
0.99570.96100.9853Train Loss
0.01690.09900.0466

Val Acc
0.98480.97120.9788Val Loss
0.03290.882
0.0596

System Testing Results
It is shown that the pre-trained model is good enough to predict new data on the original image,
CLAHE image, and UM image from negative and positive images. In the original image data, theResNet-34 architecture achieved an accuracy value of 97%, then VGG-16 achieved an accuracy valueof 96%, and EfficientNet-B0 of 90%. In the CLAHE image, the ResNet-34 architecture achieved an
accuracy value of 97.2%, VGG-16 achieved an accuracy value of 94.8%, and EfficientNet-B0 was
97.2%. In the UM image, the ResNet-34 architecture achieved an accuracy value of 96.3%, VGG-16

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Impact Analysis of CLAHE and UM-Based Image
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e-proceedings book ICoBTA-IR4.0

achieved an accuracy value of 90.5%, and EfficientNet-B0 of 94.8%.
The testing accuracy value, as shown in Table 9. shows the ability of the model to predict almost
all test data with good prediction results. In this study, the three models with the parameters used wereable to provide good performance and performance.
Table 9Model Test Data Accuracy Results
Architecture

Original Image Accuracy

CLAHE Image Accuracy

UM Image Accuracy

ResNet-34

0,969

0,972

0,963

VGG-16

0,960

0,948

0,905

EfficientNet-B0

0,905

0,972

0,948

Table 10Comparison of Model Testing Results on Negative Image Classes
Negative DR
Architecture

Original
Image

CLAHE
Image

UM Image

ResNet-34

VGG-16

EfficientNet-B0

Table 11Comparison of Model Testing Results on Positive Image Classes
Positive DR
Architecture

ResNet-34

VGG-16

EfficientNet-B030

Original
Image

CLAHE
Image

UM Imagee-proceedings book ICoBTA-IR4.0

Impact Analysis of CLAHE and UM-Based Image Quality
Improvement on Diabetic Retinopathy Disease Classification

CONCLUSION
The model's performance trained using the application of CLAHE and UM demonstrates thesuperiority of utilising the EfficientNet-B0 architecture. This is proven by the visual graph of the
CLAHE image data, which shows an improvement in the EfficientNet-B0 architecture. ResNet-34
provides optimal values for all three datasets: the original image data, CLAHE images, and UM
images. Based on the visuals and accuracy obtained from the research, it is evident that image datawith the application of CLAHE and UM methods outperforms the original image data. This isdemonstrated by applying the CLAHE and UM methods, where positive and negative image datacharacteristics appear much clearer than the original image data.

ACKNOWLEDGEMENT
We thank the Master of Electrical Engineering Study Program and Erasmus+ Ind4.0 Programfor supporting this research.

REFERENCES
Alamsyah, D., & Pratama, D. (2019). Segmentasi Warna Citra Bunga Daisy dengan Algoritma K-Means padaRuang Warna Lab. Jurnal Buana Informatika, 10(2), 153-163.
Karthik,
Maggie,&SohierDane(2019).
APTOS
https://kaggle.com/competitions/aptos2019-blindness-detection

2019

Blindness

Detection.

Farooq, M. S., Arooj, A., Alroobaea, R., Baqasah, A. M., Jabarulla, M. Y., Singh, D., & Sardar, R. (2022).
Untangling computer-aided diagnostic system for screening diabetic retinopathy based on deep learningtechniques. Sensors, 22(5), 1803.
Handono, S. F., Anggraeny, F. T., & Rahmat, B. (2020). Implementasi Convolutional Neural Network (CNN)
untuk Deteksi Retinopati Diabetik. Jurnal Informatika dan Sistem Informasi, 1(2), 669-678.
Hayati, M., Muchtar, K., Maulina, N., Syamsuddin, I., Elwirehardja, G. N., & Pardamean, B. (2023). Impact of
CLAHE-based image enhancement for diabetic retinopathy classification through deep learning. Procedia
Computer Science, 216, 57–66.
Hendriyawan, M. A., & Saputro, W. R. (2021). Diabetic Retinopathy Severity Level Classification Based on
Fundus Image Using Convolutional Neural Network (CNN). In Seminar Nasional Informatika(SEMNASIF), 1(1), 173–185. http://www.jurnal.upnyk.ac.id/index.php/semnasif/article/view/6071
Kassani, S. H., Kassani, P. H., Khazaeinezhad, R., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019).
Diabetic retinopathy classification using a modified xception architecture. In 2019 IEEE International
Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 1–6). IEEE.
Math, L., & Fatima, R. (2021). Adaptive machine learning classification for diabetic retinopathy. Multimedia
Tools and Applications, 80(4), 5173–5186.
Munadi, K., Muchtar, K., Maulina, N., & Pradhan, B. (2020). Image enhancement for tuberculosis detection
using deep learning. IEEE Access, 8, 217897–217907.
Muslih, M., & Rachmawanto, E. H. (2022). Convolutional Neural Network (CNN) untuk Klasifikasi Citra
Penyakit Diabetes Retinopathy. Skanika, 5(2), 167–176.
Tsiknakis, N., Theodoropoulos, D., Manikis, G., Ktistakis, E., Boutsora, O., Berto, A., Scarpa, F., Scarpa, A.,
Fotiadis, D. I., & Marias, K. (2021). Deep learning for diabetic retinopathy detection and classification
based on fundus images: A review. Computers in Biology and Medicine, 135, 104599.

31e-proceedings book ICoBTA-IR4.0

A Framework for Non-Homogeneous Data Generation
Federated Machine Learning

A Framework for Non-Homogeneous Data Generation inFederated Machine Learning
Daniel Mauricio Jimenez Gutierrez 1, Aris Anagnostopoulos 2, Ioannis Chatzigiannakis3 and Andrea
Vitaletti 4
1,2,3,4
1

Via Ariosto 25, Rome, Italy

jimenezgutierrez@diag.uniroma1.it; 2 aris@diag.uniroma1.it; 3 ichatz@diag.uniroma1.it;
4vitaletti@diag.uniroma1.it

ABSTRACT
A potential technique for training Machine Learning (ML) models on large amounts of decentralised
data from many entities is federated learning (FL). However, FL cannot guarantee independent andidentically distributed (IID) data, unlike centralised ML (CL). To describe the functioning of FL, it istherefore essential to measure the amount of IID data among the decentralised data. Regrettably,federated data is not as widely available as centralised data, which makes it more challenging tosimulate real-world scenarios. This work presents various techniques to produce a measured degreeof IID data by splitting centralised data into multiple nodes. We present the distances used to measure
the extent of IID data spread throughout the decentralised data. Additionally, we put into practice
partitioning techniques according to client distribution. Our suggestions are all contained in aframework that runs on Python. It makes it easier to compare the findings of CL and FL research. It
makes it easier to conduct FL research by enabling the methodical and controlled partitioning ofcentralised datasets. Considering the skew in the labels' distribution, that split can be made. We
experimented with the ECG arrhythmia detection sector to illustrate our suggested methodology. The
findings demonstrate that our method can produce federated datasets appropriate for training models
across several nodes and offer precise measurements of the non-homogeneity of client distributions.
Keywords: Federated Learning; Artificial Intelligence; Machine Learning; non-homogeneousclients; federated data

INTRODUCTION
Remarkable machine learning (ML) services have emerged in the last few years (Bohr &Memarzadeh, 2020; Krittanawong, 2018), trained on an enormous amount of publicly available data
(centralised ML, or CL). Certain pertinent situations do, however, occur when data is reasonable andprivate; for example, financial or health data are typically safeguarded and challenging to share. An
EU regulation known as the GDPR (Phillips, 2018) restricts data flow between companies and nations
and establishes stringent guidelines for handling sensitive data. It is said that having such stringent
regulations will encourage the development of data silos. Protecting data privacy in ML is essential
to safeguard sensitive information, maintain trust, adhere to legal requirements, and prevent biases,
ensuring responsible and ethical AI development.
Federated Learning (FL) (McMahan et al., 2017) runs an ML algorithm across a federation ofnodes, each contributing a dataset to calculate a shared model. This method is effective in running
ML algorithms across a federation of nodes. FL preserves privacy by enabling model training on
decentralised client devices, localising sensitive data, and only sharing model updates. Differential
privacy and secure aggregation techniques protect individual data while improving the model's
performance. However, FL is difficult because data is not spread identically or independently acrossnumerous places (IID). This raises the question of whether FL can perform as well as CL.

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Federated Machine Learning

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To solve the lack of FL datasets, this work uses metrics to assess data distribution nonhomogeneity between FL nodes. It creates federated datasets from centralized ones with controlled
non-homogeneous properties. Furthermore, the suggested metrics and division strategies are
implemented inside an adaptable framework that generates FL datasets from any centralized one.
Regarding the construction of federated datasets, a method for partitioning CL via label distribution
was proposed by (T. Lin et al., 2020) and (Hsieh et al., 2020). In addition, (Li et al., 2022) havedeveloped a suitable approach for the feature skew (Ma et al., 2022) in which the features are alteredby summing Gaussian Noise.

METHODOLOGY
We consider values that quantify the degree of non-uniformity between two or more clients.
First, there is the one revealed by the similarity measure (Nielsen, 2019) that compares two probability
distributions. Second, a metric for gauging the difference in probability distributions between the two
is put forth (Goussakov, 2020). The fact that the suggested distances are restricted between 0 and 1,
where 1 denotes completely different distributions and 0 indicates identical distributions, is one of its
advantages. They also meet the triangular inequality. The techniques presented by (Hsieh et al., 2020)and (J. Lin, 2016; T. Lin et al., 2020) are applied to generate artificial partitions of a centralised
dataset, considering the label skew partition.
Using the Physionet 2020 competition integrating six varied datasets (Perez Alday et al., 2021),
we consider high-quality 12-lead electrocardiography (ECG) data from several health facilities withdifferent ECG recording devices to categorise different cardiac abnormalities. We chose a Deep
Neural Network (DNN) and a Long-Short Term Memory (LSTM) model (Murat et al., 2020) as thecompetition's highest-achieving model (Hong et al., 2022).

RESULTS AND DISCUSSIONSThis experiment shows how to divide the dataset according to the distribution of the labels. In thesequel, we analyse multiple clients and use varying degrees of α to adjust the label skewness of theresulting partition. Initially, we merge all the partitions into a centralised dataset. We utilise thedistances to calculate the degree of similarity between the distributions of the various clients for eachresulting division. We focus on assessing the case with four clients' performances.
Figure 1
Performance of the DNN and LSTM models for CL and FL.

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A Framework for Non-Homogeneous Data Generation
Federated Machine Learning

After applying the framework with the resulting partitions, we measure the performance of thetwo models for the CL and FL versions. The performance attained for the resulting divisions basedon the measured distance is shown in Figure 1 using the F1-score. Observe that the achieved
performance decreases with increasing non-homogeneity.

CONCLUSION
To simplify comparisons between CL and FL research, the experiments in this study showed how
effective the framework is at creating decentralised datasets from a centralised dataset. The outcomesdemonstrated that the instrument was able to provide consistent datasets with varying levels ofvariability via a quantifiable and controlled degree of non-homogeneity, even exhibiting performancethat was comparable to FL instances found in real life. We have used the centralised dataset for our
tests. By reducing the parameter α, the strategies enabled us to increase the distance between divisions.
Additionally, the FL's performance in terms of the F1-Score decreases when the label skewness rises.
Regarding the F1-Score, the performance of the CL and FL are nearly equal when there is noskewness.

ACKNOWLEDGMENTS
This work was partially supported by PNRR351 TECHNOPOLE - NEXT GEN EU Roma Technopole- Digital Transition, FP2 - Energy transition and digital transition in urban regeneration andconstruction.

REFERENCES
Bohr, A., & Memarzadeh, K. (2020). Chapter 2—The rise of artificial intelligence in healthcare applications. In
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Goussakov, R. (2020). Hellinger Distance-based Similarity Measures for Recommender Systems. 18.
Hong, S., Zhang, W., Sun, C., Zhou, Y., & Li, H. (2022). Practical Lessons on 12-Lead ECG Classification:
Meta-analysis of methods from PhysioNet/Computing in cardiology challenge 2020. Frontiers in
Physiology, 12. https://www.frontiersin.org/articles/10.3389/fphys.2021.811661
Hsieh, K., Phanishayee, A., Mutlu, O., & Gibbons, P. B. (2020). The Non-IID Data Quagmire of Decentralized
Machine Learning (arXiv:1910.00189). arXiv. https://doi.org/10.48550/arXiv.1910.00189
Krittanawong, C. (2018). The rise of artificial intelligence and the uncertain future for physicians. European
Journal of Internal Medicine, 48, e13–e14. https://doi.org/10.1016/j.ejim.2017.06.017
Li, Q., Diao, Y., Chen, Q., & He, B. (2022). Federated Learning on Non-IID Data Silos: An Experimental Study.
2022 IEEE 38th International Conference on Data Engineering (ICDE), 965–978.
https://doi.org/10.1109/ICDE53745.2022.00077
Lin, J. (2016). On The Dirichlet Distribution.
Lin, T., Kong, L., Stich, S. U., & Jaggi, M. (2020). Ensemble Distillation for Robust Model Fusion in Federated
Learning, 25.
Ma, X., Zhu, J., Lin, Z., Chen, S., & Qin, Y. (2022). A state-of-the-art survey on solving non-IID data inFederated
Learning.
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Federated Machine Learning

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McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-Efficient
Learning of Deep Networks from Decentralized Data (arXiv:1602.05629). arXiv.
http://arxiv.org/abs/1602.05629
Murat, F., Yildirim, O., Talo, M., Baloglu, U. B., Demir, Y., & Acharya, U. R. (2020). Application of deeplearning techniques for heartbeats detection using ECG signals-analysis and review. Computers in Biology
and Medicine, 120, 103726. https://doi.org/10.1016/j.compbiomed.2020.103726
Nielsen, F. (2019). On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means. Entropy,
21(5), Article 5. https://doi.org/10.3390/e21050485
Perez Alday, E. A., Gu, A., J Shah, A., Robichaux, C., Ian Wong, A.-K., Liu, C., Liu, F., Bahrami Rad, A., Elola,A., Seyedi, S., Li, Q., Sharma, A., Clifford, G. D., & Reyna, M. A. (2021). Classification of 12-lead ECGs:
The PhysioNet/Computing in Cardiology Challenge 2020. Physiological Measurement, 41(12), 124003.
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Phillips, M. (2018). International data-sharing norms: From the OECD to the General Data Protection Regulation(GDPR). Human Genetics, 137(8), 575–582. https://doi.org/10.1007/s00439-018-1919-7

35

Development and Implementation of Smart IoT-Based
Aquascape Monitoring Systeme-proceedings book ICoBTA-IR4.0

Development and Implementation of Smart IoT-Based Aquascape
Monitoring System
Muhamad Amjad Mohd Azmi 1, Ghulam Fizza 2 and Haidawati Nasir 3*
1,3

Full Computer Engineering Technology, Universiti Kuala Lumpur
Malaysian Institute of Information Technology
2Electrical and Electronic Engineering, Universiti Kuala Lumpur British Malaysian Institute1

amjad.azmi@s.unikl.edu.my; 2ghulam.fizza@s.unikl.edu.my; 3haidawati@unikl.edu.my*Corresponding Author

ABSTRACT
Aquascaping, the intricate art of creating appealing underwater landscapes, demands an extensive
understanding of aquatic ecosystems and detailed maintenance. The application of technology,
specifically the Internet of Things and artificial intelligence, has begun to significantly influence thisfield, enabling precise, real-time monitoring and control of vital parameters like temperature, pH,
feeding and lighting. This paper presents the design and implementation of a Smart IoT-Based
Aquascape Monitoring System that leverages IoT technology to enhance the efficiency andsustainability of aquascaping. The system incorporates an ESP32 microcontroller and various sensors
and employs the Blynk platform for data display and user interaction. The proof-of-concept was tested
in an aquascape setup, successfully automating several maintenance aspects such as temperature
regulation, pH level monitoring, and fish feeding. This innovative technology integration aims to makethe rewarding yet demanding hobby of aquascaping more accessible and manageable.

Keywords: Aquascaping, IoT; Sensors; ESP32 microcontroller
INTRODUCTION
Aquascaping is an artful arrangement of aquatic plants, rocks, wood, and substrates that aims to create
an engaging underwater ecosystem within an aquarium. In this context, every element, both biotic andabiotic, plays a pivotal role in the overall aesthetic appeal as well as the chemical and biological balanceessential for the sustenance of the aquatic life housed within. The intricate process of aquascaping
requires a blend of patience and proficiency. Despite its challenges, it is a hobby that offers
considerable benefits. It contributes positively to mental health by offering a soothing retreat, but whenpursued passionately, it can also evolve into a profitable venture. The primary objective of aquascaping
extends beyond creating an aesthetically pleasing aquatic landscape. It also encompasses various
technical considerations such as substrate selection, water quality maintenance, plant and fish care,ornamentation, and an ongoing commitment to overall upkeep. Contrary to conventional fish tanks,
aquascapes place a heightened emphasis on the depiction of diverse flora and the panorama it creates.
This distinction necessitates special attention to plant care, making aquascapes a unique blend of art
and science (Hetami et al., 2023). Despite the aesthetic appeal and mental health benefits that thishobby offers, it comes with its own set of challenges. Maintaining the health and balance of theseminiaturised ecosystems requires constant vigilance and can be labour–intensive. Variables such as
water quality, temperature, pH levels, and feeding schedules require continuous monitoring to ensurea thriving environment. In response to these challenges, the study presents the development andimplementation of a smart Internet of Things (IoT)-based aquascape monitoring system.

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Development and Implementation of Smart IoT-Based
Aquascape Monitoring System

Incorporating advanced technologies and data analytics into aquascaping practices heralds theadvent of smart aquascaping. This innovative approach to aquaculture enhances its efficiency,
sustainability, and productivity by leveraging the power of modern digital tools. At the heart of smart
aquascaping is integrating technologies such as sensors, artificial intelligence (AI), and the Internet ofThings (IoT). The IoT functions as a digital nexus that enables communication among various devices.
This interconnectedness, integral to the IoT structure, promotes the smooth exchange of data andcoordination of tasks across different elements of an aquascape setup (Rastegari et al., 2023). The
amalgamation of these technologies within aquascaping cultivates a more accurate, data-informed
methodology for handling and optimising aquascape conditions. This system aims to automate several
facets of aquascape maintenance, such as monitoring temperature and pH levels, controlling lighting,and managing fish feeding.
This paper is organised into four sections. Section 2 will encompass a review of the literature
pertaining to related work. Section 3 will delve into the methodology used, while Section 4 will be
dedicated to discussing the results. The study will be concluded in Section 5.

REVIEW OF RELATED WORKThe aquascaping, the art of creating aesthetically pleasing underwater landscapes, requires acomprehensive understanding of the aquatic ecosystem. It involves monitoring and controlling
numerous variables to maintain the health and balance of the system. Recently, technology has playeda significant role in this field, particularly using the Internet of Things (IoT) and artificial intelligence(AI). Aquatic environment monitoring has seen the successful application of IoT technologies. Realtime data provided by wireless sensor networks allow for proactive management of crucial parameters,such as temperature, pH, dissolved oxygen, and ammonia levels. In addition, AI and machine learning
have been used to predict water quality parameters and analyse fish behaviour, among otherapplications. Machine vision, for instance, can aid in fish detection, species classification, and feeding
behaviour analysis. Blockchain technology has also found its way into the aquaculture industry,enhancing traceability in the fishery supply chain(Dilli Kumar V et al., 2023; Khudoyberdiev et al.,2023; Zhang & Gui, 2023). The application of intelligent systems, particularly those leveraging
Internet of Things (IoT) technology, has significantly increased. Such research initiatives aim to assist
users in sustaining ideal environmental conditions within their plant aquariums (Hutabarat et al., 2022).
The research of Akhter and colleagues (Akhter et al., 2021) explores the crucial water parameters forfish farming, such as temperature, pH, nitrate, phosphate, calcium, magnesium, and dissolved oxygen,and review the existing sensors designed to measure these parameters. The researchers underscore thatmany current sensors are costly, necessitate enhancements in sensitivity and power requirements,
require improved compatibility with IoT technology, and need to sustain continuous field usage. Theysuggest implementing a cost-effective system, employing both commercially available and custombuilt sensors, which they deemed economical and efficient for field applications in fish farming.
Petkovski et al. (2021) delve into examining IoT-based systems specifically designed for aquaculture.
The researchers pose five research queries to identify the sensor types in use, the varieties of singleboard computers utilised, the data transport protocols in place, the cloud-based platforms applied inaquaculture, and the advantages of employing IoT. Their findings reveal the usage of seventeen distinct
types of sensors, with temperature, pH, and dissolved oxygen sensors emerging as the most prevalent.
Raspberry Pi, Arduino, and ESP were the most frequently utilised single-board computers. Despite
providing an overview of the sensor types, the authors did not delve into details such as the
manufacturing process, sensor models, or cost considerations associated with these sensors.
Despite the progress made, there remains a noticeable absence of comprehensive, user-friendly
IoT solutions tailored for the maintenance and monitoring of aquascapes. Many of the current systems
are geared towards larger-scale applications such as marine aquaculture or commercial fish farming,
which may not account for the unique considerations that come with aquascaping, including the needto delicately balance various parameters to preserve the aesthetic integrity of the setup. This study

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introduces the design and execution of an IoT-based aquascape monitoring system that quantifies
critical parameters such as pH, temperature, and food incorporates automated controls for feeding fishand managing light conditions. This system, specifically designed for aquascaping, fills a noticeable
void in the research by offering a comprehensive, cost-effective, and user-friendly solution foraquascape upkeep and monitoring. This integration of IoT technology with the unique needs ofaquascaping paves the way for the exploration of innovative, custom-made solutions within this field,
thereby making this enriching hobby more accessible and manageable.

METHODOLOGY
The overall methodology for developing and implementing the smart IoT-based aquascape Monitoring
System is visualised in Figure 1.
Figure 1
Block Diagram of Smart IoT-Based Aquascape Monitoring System

The fundamental component of the system is a collection of sensors designed to monitor a rangeof parameters essential for maintaining a thriving aquascape. Included in this sensor suite are a pH
sensor for the assessment of water acidity, a temperature sensor for tracking the thermal state of thewater, a load cell sensor in conjunction with an HX711 Module for gauging weight, and a servo motor
to regulate the feeding of fish. The sensory data collected is conveyed to an ESP32 microcontroller, aneconomical system-on-a-chip microcontroller fortified with integrated Wi-Fi and dual-mode Bluetoothcapabilities. This primary function is to interpret and process the data produced by the sensors.
Additionally, it governs the relay and OLED display, as well as handles the communication with the
Blynk server. After data processing, the ESP32 microcontroller communicates the information to the
Blynk server via Wi-Fi. The Blynk server serves as a cloud-based platform tailored for IoT
applications, enabling remote supervision and management of the system. It offers a user-friendly
interface where real-time sensor data is exhibited, thus ensuring continual monitoring of aquascapeconditions. Access to the system's user interface is provided through the Blynk app, operable via asmartphone or computer. This app grants users real-time updates on the aquascape status, including
metrics such as pH level, temperature, and weight. In addition, it empowers users to remotely command
the servo motor for fish feeding, control the relay, and observe real-time readings on the OLED display.

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Development and Implementation of Smart IoT-Based
Aquascape Monitoring System

Figure 2
Creation of the diagram using the fritzing tool

The schematic illustration of the proposed system's hardware is presented in Figure 2. The
microcontroller, which acts as the heart of the system, is tasked with processing the signals it receives
from the sensor module. Alongside this, a microcomputer is employed to prepare the sensor readingsfor onward transmission to the Blynk server platform. The sensor module comprises a temperature
sensor and a pH sensor, while the system's lighting is controlled through LED lights.

RESULTS AND DISCUSSIONSThe prototype of a smart IoT-Based Aquascape Monitoring System is illustrated in Figure 3. For thisproject, a hands-on, proof-of-concept assessment was conducted within an aquascape environment,
presenting a concrete example of the custom-built circuit developed for this research. The initiation ofsystem evaluation involved the loading and running the Arduino IDE's file codes on the ESP32
microcontroller. The aquascape habitat was equipped with critical components, such as pH andtemperature sensors, to sustain an ideal environment for aquatic life. LEDs were integrated as a lightsource to mimic the required lighting conditions, promoting the growth and survival of the organisms
within the aquascape.
Figure 1
Prototype for Smart IoT-Based Aquascape Monitoring System

39Development and Implementation of Smart IoT-Based
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Temperature Monitoring
Maintaining a balanced aquatic environment necessitates consistent water temperaturemonitoring, as shown in Table 1. The sensor was tested for accuracy by immersing it in water atvarying temperatures. The readings of sensors were then compared with those of a digital thermometer.
The system showed a high level of precision, with sensor readings closely aligning with thethermometer readings, regardless of the temperature of the water.
Table 1
Monitoring the Temperature of Water
Activities
Soak temperature sensor in hot water

Expected Result
The temperature reading same as thethermometer reading.

Actual results
Were as expected

Soak temperature sensor in cold water

The temperature reading same as thethermometer reading.

Were as expected

PH Monitoring
The pH sensor of a smart IoT-based aquascape monitoring system was evaluated next in Table
2. The sensor was immersed in water with known pH values obtained by using pH powder. Although
the sensor provided less accurate readings due to its lower quality, it could still provide a reasonableestimate of the water's pH level.
Table 2
Monitoring the pH of Water
Activities
Soak the pH sensor in water with areading pH of 9.18.

Expected Result
The pH reading same as the brewing
water.

Soak the pH sensor in water with areading pH of 4.01.

The pH reading same as the brewing
water

Actual results
Less accurate reading
due to low-quality
sensor.
Less accurate reading
due to low-quality
sensor.

Auto-Feeding
The auto-feeding mechanism of the system, controlled by a servo motor, was also tested, as
depicted in Table 3. The motor was programmed to rotate and release a controlled amount of food into
the aquarium at specific times. The mechanism functioned as expected, consistently releasing food at
the designated times.
Table 3
Auto Feeding Testing
Activities
Set a certain timer to start feeding.

Expected Result
The Servo motor starts to rotate
when the time comes.

Actual results
Were as expected

Quantity of Food Given
The system was designed to display the remaining amount of fish food on an OLED display
depicted in Table 4. This feature was tested by reducing the amount of food in the container andobserving the weight displayed. In most cases, the display showed the correct weight. However, when
the amount of food was reduced by 2 grams from an initial 50 grams, the display still showed 50 grams.
This minor discrepancy could be due to the sensitivity of the weight measurement mechanism.
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Development and Implementation of Smart IoT-Based
Aquascape Monitoring System

Table 4
Testing Quantity of Food
Activities
Set a certain timer to give start feeding.
reducing the amount of fish food by 2grams out of 50 grams.

Expected Result
The food weight displayed is 48
grams

Actual results
The display is still 50 grams
after reducing 2 grams of
the food.

Set a certain timer to give start feeding.
reducing the amount of fish food by 3grams out of 50 grams.

The food weight displayed is 47
grams

Were as expected

Automation Light
The system's automated lighting feature was tested by programming the relay to turn the lighton for specific durations, as shown in Table 5. The light was successfully turned on for the designateddurations, ranging from 50 seconds to 1 hour, demonstrating the reliability of the system's automatedlighting control.
Table 5
Testing Light Automation
Activities
Set a certain timer to light up for 50
seconds.

Expected Result
Light will light up for 50 seconds.

Actual results
Were as expected

Set a certain timer to light up for 60
seconds.
Set a certain timer to light up for 1 hour.

Light will light up for 60 seconds.

Were as expected

Light will light up for 1 hour.

Were as expected

User Interface
The system's user interface was designed for straightforward navigation and easy access. Realtime sensor readings, including temperature, pH value, and the remaining quantity of food, aredisplayed on both the Blynk mobile application, as shown in Figure 4, and the Blynk website, as shownin Figure 5.
Figure 4
Blynk Mobile Application

Figure 5
Blynk Website

Additionally, the interface provides indicators for the feeding and lighting status of theaquascape. To ensure seamless monitoring, the system not only empowers aquascape owners tosupervise their setup locally via an OLED display but also remotely through the Blynk application.
This feature greatly enhances the convenience of managing the aquascape, allowing owners to keeptabs on their setup from any location with internet access.

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CONCLUSION AND FUTURE WORKThe successful deployment of a smart IoT-based aquascape monitoring system signifies a notable
convergence of technology, science, and aesthetics, streamlining the upkeep of thriving aquascapes.
The system provides an optimal environment for aquatic life, equipped with sensors to monitor water
acidity, temperature, and remaining fish food. Key features include automated feeding and lighting
mechanisms operating on pre-set schedules, eliminating manual intervention. The user-friendly
interface displays real-time sensor data, and its integration with the Blynk app allows remote control
and monitoring, enhancing user experience. This innovation represents a significant advancement inaquascaping and broader aquaculture applications. Future work includes expanding usage tocommercial enterprises and incorporating machine learning for predictive maintenance.

REFERENCES
Akhter, F., Siddiquei, H. R., Alahi, M. E. E., & Mukhopadhyay, S. C. (2021). Recent Advancement of the Sensors
for Monitoring the Water Quality Parameters in Smart Fisheries Farming. Computers, 10(3), 26.
https://doi.org/10.3390/computers10030026
Dilli Kumar V, Eshwar M, Manova Sam M, & Lavanya R. (2023). Blynk based aquaculture monitoring systemusing IOT. World Journal of Advanced Engineering Technology and Sciences, 8(1), 262–269.
https://doi.org/10.30574/wjaets.2023.8.1.0039
Hetami, A. A., Aransyah, M. F., & Andreana, A. G. (2023). Portrait of the aquascape industry in Indonesia:
Business opportunities and challenges. AACL Bioflux, 16(1).
Hutabarat, D. P., Susanto, R., Prasetya, B., Linando, B., & Senanayake, S. M. N. (2022). Smart system for
maintaining aquascape environment using internet of things based light and temperature controller.
International Journal of Electrical and Computer Engineering (IJECE), 12(1), 896.
https://doi.org/10.11591/ijece.v12i1.pp896-902
Khudoyberdiev, A., Abdul Jaleel, M., Ullah, I., & Kim, D. (2023). Enhanced Water Quality Control Based on
Predictive Optimization for Smart Fish Farming. Computers, Materials & Continua, 75(3), 5471–5499.
https://doi.org/10.32604/cmc.2023.036898
Petkovski, A., Ajdari, J., & Zenuni, X. (2021). IoT-based Solutions in Aquaculture: A Systematic Literature
Review. 2021 44th International Convention on Information, Communication and Electronic Technology(MIPRO), 1358–1363. https://doi.org/10.23919/MIPRO52101.2021.9597005
Rastegari, H., Nadi, F., Lam, S. S., Ikhwanuddin, M., Kasan, N. A., Rahmat, R. F., & Mahari, W. A. W. (2023).
Internet of Things in aquaculture: A review of the challenges and potential solutions based on current andfuture trends. Smart Agricultural Technology, 4, 100187. https://doi.org/10.1016/j.atech.2023.100187
Zhang, H., & Gui, F. (2023). The Application and Research of New Digital Technology in Marine Aquaculture.
Journal of Marine Science and Engineering, 11(2), 401. https://doi.org/10.3390/jmse11020401

42e-proceedings book ICoBTA-IR4.0

Preliminary Results of a Deep Learning Model

Preliminary Results of a Deep Learning Model for Classifying
Watermelon Sweetness Through Field-Spot Detection
Maya Fitria1, Muhammad Hafez Al-Assad2, Yudi Candra3, Silvia Roza4, and Rahmad Dawood5*
1,2,3,4,5

Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia
1,5
Telematics Research Center, Indonesia

1

mayafitria@usk.ac.id; 2m.hafez@mhs.unsyiah.ac.id; 3yudi.c@mhs.unsyiah.ac.id; 4silviaroza@mhs.unsyiah.ac.id;
5rahmad.dawood@usk.ac.id*Corresponding Author

ABSTRACT
Watermelon is a popular fruit commodity due to its pleasant and refreshing taste. A ripe and sweet
watermelon is recognised using human senses by inspecting its weight, the sound produced by tapping its
bottom, and its visual characteristics, such as the field spot and colour. A field spot on a watermelon
indicates that the fruit has had sufficient time to mature while on the vine. However, these traditional
methods are considered subjective and less efficient because disparities exist in evaluating each specimen.
Thus, this study aims to build a deep-learning model to classify the ripeness and sweetness of watermelons
based on field-spot appearance. Four convolutional neural networks, EfficientNet B4, EfficientNet B7,
ResNet-50, and ShuffleNet V2, were implemented in this study. This study also proposes a novel dataset ofwatermelons as a baseline for ripeness and sweetness classification to implement a deep-learning model.
The results reveal that EfficientNet B4 outperformed other architectures with an accuracy of 92.22%.
Under the testing procedure, the confusion matrices demonstrate that EfficientNet B4ss produced the best
performance among the algorithms.
Keywords: Field spot; watermelon sweetness level; deep learning, melon dataset; classification model

INTRODUCTION
Belonging to the Cucurbitaceae family, watermelon (Citrulus lanatus) is one of the most crucial seasonalhorticultural commodities cultivated worldwide (Saediman et al., 2020). These exotic fruits are grown intropical and subtropical areas. Watermelon contains high nutrients and is a good source of vitamins suchas B, C, E, calcium, iron, and zinc, which are beneficial for human health (Theurkar et al., 2023). Several
studies have demonstrated that watermelon is medicinal because it effectively decreases blood pressure,cholesterol, cancer, diabetes, cardiovascular disorders, and obesity (Lum et al., 2019; Dube et al., 2021;
Karimi et al., 2023).
Watermelon also has commercial and economic value, contributing to an annual production of over
100 million tons (Bantis et al., 2019; Helilusiatiningsih, 2022). It holds a broad market share, ranging fromtraditional to modern markets. The Food and Agricultural Organization of the United Nations reported thata total area of 3.2 million hectares was used for watermelon cultivation, resulting in a global production of
103 million tons in 2018 (Manivannan et al., 2020). Over 80% of global watermelon production originatesfrom Asia (Dube et al., 2021). The refreshing taste, high water content, and attractive red, yellow, and pinkcolours increase watermelon consumption. From 2014 to 2018, watermelon consumption in Indonesia

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increased by 6.14% (Isharthani et al., 2020), indicating that the public widely consumes watermelon andthat the market is highly competitive. The watermelon must meet domestic and international market
standards to enhance this competitiveness and be accepted widely by consumers, commonly indicated byits sweetness.
Based on interviews conducted at various fruit markets in Banda Aceh, within the watermelon
agrotourism of Aceh Besar, and at the UPTD Balai Benih Hortikultura, Tanaman Pangan, and Tanaman
Perkebunan in Banda Aceh, the ripeness and sweetness of watermelons are assessed through a visual
examination of their field-spot appearance and weight and by tapping them to gauge the sound.
Nevertheless, this visual assessment of watermelon ripeness is prone to subjectivity due to the variationsin interpretation among individuals.
Furthermore, the traditional method of categorising watermelon quality is not standardised.
According to the Indonesian National Standard, watermelon sweetness is indicated by the total dissolved
solids in the fruit flesh, called the Brix value or degree Brix, which is >8% (Badan Standarisasi Indonesia,2009). However, measuring the Brix value in the fruit requires the flesh to be sliced, making this processinvasive and potentially damaging (Jaywant et al., 2022).
Many existing studies have been conducted to identify the ripeness and sweetness of various fruits,
such as melon, apple, strawberry, pineapple, and watermelon. In 2019, Jie et al. developed a watermelon
ripeness detection method with visible and near-infrared images of watermelon as the object. They
demonstrated an analysis method for spectral characteristics in detecting ripening watermelons. The results
revealed that the corrected intensity ratio between peak1 and peak2 has a sophisticated predictive ability
with a classification-level accuracy of 88.1%. In 2020, Albert-Weiß proposed an acoustic resonance-basedmodel combined with a deep learning model for classifying ripe and overripe watermelons. This study
demonstrates that this classification model trained on acoustic information achieves an impressive
accuracy of 96%. In 2020, Nazulan et al. also investigated sweetness detection and classification using amachine-learning algorithm. The watermelon's colour, field spot, and shape were detected using the Kmeans clustering method with 84.62% accuracy. However, the dataset used in their work is considered too
small, at only 15 images.
By adopting the most recent methods and technology, more studies must be conducted on quality
detection regarding the ripeness and sweetness of agricultural commodities, such as watermelons. This
study proposes a novel approach to assess the sweetness of watermelons based on their field spots byemploying deep learning techniques to recognise the potency of deep learning in data-driven applications
and computer vision tasks (Ganatra et al., 2021). The watermelons are grouped into two classes: sweet andnonsweet watermelons. The Jamaican F1 watermelon variety was chosen for this purpose, as this variety
is the predominant watermelon cultivated in Indonesia. The study employs three distinct deep learning
architectures: EfficientNet B4, EfficientNet B7, ResNet-50, and ShuffleNet V2.
EfficientNet is an architecture with exceptional performance, attaining the top-1 position inrecognising images within the ImageNet dataset (Tan et al., 2019). In addition, ResNet-50 is a distinctive
and widely renowned architecture for image classification, demonstrating impressive proficiency in patternrecognition (Theckedath et al., 2020; Tian et al., 2019). Moreover, ShuffleNet is an architecture designed
for computational efficiency, achieved through point group convolution techniques and channelrandomisation, reducing the required computational operations. Therefore, ShuffleNet delivers strong
performance on a smaller scale (Jin et al., 2022).

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METHODOLOGY
The procedure of this work consists of four stages: data collection, preprocessing, model development, andmodel verification, as illustrated in Figure 1.
Figure 1
Research procedure for watermelon field-spot detection.

Characteristics of a Ripe Watermelon
According to the field findings from the interviews, there are many ways to determine the ripenessof a watermelon. One is tapping the watermelon’s lower part and listening to the sound. A watermelon
with a deeper sound is a ripe watermelon. Another method is determining the watermelon’s weight (Kato,
1997). The last is by examining the field spot of the watermelon. A watermelon with a larger field spotwith a yellowish colour indicates that it has had enough growing period as long as it is on the vine. In
contrast, the watermelon with a smaller spot or a white colour indicates that the watermelon is not ripe,
as presented in
Figure 2. This work focuses on determining the sweetness and ripeness of watermelon based on its
field spot. Moreover, a sensory evaluation was conducted on the watermelon to provide the classification
guidelines for its sweetness level, and the sweetness standards emerged based on the Indonesian National
Standard using the Brix value. A sweet watermelon has a Brix level of >8%, and the nonsweet class has
a Brix value of ≤8% (Badan Standarisasi Indonesia, 2009).
Figure 2
Field-spot area of a watermelon: sweet watermelon (left) and nonsweet watermelon (right).

Data Collection Setup
The images were taken shortly after watermelon harvesting. The dataset was gathered under naturallighting from the sun. The images were taken at a distance of 30 cm between the lens and the watermelon
using a Canon EOS M10 with a focal length of 16 and an aperture of f/3.5. Six sides of the watermelon

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were captured: front, back, right, left, top, and bottom. The image dimensions are 5184x3456 pixels.
Figure 3 depicts the image-gathering setup for the watermelon dataset. The original collected datacomprises 150 images.
Figure 3
Setup for gathering data for the dataset.

Data Preprocessing
After all data were collected, the preprocessing stage was conducted. The stage was divided into
four processes: image labelling and resizing, background removal, oversampling, and image augmentation.
The preprocessing started with image labelling. Each image was labelled as either sweet or nonsweet,
based on the Brix value of the melon measured using a refractometer after the images were taken. The
labelling process resulted in 90 sweet watermelons and 60 nonsweet watermelons. Subsequently, each
image was rescaled to a fixed dimension of 244x244 pixels.
Moreover, the background of the resized images was removed using the Python library rembg. The
tools employ a machine learning algorithm trained on an extensive collection of images, enabling precise
detection and distinction of the foreground object from the background (Fan et al., 2022). “Rembg” is anabbreviation for “remove background,” describing its primary functionality. The library uses the U-2-Net
framework, a deep-learning model for salient object detection. It leverages the power of a convolutional
neural network (CNN) to analyse image pixels and categorise them as either foreground or background(Liang, 2022). Figure 4 presents an image whose background was removed using rembg.
Figure 4
Background removal example using rembg.

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Preliminary Results of a Deep Learning Model

Image augmentation was performed to resolve the unbalanced data in each class. The process was
conducted in each class to increase the number of data to vary the data. The augmentation process was
performed by implementing the Python library imgaug. The diversity of the dataset can effectively be
increased by using imaging, leading to better model performance and improved generalisation of unseen
data (Garg et al., 2020). This library provides several augmentation techniques, such as flipping, rotating
Gaussian blur, and random translation. The augmentation techniques used horizontal flipping, vertical
flipping, 15o rotation, random translation, and Gaussian blur (Figure 5). Table 1 lists the total of the augmented dataset, which is 900 images divided into 450
data points for each class.
Table 1
Collected Dataset and After Preprocessing
Class

Original Data

Augmented Data

Total Data

Sweet

90

360

450

Nonsweet

60

390

450

Total

150

750

900

Figure 5
Augmented image example: (a) original data, (b) flipping horizontal, (c) flipping vertical, (d) 15o rotation, (e) randomtranslation, and (f) Gaussian blur.

(a)

(b)

(c)

(d)

(e)

(f)

After the datasets are augmented, they are split into training, validation, and testing data at a ratioof 7:2:1 for both classes, sweet and nonsweet watermelon. The training data are employed to train the
deep learning models, whereas the validation data validates classification performance during the training
process for overfitting prevention. The testing data aim to evaluate the classification performance of thechosen models. Table 2 exhibits the distribution of datasets used for the model development andverification stages.
Table 2Distribution of the Augmented Watermelon Dataset
Dataset

Sweet

Not Sweet

Total

Training

315

315

630

Validation

90

90

180

Testing

45

45

90

Total

450

450

900

Model Development
The second stage is model development, divided into training and validation steps. The training
model was performed on 630 images, comprising 315 images for each category, sweet and nonsweet
watermelons. Model hyperparameters were adjusted to determine the best combination for optimal

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performance. Table 3 lists the parameter set. In this case, the same optimiser, number of epochs, and batch
size were used as parameters. The fine-tuning learning rates were set to 10-3, 10-4, and 10-5 to examine
how quickly the model adapted to the problem. A too-low learning rate value could lead to a lengthy
training process, whereas a too-large learning rate value could result in an unstable training process(Basodi et al., 2020). The validation process was performed to validate model performance during neuralnetwork training and prevent overfitting (Febriana et al., 2022). The process was conducted on 180 data
(Table 2).
Table 3
Parameter Set for Watermelon Field-Spot Detection
Parameters

EfficientNet B4

EfficientNet B7

ResNet-50

ShuffleNet V2

Optimizer

Adam

Adam

Adam

Adam

Epoch

200

200

200

200

Batch Size

16

16

16

16

Learning Rate

10-3

10-3

10-3

10-3

10-4

10-4

10-4

10-4

10-5

10-5

10-5

10-5

Model Verification
This stage was conducted on the testing dataset and aims to verify the trained model obtained in theprevious process. The number of images is 90 for sweet and nonsweet watermelon. This stage also
examines model performance regarding accuracy, precision, recall, and F1-score using the confusion
matrix. The matrices also provide prediction scores for sweet and nonsweet watermelon.

RESULTS AND DISCUSSION
Table 4 presents the training results for the four architectures: EfficientNet B4, EfficientNet B7, ResNet50, and ShuffleNet V2, employing three hyperparameter values, 10-3, 10-4, and 10-5. The data in the tablereveal that the EfficientNet B4 model with a learning rate of 10-3 produced the highest accuracy of 96.03%
and training loss of 35.48%. As the learning rate diminished to 10-4 and 10-5, the training accuracy of the
EfficientNet B4 model also decreased, followed by an increased training loss. A similar pattern emergedwith EfficientNet B7, ResNet-50, and ShuffleNet V2, where setting the learning rate to a lower value
produced greater training accuracy. Nevertheless, as the learning rate decreased further, a decline intraining accuracy and a rise in training loss ensued. However, EfficientNet B7 demonstrated distinct
behaviour, where reducing the learning rate to 10-5 led to a significant decline in training accuracy from93.32% to 74.3% and an escalation in training loss from 39.14% to 66.98%. Moreover, EfficientNet B4
outperformed the other algorithms, followed by EfficientNet B7. Despite its different behaviour,
ShuffleNet V2 obtained superior training accuracy compared to ResNet-50. Notably, the learning rate of
10-3 displayed inferior results compared to those of 10-4 and 10-5.
Figure 6, Figure 7,
Figure 8, and48

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Preliminary Results of a Deep Learning Model

Figure 9 illustrate the graphical representation of the model performance on both the training andvalidation datasets. The learning curves are presented for each model with a learning rate of 10-3, 10-4, and10-5 in each figure in parts (a), (b), and (c), respectively. As observed in the figures (left), each architecture's
training and validation accuracy increases progressively with each epoch, indicating that the models arelearning the pattern gradually in the training data. However, using a learning rate of 10-5 in EfficientNet
B7 led to underfitting, as the validation accuracy is higher than the training accuracy between epochs 25
and 100. Underfitting also occurred in EfficientNet B4 and ShuffleNet V2 when both architectures set thelearning hyperparameter to 10-5.

With a learning rate of 10-3, the training and validation loss for EfficientNet B4 and B7 decreased(Figures 6 and 7), leading to convergence. However, a small gap exists between the training and validation
loss for these models. A decline in convergence was produced for EfficientNet B4 and B7 by setting thelearning rate to 10-4, although small fluctuations appear in the curves. Tuning the hyperparameter to alower value at 10-5 presents a slower decline, differing from the more significant decreases in the curves
for the learning rates of 10-3 and 10-4, which start early and progress steadily.
Table 4
Results of Each Architecture
Architectures

Learning Rate

EfficientNet B4

EfficientNet B7

ResNet-50

10-3Training
Accuracy
0,9603Training
Loss0,354810-4

0,9231

0,3989-5

100,84590,506710-3

0,95360,3602-4

10

0,93320,391410-50.74300.6698-3

100.91700.395110-4

0.88780.4305-50.82580.5130-3

100.89870.305410-40.84450.5296-50.77010.6688

10
ShuffleNet V2

10

For ResNet-50 in
Figure 8, training and validation loss run parallelly, approaching convergence. Regarding training
and validation accuracy and loss, a substantial fluctuation occurs in ResNet-50 using learning rates of 103
and 10-4. A lower learning rate improves training and validation accuracy in the EfficientNet B4 and B7
models.
The curve produced using a learning rate of 10-3 inFigure 9
Learning curves of the ShuffleNet V2 model(a) experiences a significant decrease starting from epoch 0 anddecreasing gradually closer to convergence. In contrast, tuning the learning rate to a lower value influenced
the model performance for ShuffleNet V2. The training and validation loss tend to decline with learning
rates of 10-4 and 10-5; however, convergence was not achieved by epoch 200, implying a challenge for the49Preliminary Results of a Deep Learning Modele-proceedings book ICoBTA-IR4.0

model to learn unseen data effectively. In summary, the learning curves reveal that a learning rate of 10-4
demonstrates the most favourable performance on the training and validation data, particularly for
EfficientNet B7.
Figure 10 presents the confusion matrices of the models using EfficientNet B4, EfficientNet B7,
ResNet-50, and ShuffleNet V2 testing with a learning rate of 10-4 on 90 watermelon images. In the first
confusion matrix (a), the EfficientNet B4 model yields the best prediction. The model correctly predicted
45.6% of the sweet and 46.7% of the nonsweet watermelon images. Only seven images were classified in
the incorrect class. The second-best prediction was yielded by EfficientNet B7, where 40 sweet images
were accurately predicted, and eight sweet watermelon images were incorrectly detected as nonsweet
watermelons. The ResNet-50 model also demonstrates an increasingly incorrect prediction compared to
EfficientNet B7, where six images were misidentified as the sweet class, and eight were incorrectly
identified as a nonsweet class. In contrast, the confusion matrix of the ShuffleNet V2 model represents anincreased number of inaccurate predictions of both classes, with 5% and 8.9% incorrect predictions for the
sweet and nonsweet classes, respectively.

-4

Table 5 exhibits the testing outcomes of the models employing the hyperparameter value of 10 . As

the table indicates, these four models have achieved high accuracy, precision, recall, and F1 scores. The
highest accuracy was achieved by EfficientNet B4, indicating that the model performed exceptionally wellin predicting watermelon sweetness. The recall for this model also reached a value of 93.33%, indicating
the ability of the model to capture all instances of the positive class (sweet/ripened watermelon). Good
performance was also achieved by EfficientNet B4, ResNet-50, and ShuffleNet V2, with accuracy in therange of 81.11% to 85.56%.
Figure 6
Learning curves of the EfficientNet B4 model.

(a) Learning rate: 10-350e-proceedings book ICoBTA-IR4.0

Preliminary Results of a Deep Learning Model(b) Learning rate: 10-4(c) Learning rate: 10-5
Figure 7
Learning curves of the EfficientNet B7 model.

(a) Learning rate: 10-351Preliminary Results of a Deep Learning Modele-proceedings book ICoBTA-IR4.0(b) Learning rate: 10-4(c) Learning rate: 10-5

Figure 8
Learning curves of the ResNet-50 model.

(a) Learning rate: 10-352e-proceedings book ICoBTA-IR4.0

Preliminary Results of a Deep Learning Model(b) Learning rate: 10-4(c) Learning rate: 10-5

Figure 9
Learning curves of the ShuffleNet V2 model.

53Preliminary Results of a Deep Learning Modele-proceedings book ICoBTA-IR4.0(a) Learning rate: 10-3(b) Learning rate: 10-4(c) Learning rate: 10-5
Table 5Results for Each Architecture with a Learning Rate of 10-4
Architecture

Accuracy

Precision

Recall

F1-Score

EfficientNet B40,92220,91300,93330,9231

EfficientNet B70,8556

0,88100,82220,8506

ResNet-50

0,83330,84090,82220,8315

ShuffleNet V20,8111

0,81810,80,8090Figure 10
Confusion matrices for each architecture, employing a learning rate of 10-4.

54e-proceedings book ICoBTA-IR4.0

Preliminary Results of a Deep Learning Model(a) EfficientNet B4(b) EfficientNet B7(c) ResNet-50(d) ShuffleNet V2

CONCLUSION
This study proposed a novel deep-learning model for detecting the sweetness of watermelon based on thephysical appearance of the field spot. The degree of sweetness is categorised into two groups: sweet andnonsweet. A preliminary evaluation was conducted using four robust CNN frameworks: EfficientNet B4,
EfficientNet B7, ResNet-50, and ShuffleNet V2. The evaluation revealed that the EfficientNet B4 model
outperformed the other algorithms with an accuracy of 92.22%. This outcome is also supported by the
confusion matrix results, indicating that EfficientNet B7 predicts results with a minimal error of 7.7%.
Nevertheless, further experiments should be conducted by tuning the hyperparameters, such as the batch
size, learning rates, number of epochs, and optimiser, to determine the best combination for each algorithm.
Furthermore, transfer learning and pretrained model experiments should be conducted to leverage existingknowledge and achieve better performance.

ACKNOWLEDGMENT
This work was supported by the Keio University and APNIC Foundation under the CBR grant scheme in
2022.

REFERENCES
Albert-Weiß, D., Hajdini, E., Heinrich, M., & Osman, A. (2020). Cnn for ripeness classfication of watermelon fruits
based on acoustic testing. In Proceedings of the Virtual 3rd International Symposium on Structural Health
Monitoring and Nondestructive Testing (SHM-NDT 2020), Quebeq, QU, Canada (pp. 25-26).

55

Preliminary Results of a Deep Learning Modele-proceedings book ICoBTA-IR4.0

Badan Standarisasi Indonesia. (2009). SNI 7420:2009.
Bantis, F., Koukounaras, A., Siomos, A. S., Radoglou, K., & Dangitsis, C. (2019). Optimal LED wavelengthcomposition for the production of high-quality watermelon and interspecific squash seedlings used forgrafting. Agronomy, 9(12), 870.
Basodi, S., Ji, C., Zhang, H., & Pan, Y. (2020). Gradient amplification: An efficient way to train deep neuralnetworks. Big Data Mining and Analytics, 3(3), 196-207.
Dube, J., Ddamulira, G., & Maphosa, M. (2021). Watermelon production in Africa: challenges andopportunities. International Journal of Vegetable Science, 27(3), 211-219.
Fan, M., Lu, W., Niu, W., Peng, X., & Yang, Z. (2022). A Large-Scale Invariant Matching Method Based on
DeepSpace-ScaleNet for Small Celestial Body Exploration. Remote Sensing, 14(24), 6339.
Febriana, A., Muchtar, K., Dawood, R., & Lin, C. Y. (2022). USK-COFFEE Dataset: A Multi-class Green Arabica
Coffee Bean Dataset for Deep Learning. In 2022 IEEE International Conference on Cybernetics andComputational Intelligence (CyberneticsCom) (pp. 469-473).
Ganatra, N., & Patel, A. (2021). Deep learning methods and applications for precision agriculture. Machine Learning
for Predictive Analysis: Proceedings of ICTIS 2020, 515-527.
Garg, S., & Garg, S. (2020). Prediction of lung and colon cancer through analysis of histopathological images byutilizing Pre-trained CNN models with visualization of class activation and saliency maps. In Proceedings of the2020 3rd Artificial Intelligence and Cloud Computing Conference (pp. 38-45).
Helilusiatiningsih, N., & Utomo, P. S. (2022). Post Harvest and Technology of Watermelon (Citrullus lanatus) Plants
in Integrated Field Laboratory Faculty of Agriculture Kediri. Formosa Journal of Applied Sciences, 1(6), 10411050.
Ishartani, D., Sari, A. M., & Arifani, R. (2020). Partial Characterization of Watermelon Albedo Pectin Extracted
Using Citric Acid Combined with Microwave Assisted Extraction. In IOP Conference Series: Earth andEnvironmental Science (Vol. 518, No. 1, p. 012060).
Jaywant, S. A., Singh, H., & Arif, K. M. (2022). Sensors and instruments for brix measurement: A
review. Sensors, 22(6), 2290.
Jie, D., Zhou, W., & Wei, X. (2019). Nondestructive detection of maturity of watermelon by spectral characteristic
using NIR diffuse transmittance technique. Scientia Horticulturae, 257, 108718.
Jin, X., Bagavathiannan, M., Maity, A., Chen, Y., & Yu, J. (2022). Deep learning for detecting herbicide weed control
spectrum in turfgrass. Plant Methods, 18(1), 94.
Kato, K. (1997). Electrical density sorting and estimation of soluble solids content of watermelon. Journal of
Agricultural Engineering Research, 67(2), 161-170.
Karimi, E., Abaj, F., Gholizadeh, M., Asbaghi, O., Amini, M., Ghaedi, E., & Hadi, A. (2023). Watermelon
consumption decreases risk factors of cardiovascular diseases: A systematic review and meta-analysis ofrandomized controlled trials. Diabetes Research and Clinical Practice, 202, 110801.
Liang, J. (2022). The research of background removal applied to fashion data: The necessity analysis of backgroundremoval for fashion data.

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Preliminary Results of a Deep Learning Model

Lum, T., Connolly, M., Marx, A., Beidler, J., Hooshmand, S., Kern, M., & Hong, M. Y. (2019). Effects of fresh
watermelon consumption on the acute satiety response and cardiometabolic risk factors in overweight and obese
adults. Nutrients, 11(3), 595.
Manivannan, A., Lee, E. S., Han, K., Lee, H. E., & Kim, D. S. (2020). Versatile nutraceutical potentials ofwatermelon—A modest fruit loaded with pharmaceutically valuable phytochemicals. Molecules, 25(22), 5258.
Nazulan, W. N. S. W., Asnawi, A. L., Ramli, H. A. M., Jusoh, A. Z., Ibrahim, S. N., & Azmin, N. F. M. (2020).
Detection of sweetness level for fruits (watermelon) with machine learning. In 2020 IEEE Conference on Big
Data and Analytics (ICBDA) (pp. 79-83).
Saediman, H., Alwi, L. O., Rianse, I. S., Taridala, S. A. A., Salahuddin, S., Indarsyih, Y., & Astuti, R. W. (2020).
Comparative profitability of melon and watermelon production in South Konawe District of Southeast
Sulawesi. WSEAS Trans. Bus. Econ, 17, 933-939.
Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International
conference on machine learning, 97, 6105-6114.
Theckedath, D., & Sedamkar, R. R. (2020). Detecting affect states using VGG16, ResNet50 and SE-ResNet50
networks. SN Computer Science, 1, 79.
Theurkar, S. V., Gawari, D. P., & Birhade, D. N. (2023). Watermelon as a potential nutritional horticultural crop: A
comprehensive review. World Journal of Advanced Research and Reviews, 18(2), 627-629.
Tian, X., & Chen, C. (2019). Modulation pattern recognition based on Resnet50 neural network. In 2019 IEEE 2ndInternational Conference on Information Communication and Signal Processing (ICICSP) (pp. 34-38).

57

LEEAF: Olive Leaf Infection Detection Using Artificial Intelligence

e-proceedings book ICoBTA-IR4.0

LEEAF: Olive Leaf Infection Detection Using Artificial Intelligence
Themistoklis Sarantakos1, Daniel Mauricio Jimenez Gutierrez 2, Dimitrios Amaxilatis1, and Ioannis
Chatzigiannakis2
2
1

1
Spark Works Ltd, Galway, Ireland
Sapienza University of Rome, Rome, Italy{tsaradakos,d.amaxilatis}@sparkworks.net; 2{jimenezgutierrez,ichatz}@diag.uniroma1.it

ABSTRACT
Olive trees, which civilisations have cultivated for thousands of years, offer a compelling case studyof disease diagnosis on plant leaves. These venerable trees are susceptible to various diseases, but
diagnosing them is far from straightforward. The symptoms of diseases affecting olive leaves canvary extensively between olive trees and even within individual leaves on the same tree. This inherent
complexity, combined with the olive groves' vulnerability to pathogens like aculus olearius and olive
peacock spot, has impeded the development of robust and effective disease detection algorithms. The
LEEAF framework has been developed to confront this multifaceted challenge head-on. Our
approach amalgamates cutting-edge deep learning techniques, drawing upon convolutional neuralnetwork (CNN) models and vision transformer models while harnessing the power of cloudcomputing through AWS Recognition to create a system that can accurately detect and classify
diseases commonly affecting olive leaves. LEEAF is structured in a three-tiered fashion, comprising
the Cloud, Edge, and Far Edge layers that work in unison to share information and data, including
but not limited to olive tree images, weather forecasts, and ML predictions. Each layer possesses
distinct capabilities in processing power, data storage resources, and power reserves, enabling themto excel at different process stages. This holistic approach empowers LEEAF to identify trees
suffering from infection or infestation accurately. Meticulously labelled datasets that encompassed
over 2000 instances of olive leaves in various conditions were used to experiment with different ML
models and operations. Our multi-tiered approach offered scalability and efficient infrastructure
usage, yielding highly promising results. The models developed by LEEAF demonstrated remarkableaccuracy, achieving approximately 99.6% for multiclass classification, showing the potential of deeplearning models in tackling the intricate and nuanced challenges presented by olive leaf disease
detection.

Keywords: UAVs, machine learning; olive pests and diseases; diagnosis, smart farming
INTRODUCTION
In the ever-evolving landscape of agricultural research, applying deep learning techniques for disease
detection has emerged as a prominent and imperative area of study, particularly in recent years. This
surge in interest is driven by the potential for deep learning methods to revolutionise how we identify
and mitigate plant diseases (Cruz et al., 2017). Despite the significant progress made in this field, it is
not without its challenges, chief among them being the vast diversity of plant species and theirunique regional characteristics. Traditional agriculture is being transformed by the Fourth Industrial
Revolution, with Smart Farming and IoT solutions revolutionising the sector (Kalyani & Collier,2021; O'Grady et al., 2019). Plant diseases, like Bactrocera Oleae and Aculus olearius, pose asignificant challenge, causing substantial yield losses. LEEAF addresses these using UAVs andadvanced technology to detect and diagnose olive leaf pests and diseases, enabling timely andsustainable treatments. UAVs effectively capture crop and leaf images, monitor crop health, andboost agricultural productivity (Uddin et al., 2021). End-user applications simplify UAV device
control and image review, providing easy access to vital information. In the context of LEEAF, weleverage cutting-edge technology to empower farmers with insights for efficient olive grove58

e-proceedings book ICoBTA-IR4.0

LEEAF: Olive Leaf Infection Detection Using Artificial Intelligenceinspection, regardless of the grove's size or location, marking a significant advancement inagriculture.
State-of-the-art ML technologies like Transfer Learning (TL) are used to develop LEEAF.
TL involves leveraging knowledge from one pre-trained model to improve performance on related
tasks. We can achieve impressive results by utilising pre-trained models as a starting point and finetuning specific parts for the new task, especially when the available data is limited. Additionally, on
top of the use of TL, other technologies can be useful for exploring the domain of leaf classificationand disease identification. Convolutional Neural Networks (CNNs) are potent deep learning models
tailored for computer vision tasks (Harakannanavar et al., 2022). They excel at automaticallyextracting meaningful features from visual data, proving vital in numerous applications like image
classification, object detection, image segmentation, and image generation. Residual Neural Network(ResNet) is a highly regarded CNN architecture. It was originally trained on the ImageNet dataset,making it adept at learning intricate image representations. Its pre-trained layers can be used fortransfer learning, achieving remarkable performance with limited annotated data. Very Deep
Convolutional Network (VGG), another notable CNN architecture, also benefits from ImageNet
training. Leveraging its pre-trained weights and features extends its applicability to new tasks,
particularly in data-scarce scenarios. DenseNet121, a variant of DenseNet, features denseconnections between layers, enhancing feature reuse and gradient flow. This architecture performs
well with limited data, efficiently transferring knowledge. MobileNetV2 is tailored for resourceconstrained environments. It uses depth-wise separable convolutions to reduce computations andparameters while maintaining high accuracy, making it ideal for mobile and embedded devices. On
the domain of leaf segmentation in images containing more than one leaf, the “You Only Look
Once” (YOLO) framework stands out for its remarkable speed, bypassing intricate pipelines. It is an
excellent choice for real-time processing, simplifying the development of custom models.

METHODOLOGY AND RESULTS
LEEAF uses two base pillars for its AI operations: the leaf extraction and classification models(Alshammari et al., 2022). The first one receives the raw images collected and extracts from themeach leaf that can be identified. This operation uses a YOLO-based ML model that has proven easy
to train and reliable enough to use under any conditions. The second one uses an ML model based on
the ResNet50 or VGG19, which has proven to be among the best-performing models we haveidentified and the AWS Recognition cloud service.
Leaf Extraction
This YOLO-based ML model outputs a set of bounding boxes and segmentation masks that
describe the location of each detected object in the images provided as input. To evaluate how thedifferent sizes of YOLO base models can affect the performance of our system, we tested with all 5
YOLO base model sizes. We found that the medium ones guaranteed the best performance. This canbe attributed to many reasons, but we mainly believe that smaller models are not elaborate enough todifferentiate between leaves, tree branches, and ground vegetation. In comparison, larger models candifferentiate between more than one class (i.e., detecting people, cards, and bikes) instead. The scoreof each model is presented in Table 1. For the evaluation of each model, we used the same set of
input images we collected from our on-field experiments and labelled using the TrainYOLO online
tool.
Table 1
Scores on the leaf detection on the test dataset for the different YOLO-based models
Base Model Size

Nano

Small

MediumLarge

Extra Large

Precision

0.751

0.643

0.742

0.505

0.411

59

LEEAF: Olive Leaf Infection Detection Using Artificial Intelligence

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Leaf Classification
To classify the extracted leaves, we need to judge whether they are healthy or unhealthy andthe pathogen that has affected them. Our experiments tested only two pathogens, aculus orealus andpeacock spot. The images we used for training and evaluating our dataset were from the Olive Leaf
dataset available online. As mentioned, we focused on two ML models that have proven moreaccurate in the results: ResNet50 and VGG19. ResNet-50 is a deep neural network architecture
known for its excellent performance in image classification tasks (Ksibi et al., 2022). VGG19 is a 19layer-deep convolutional neural network that can, on its own, classify images into 1000 object
categories. These networks are re-trained to produce results in our application domain, classifying theprovided images into one of our three categories. Our cloud-based AWS Recognition tool wastrained and evaluated with the same dataset. Overall, we used 980 train and test images to evaluate
these models.
All three options performed quite well, reaching an accuracy score of over 98%, showing they
all can be considered equally capable. In more detail, the VGG19 achieved the lowest accuracy with
98,2%, followed by ResNet50 with 99.3%, and finally, Recognition with 99.6%. As we cannot
interfere with the training procedure of Recognition, we can assume that the generated model resultsfrom an optimisation done by the AWS backend to provide us with the best possible option. The
other two can be further fine-tuned by tweaking their training parameters to reach even better scores.
The main drawback of using AWS’s tool is that its execution needs to happen in the cloud andcannot be transferred to our hardware (e.g., an edge device or a mobile phone). This is important in
AI and ML applications to avoid sharing personal data and transferring large volumes of data fromthe on-site infrastructure to the cloud services (Kalyani & Collier, 2021). While ResNet50 and
VGG19 do not achieve the best performance possible, their use is preferred in Edge-Basedapplication environments.

CONCLUSION
The integration of computer vision technologies with drones offers an exciting avenue to transformthe identification of leaf infections in olive trees. By leveraging the capabilities of computer visionalgorithms and aerial monitoring, agricultural professionals and researchers can access valuable
information about the well-being of olive tree orchards. They can detect infections early on and enact
precise measures for efficient disease management. With ongoing technological advancements, thefuture promises substantial opportunities to enhance olive tree health and ensure the sustainability ofolive production.

REFERENCES
Alshammari, H., Gasmi, K., Ltaifa, I. B., Krichen, M., Ammar, L. B., & Mahmood, M. A. (2022). Olive
disease classification based on vision transformer and CNN models. Computational Intelligence and
Neuroscience, 2022.
Cruz, A. C., Luvisi, A., De Bellis, L., & Ampatzidis, Y. (2017). X-FIDO: An effective application for detecting
Olive quick decline syndrome with deep learning and data fusion. Frontiers in plant science, 8.
Harakannanavar, S. S., Rudagi, J. M., Puranikmath, V. I., Siddiqua, A., & Pramodhini, R. (2022). Plant leaf
disease detection using computer vision and machine learning algorithms. Global Transitions
Proceedings, 3(1), 305-310.
Kalyani, Y., & Collier, R. (2021). A systematic survey on the role of cloud, fog, and edge computing
combination in smart agriculture. Sensors, 21(17), 5922.

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LEEAF: Olive Leaf Infection Detection Using Artificial Intelligence

Ksibi, A., Ayadi, M., Soufiene, B. O., Jamjoom, M. M., & Ullah, Z. (2022). MobiRes-net: a hybrid deeplearning model for detecting and classifying olive leaf diseases. Applied Sciences, 12(20), 10278.
O'Grady, M. J., Langton, D., & O'Hare, G. M. P. (2019). Edge computing: A tractable model for smart
agriculture? Artificial Intelligence in Agriculture, 3, 42–51.
Uddin, M. A., Ayaz, M., Mansour, A., Aggoune, E. H. M., Sharif, Z., & Razzak, I. (2021). Cloud-connected
flying edge computing for smart agriculture. Peer-to-Peer Networking and Applications, 14(6), 3405-3415.

61

Image Detection Using YOLOv7 Algorithm to Detect the
Moulted Crabs
Mohammad Affaiq Aini1, Lee Beng Yong2, Yong Yueh Tiam3, Leong Siow Hoo4, Khong Heng Yen5*,
Firdaus Abdullah6, Sharifah Raina Manaf7, and Chua Tze Nan8
1

College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Sarawak Branch, Malaysia
5
Faculty of Applied Sciences, Universiti Teknologi MARA, Sarawak Branch, Malaysia
6
Faculty of Business and Management, Universiti Teknologi MARA, Selangor Branch, Malaysia
7
Faculty of Plantation and Agrotechnology, Universiti Teknologi MARA, Sarawak Branch, Malaysia
8
GO Aquaculture Sdn Bhd, Sarawak, Malaysia
khonghy@uitm.edu.my*Corresponding author

ABSTRACT
Edible crab commercial exploration is expanding quickly on a global scale. Crab production in
Malaysia from both aquaculture and fishing was about 16,074 tonnes last year. Soft-shell crab is oneof the most valuable marketing formats. To produce high-quality soft-shell crabs, they are harvested
by removing them from the water within 2-3 hours after they have been moulted to stop the shell fromhardening. Thus, local soft-shell crab farming could not further develop a large-scale production tomeet the market demands due to the dependence on labour-intensive and high mortality rate of crabs.
These expenditures increase the impact of any expansion of intense culturing. A harmless, quick, andautomatic approach to identifying moulting is a significant development in soft-shell crab farming andmarketing. This study investigates the pivotal role of image preprocessing using the YOLOv7 algorithm
to identify the moulted crabs in developing an IoT-embedded video and real-time monitoring system.
One hundred twenty images dataset of crabs in the containers were acquired using an Internet Protocol(IP) wireless camera at a crab farm. The dataset is labelled and split into an appropriate ratio, wherea significant percentage of the data was used for training the model, and the remaining was to validate
its performance. The result of the system shows an average Intersection over Union (IoU) of 53.75%,
average Precision of 96.67%, Recall of 50.41%, and mean Average Precision (mAP) of 62.50%. In
conclusion, the findings of this study validated that YoLov7 is an efficient algorithm with high accuracy
in monitoring the crab moulting processes for a soft-shell crab’ farm. The study is currently working
on improving its Recall and mAP.
Keywords: Soft shell crab; Image detection; YOLOv7, IoT; Real-time monitoring62

e-proceedings book ICoBTA-IR4.0

Measuring Leaf Area Using AutoCAD

Measuring Leaf Area Using AutoCAD
Seraphina ak Dominic1*, Margaret Chan Kit Yok2, and Siraj Munir bin Mohammad3
1

1

Sarawak State Famers’ Organisation, Kuching, Sarawak
2,3
Universiti Teknologi MARA Sarawak, Malaysia

seraphinadominic07@gmail.com; 2drmchan@uitm.edu.my; 3sirajmunirmohammad@gmail.com*Corresponding Author

ABSTRACTLeaf area can be determined by regression equitation, grid count method, gravimetric method,
planimeter or image processing-based algorithm method. In this paper, AutoCAD was applied tomeasure the leaf area of oil palm. The image of the leaf was acquired using a printer scanner andstored in JPEG format. The image was uploaded to the AutoCAD 2014 software. The actual length of
the biggest width of the leaf was measured first to be used for the reference length in AutoCAD. The
area of the image was automatically calculated by drawing the line along the leaf perimeter using
POLYLINE tools in the software based on the coordinates at the leaf perimeter principle. The meanleaf area of twenty sampled leaves of oil palm obtained was compared with the results of the Manual
Method using grid count and the Calculation Method based on estimates using the formula. The
mean value of the Calculation Method was shown with the highest leaf area of 1438.67 cm2, followedby the Manual Method and AutoCAD method with 1153.67 cm2 and 1141.00 cm2, respectively. The
three methods had no significant differences, as ANOVA indicated a p-value of 0.253. There wasonly 1.1 percent difference between the Manual Method and the AutoCAD Method compared to the24.7 percent difference between the Manual Method and the Calculation Method. The AutoCAD
Method was concluded to be more efficient and accurate in determining the leaf area of oil palm.
Keywords: AutoCAD; leaf area; oil palm

INTRODUCTION
In 2022, palm oil was the biggest contributor to the Malaysian gross domestic product (GDP), with a
4.9 percent share of the total 8.9 percent share in the agriculture sector in Malaysia (Statista, 2023).
As oil palm makes a significant impact on the Malaysian agriculture sector, it is important to enhance
the production of oil palm. Yield production is highly dependent on the growth performance of oil
palm.
The leaf area is an important part of the oil palm for identifying growth performance. It ishighly correlated with photosynthesis, transpiration, evapotranspiration, and inspecting pests anddiseases in that area (Marlon et al., 2011). According to Chaudhary et al. (2012), the measurement ofleaf area can be done by destructive and non-destructive methods. The destructive method requires
the removal of the leaf from the plant to be measured, while the non-destructive method is a direct
measurement without removing the leaf from the mother plant. The traditional methods for leaf area
measurement commonly used are the grid count method, hand-planimetry, gravimetric method, dot
counting, photoelectric planimetry, air flow, linear measurement of leaves, leaf weighing, detachedleaf counting and the rating method (Pandey & Hema, 2011). In the report of Chaudhary et al.
(2012), a simple, fast and accurate algorithm for leaf area measurement using image processing wasintroduced. Two approaches in oil palm can determine leaf area. based on manual or calculated
methods (Jonckheere et al., 2004). In the manual or planimetric method, leaf area, the leaf perimeter,

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can be measured with a planimeter, and its area is computed based on the correlation between
individual leaf area and the number of area units covered by that leaf in a horizontal plane. In thecalculated method, the leaflet is measured using the length and mid-width and estimated using theformula rla=2n x b where rla stands for mean area per leaf, N is the number of leaflets on one sideand b is the length x width (Corley & Breure, 1981).
AutoCAD is a commercial software application for 2D and 3D computer aid design (CAD)
and drafting, available since 1982 as a desktop application used for engineering and surveying work
(Cohn, 2010). With the aid of a digital camera to create an image, the application of AutoCAD wasdescribed by Tian et al. (2008) as a rapid and non-destructive method for measuring the leaf area oflandscape plants with a needle or small leaf. The limitation was that the same distance of the camera
had to be maintained to take the picture so that only one calibration was needed in AutoCAD. The
destructive method could resolve it by scanning the leave to capture the image with a scanning
printer. This has been done by Rico-Garcia and Hernandez (2009).
This paper describes the methodology used to prepare oil palm leaf for image acquisition,
image processing, and leaf area calculation using AutoCAD. Two other methods, the direct manual
method using grid count and the indirect method using a calculation based on a formula, were carriedout for validation.

METHODOLOGY
20 leaf samples from Frond 17 from six years’ oil palms were taken in the morning due to lowlevel sunlight exposure to the leaflet of oil palm. Frond 17 was identified as a fully open frond at theterminal shoot as the frond 1 counting downwards where on the left or right of the shoot dependingon the left-handed and right-handed harvesting pattern shown in Figure 1.
Figure 1
Graphical Representation of Oil Palm Spiral with Left-Handed Spiral (a) and Right-Handed Spiral (b) (Fairhust,
1998)

The Frond 17 was cut at the base and removed from the palm. At the point on the rachis where
the flat top changed to an angular shape, approximately two-thirds along the frond from the base was
located, and four adjacent leaflets from each side were selected and detached with a sharp knife. The
central one-third of each leaflet was then cut and placed in a clean, labelled polythene bag. Twenty

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Measuring Leaf Area Using AutoCAD

samples were used for leaf area determination using three methods, and the analysis of variance(ANOVA) of data was computed using IBM SPSS Statistic 21.
AutoCAD Method
The actual length of the biggest width of a sampled leaf was measured first to be used forcalibration for the reference length in AutoCAD for all sampled leaves. Each sampled leaf was cut
into segments and inserted in a transparency file to ensure the leaf would not get twisted or curled.
Then, the image of the plant leaf was acquired using a scanner and saved in JPEG format. It is
important to ensure that the picture was taken parallel to the surface where the leaf segments wereplaced to maintain a fixed distance for scale factors to be the same for all images. The scanned image
was then uploaded into the AutoCAD software version 2014, as shown in Figure 2. Figure 3 shows
measuring the leaf area of oil palm using the AutoCAD software.
Figure 2
Uploaded Image of the Scanned Oil Palm Leaf Saved in JPEG Format into the AutoCAD Software

Figure 3
Measuring Leaf Area of the Oil Palm in the AutoCAD Software

Once the image was in the workspace of the AutoCAD software, sized to its real scale, acontinuous line was plotted around the leave using the POLYLINE command to form a closed
object. The area of the object would be calculated automatically. The process is described in Table 1.

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Table 1
Steps of Measuring Leaf Area Using AutoCAD Version 2014
Steps

Description of action

Comm

Command
Start

End

1

Attaching the image in AutoCAD work

In ordinary way

2

Knowing the dimension before scaling the image

In ordinary way

3

Scaling the image to real scale

Scale

4

Selecting the image to specify scale factor
Specify the new length of the object based on theactual size of the biggest width of leaf
Ensure the length of leaf is same.

r

Dist

Enter

Drawing a close line around the leaves.
Knowing the area of the leaf image by selecting theline of the leaves.

Pline

Enter
Double
Enter

5
6
7
8

Enter
Enter

Enter
Enter

Area

Enter

The sampled leaf was cut into segments and inserted in a transparency file to ensure
the leaf would not get twisted or curled. A photocopied transparency grid of one cm x one
cm prepared from graph paper was placed over the leaf. The leaf area was manually
determined by counting the grids overlapping the leaf.
Calculation Method

The length and midpoint of the sampled leaf were measured using a measuring tape.
The leaf area was calculated based on equation (1).
A = 2n x b(1)
where n was the width of the midpoint of the leaflet on one side and b was the length.
RESULTS AND DISCUSSIONS
Table 2 shows the mean leaf area obtained from the three methods used for the same twenty sampled
leaves. The mean value of the Calculation Method was shown with the highest leaf area of 1438.67
cm2. It was followed by the Manual Method, which recorded the mean leaf area of 1153.67 cm2
while the AutoCAD method obtained 1141.00 cm2.
Table 2Comparison of Mean Leaf Area (cm2) from AutoCad, Manual and Calculation Methods
No
1
2
3

MethodAutoCAD
ManualCalculation
Total
ANOVA @ α = 0.05

Mean
1141.00
1153.67
1438.67

Standard Deviation
134.842219.246282.7971244.44240.427
p = .253

However, there were no significant differences among the three methods, as ANOVA
indicated a p-value of .253. There was only a 1.1 percent difference between the Manual Method andthe AutoCAD Method compared to the 24.7 percent difference between Manual Method and the
Calculation Method. The standard deviation of the AutoCAD method was shown to be the lowest,
while the Calculation Method was the highest. This would be expected as the Calculation Method
described by Corley and Breure (1981) was based on estimates and thus indicated that this method
would give an overestimated value of mean leaf area. The manual Method was accepted to be66

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Measuring Leaf Area Using AutoCAD

accurate but labour-intensive and has since been replaced by indirect methods like the Calculation
Methods. Or, most recently, digital methods using imaging analysis.
AutoCAD would be the method to provide the most accurate leaf area as the method had theability to trace the tooth area and other related features at the perimeter of the leaf. The findings ofHong and Jufen (2003) showed there were close relationships between the CAD image processing
method and the other traditional methods like the grid-counting method, leaf-copy and weighing
method and instrumental scanning method. Similarly, in the report of Tian et al. (2008), AutoCAD
provided a non-destructive method for measuring the leaf area of leaf captured by a digital camera. In
comparing the results with traditional methods that included the grid method, paper-cut method andinstrumental scanning method, the value measured was significantly correlated with those measured
by the traditional methods and thus concluded that the AutoCAD method was suitable for measuring
the leaf area of plants with needle leaf or small leaf. However, for big leaves like oil palm, it wasnecessary to employ a destructive method, thus allowing the capturing of the image by using ascanner, which overcame the limitation of digital camera image whereby there had to be a constantpicture of the camera to the intended measured leaf sample and ruler as an indicator for actual scale.
The method described, therefore, was found to be more efficient and accurate.

CONCLUSION
Leaf area measurement is important for ensuring the productivity of oil palm yield it describes afundamental property of the oil palm canopy related to the growth and metabolism of plants, as wellas the accumulation of dry matter and yield. The traditional Calculation Method was anoverestimated value. The Manual Method using grid lines overlapping the leaf was assumed to be an
accurate value. However, it was tedious and time-consuming. It was also based on some estimates ofgrid count at the irregular leaf edge. Therefore, it can be concluded that the AutoCAD method
provided a reliable method, and the process was very efficient, accurate and user-friendly inestimated leaf area.

ACKNOWLEDGEMENT
The authors would like to express their appreciation to Daitoku Sdn. Bhd. for funding the project.
This project was not possible without the permission of Madam Noria to conduct the study in her
smallholding oil palm plantation.

REFERENCES
Corley, R.H.V., & Breure, C.J. (1981). Measurements in oil palm experiments. Internal report.
London, Unilever Plantation Group.
Cohn, D. (2010). Evolution of computer-aided design. Desktop Engineering Digital Edition, (12).
Fairhurst, T. (1998). Nutrient deficiency symptoms and disorders in oil palm (Elaeis guineensis
Jacq.). Pocket Guide. PPI, Singapore.
Hong, C., & Jufen, L. (2003). Measurement of plant leaf area using CAD image processing
techiques. Gansu Nong ye da xue xue bao= Gansu Nongye Daxue Xuebao, 38(4), 467-470.
Jonckheere, I., Flecks, S., Nackaerts, K., Muysa, B., Coppin, P., Weiss, M., & Baret, F. (2004).
Review of methods of in-situ leaf area index determination Part I. Theories, sensors andhemispherical phtography. Agricultural and Forestry Meteorology, 121(1-2), 19-35.

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Marcon, M., Mariano, K., Braga, R. A., Paglis, C. M., Scalco, M. S., & Horgan, G. W. (2011).
Estimation of total leaf area in perennial plants using image analysis. Revista Brasileira deEngenharia Agrícola e Ambiental, 15, 96-101.
Pandey, S. K., & Hema, S. (2011). A simple, cost-effective method for leaf area estimation. Journalof Botany, 2011(2011), 1-6.
Chaudhary, P., Godara, S., Cheeran, A. N., & Chaudhari, A. K. (2012). Fast and accurate method forleaf area measurement. International Journal of Computer Application, 49(9), 22-25.
Rico-Garcia, E., Hernandez-Hernandez, F. Soto-Zarazua, G. M., & Hernandez-Ruiz, G. (2009). Two
new methods for the estimation of leaf area using digital photography. International Journal of
Agriculture and Biology, 11(4), 97-400.
Statista (2023) https://www.statista.com/statistics/952996/malaysia-palm-oil-share-of-gdp/
Tian, Q., Coa, Z. Z., & Zhang, R. (2008). Digital camera and AutoCAD based method for measuring
the leaf area of landscape plants. Grassland and Turf, 3, 25-28.

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A Short Course on IoT

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A Short Course on IoT
Ioannis Chatzigiannakis1*, and Andrea Vitaletti2
1, 2

Sapienza Università di Roma, Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Rome, Italy1{ichatz,vitaletti}@diag.uniroma1.it*Corresponding Author

ABSTRACT
We present a short course on the Internet of Things designed to give a first understanding of themain issues in developing IoT solutions. The course is organised into four main sessions of 3 hours
each. T h e f i r s t session introduces the main IoT concepts. It focuses on the relationship of IoT withBig Data and the constraints introduced by the limited availability of resources on IoT devices. The
second session is focused on h a n d s - o n . To simplify the experiments, we exploit a convenientonline simulator and Arduino IDE, focusing on the maker approach, namely in doing the right thingwith minimal consideration in terms of efficiency. The third session focuses on the engineering
approach where efficiency is crucial and introduces a more advanced developing tool based on
FreeRTOS, a Real-Time Operating System for embedded devices. The last session introduces IoTLAB, an infrastructure for multiple node experiments. The last section of the paper discusses thelessons learned in the first edition of the course and introduces possible future work. All thematerial for the course is available at “A Short Course on IoT” (n.d.).

Keywords: Course plan; Education targets; Maker approach; Experimentation infrastructure;
IoT deployment

INTRODUCTION
This paper presents a short course on the Internet of Things designed to give a first understanding of
the main issues in developing IoT solutions. The course is organised into four main sessions of 3hours each.
The first session provides (see section 2) an introduction to the Internet of Things. In
particular, we investigate the connection between the IoT and Big Data. In our vision, IoT is the fuelof Big Data, allowing us to quantitatively measure phenomena that, up to yesterday, we were only
able to guess. Thanks to such measures, we can build models to understand the observed phenomena betterand predict future behaviours. T h i s way, we build actionable intelligence that allows us to act in theenvironment consciously. However, it is crucial to understand that IoT means a network of resourceconstrained devices: if computational power, memory, energy, bandwidth, and costs are notconstrained, it is likely the Internet, not the Internet of Things. Such constraints need the introduction ofconcepts such as edge-computing and duty cycles.
We believe experimenting is the most efficient way to learn this subject. Consequently, the course
encourages attendants to put their hands on it. No background is needed, even if basic electronics andcomputer science knowledge might help. The second session (see section 3) is designed to startexperimenting with real devices. The main goal is to allow students to integrate sensors suitable formonitoring. In this session, we pursue the Maker approach; namely, we focus on effectiveness, i.e.,
doing the right things, without considering efficiency. The hands-on start on the very nice andpractical simulator WOKWI (“Wokwi - Online Arduino and ESP32 Simulator,” n.d.). It leverages
the Arduino Programming language, allowing us to experiment with several hardware platforms andintegrate various sensors and actuators in the simplest but realistic way. Indeed, after some69

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simulations, students can upload their projects into real devices. In our case, we used the well-known
ESP32 platform. We exploited the ESP32- DevKit, which provides WiFI and BLE connectivity.
During the course, we stress that even if such technologies are not well suited for the IoT, they
represent a very practical means of experimenting with the IoT. Furthermore, Heltec provides an
ESP32 device supporting LORA (“ESP32 Series — esp32 latest documentation,” n.d.).
Once students have familiarised themselves with simple projects, it is time to clarify that theMaker approach is insufficient. Session 3 (see section 4) is designed to introduce the Engineering
approach focused on efficiency, namely doing things right. The main concept here is that we needmetrics to measure how much our solution satisfies the user requirements. An interactive approach
that, step after step, converges toward a satisfactory solution is encouraged. Only clear metrics allow us
to measure to what extent we indeed converge. Furthermore, while the Arduino programming language
is a very nice introduction, we discuss the need for a more sophisticated RTOS supporting
multitasking, Real-Time Applications and Real-Time Scheduling. To this purpose, in this section,
students experiment with FreeRTOS (“FreeRTOS,” n.d.) running on ESP32.
Finally, session 4 (see section 5) is designed to scale up the solutions developed in the previous
sessions in a testbed supporting many nodes. We introduce the IoT-LAB (“FIT IoT-LAB.,” n.d.), a
large-scale infrastructure suitable for testing small wireless sensor devices and heterogeneouscommunicating objects. Furthermore, IoT-LAB allows us to measure the use of resources on thenodes conveniently so that students can easily understand the impact of simple duty-cycle
approaches.
All the material for the course is available at “A Short Course on IoT” (n.d.). The course
design leverages the experience of teaching IoT in the MSC in Computer Engineering at Sapienza
University of Rome (Chatzigiannakis, n.d.).
The paper concludes with section 6, in which we discuss the lessons learned during the firstedition of the course and introduce possible future work.
Figure 1
The IoT allows us to observe real-world phenomena at an unprecedented level of granularity and to consciously
act in the environment in view of informed decisions obtained by analysing the collected data.

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A Short Course on IoT

INTRODUCTION TO THE INTERNET OF THINGS
The course is designed to be an introduction to the IoT. Most people relegate IoT to a convenient way
to implement remote control by mobile phones. This is a nice feature of the IoT, but we believe IoT
should be placed in a wider perspective. Lord Kelvin, the father of the first and second laws ofthermodynamics, said: “When you can measure what you are speaking about and express it innumbers, you know something about it”. The IoT is indeed a tool to measure phenomena at an
unprecedented granularity level observed only qualitatively before the advent of such technology. In
other words, the IoT fuels Big Data Analytics (Malek et. al., 2017). This is summarised in Figure 1.
The IoT is made of resource-constrained devices connected to the Internet. Internet
connectivity guarantees that IoT devices can be anywhere and interrogated at any time. The
ubiquitous presence of IoT devices allows us to collect data on many phenomena at an unprecedented
level of granularity. Such data, possibly enriched by other data available on the Internet, are distilled
into actionable intelligence, thanks to data analytic tools such as Machine Learning. The actionable
intelligence operates in the environment, commanding the actuators of the IoT devices. New
monitoring data confirm the effectiveness of such actions in an iterative process trying toconverge to optimal solutions. The main goal of the first session is to illustrate this vision.
However, s t u d e n t s m u s t u n d e r s t a n d that the IoT is a network of resource-constrained
devices. Nowadays, students deal with relatively powerful computers that provide GHz clocks, GB
of RAM, Mbps connectivity, and high-capacity batteries that can be recharged daily. In contrast, atypical IoT device can have a MHz clock, hundreds of KB of RAM, a few Kbps of connectivity (e.g.
LORA), and a battery with limited capacity that cannot be recharged easily. Moreover, the goal ofubiquitous deployments requires cost-effective devices that limit the quality of hardware components.
These constraints impose a careful design of the IoT solutions (Pereira et. al., 2020) and theemployment of energy-aware approaches (e.g. duty-cycle) as well as delegating the computation to more
powerful devices on edge, when possible, just to mention two possible approaches. Furthermore, tosupport access to low-level functionalities, IoT coding is mostly done in C or C++ languages, and insome cases, students need some time to familiarise themselves with such languages. D e s i g n i n g areliable IoT solution requires moving from the maker approach, mostly focused on effectiveness, toan engineering approach where efficiency is a key design ingredient.
At the end of this session, as an exercise, students are encouraged to explore IoT projects
presented on well-known platforms (e.g. H a c k e r N o o n ), identifying possible issues regarding theinefficient use of the resources.
First Experiences with Real Hardware: The Maker Approach
The main goal of this session is to put hands on. We believe this allows students to understandbetter the concepts discussed so far and have fun carrying out practical activities. Initially, we pursue
the maker approach; namely, we focus on doing the right thing with minor concerns in terms ofefficiency. To simplify this activity, we initially focus on WOKWI (“Wokwi - Online Arduino and
ESP32 Simulator,” n.d.), an online tool capable of realistically simulating many popular boards, partsand sensors. In particular, we simulate an ESP32 programmed in the Arduino Programming
language (see Figure 2) performing the following four main activities that will be replicated in theother sessions:a) The simplest actuator, namely a led.
b) The simplest digital sensor is a button.
c) An analogue sensor, namely a potentiometer, is connected to the ADC.
d) A simple example is the SR04 Ultrasonic Sensor.
e) MQTT connectivity.
For convenience, these activities rely on WiFi connectivity. Still, from the beginning, it is very
important to stress that this is a convenient option inappropriate in many IoT deployments, mostly71

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due to its high energy requirements.
Figure 2
A relatively complex simulation where the data acquired by the ADC connected to the variable resistor isdelivered to a back-end via MQTT. Similarly, commands can be delivered to the code to switch the LED on/off.

Once students are familiar with the above simulations, they are ready to experiment with real
hardware. In particular, we exploit the ESP32-DevKit (“ESP32-DevKitC V4 Getting Started Guide,”n.d.), a very flexible ESP32 platform which provides WiFI and BLE connectivity. Heltec (“ESP32
Series — esp32 latest documentation,” n.d.) provides similar hardware supporting LORA
connectivity.
Running the experiments on real hardware (see figure 3) is straightforward since WOKWI
allow us to download the Arduino code corresponding to the simulations that can be conveniently
uploaded to the ESP32 using the Arduino IDE (“Download and install Arduino IDE,” n.d.).
A simple backend relying on Thingsboard (ThingsBoard, 2017) allows us to collect the data
by MQTT visualise them in a nice dashboard and send MQTT commands to the ESP32 via a niceinterface.

The Engineering Approach: Measure Performance and Exploit an RTOS
This session is focused on two main goals: a) use metrics to measure to what extent yoursolution satisfies the user requirements, and b) introduce a real-time operating system (RTOS) moresuitable for reliable IoT deployments.
For the sake of simplicity, we focus on a simple project based on “Accident study findings”(2016), a paper presenting the results of the accident study findings on household uses of gas. We
quickly analysed the document in the class to identify a set of minimal requirements to detect fires in
the proximity of gas cabinets and to inform the emergency centre promptly. Here, we focus on asingle requirement to clarify the purpose of this activity, namely the need to detect a fire at 5 metersfrom the cabinet with an accuracy of at least 75% in at most 1 minute. We first analyse different
sensors to detect fires. Then, we designed an experiment in which sensors were placed at different
distances from the fire source to measure their ability to detect the episode reliably. Students

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A Short Course on IoTexperience concepts such as accuracy, false positives and negatives, and dependence on measures fromrelevant factors such as distance, the presence of the sun, etc. An iterative process to improve theoriginal solution is encouraged, as well as the design of experiments to quantify the quality of thesolutions in view of the user requirements.
Figure 3
An ESP32 is connected to a breadboard with a lead and a button. The USB cable provides power and debug
functionalities through the serial terminal.

Figure 4
A simple example is to blink two LEDs. On the left, the Arduino code is based on a single loop. On the rightis the FreeRTOS code with multiple tasks.

In the second part of the session, we introduce the need for Real-Time Operating Systems(RTOSs). Real-time requirements usually characterise IoT. Namely, IoT devices have to respondto a certain event within a strictly defined deadline. In the above example, we must detect the 1minute. This requires a deterministic behaviour of the scheduler. In this session, we introduce
FreeRTOS (“FreeRTOS,” n.d.), a state-of-the-art RTOS that supports the ESP32. In FreeRTOS, thescheduler exploits the user’s defined priorities to know which thread of execution to run next. The
ability to split a program into tasks with different priorities implies a dramatically different approach73

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to coding compared to the single loop of Arduino coding (see Figure 4).
Students analyse how to split the program into different independent tasks that can becoordinated when needed by mechanisms exploiting suitable OS functionalities. This activity isconcluded by porting the activities encoded in session 2 (see section 3) in Arduino into FreeRTOS. A
rich set of examples is available at https://github.com/espressif/esp-idf.
Leverage Existing IoT Infrastructures
Up to this section, students have experimented with a single device with different tools at
different levels of complexity. They proved to be able to integrate simple sensors and actuators onreal hardware, communicate acquired data to a back-end, and get commands from it. However,real IoT deployments are usually made of multiple devices. To explore scalability issues, weleverage IoT-LAB (“FIT IoT-LAB.,” n.d.). It is a large-scale infrastructure for testing small
wireless sensor devices and heterogeneous communicating objects. IoT-LAB supports FreeRTOS
code https://iot-lab.github.io/docs/os/freertos/, even if the ESP32 is not currently among the
supported hardware. This proves to students the capability of FreeRTOS to run on heterogeneous
hardware platforms with the same code. IoT-LAB allows us to experiment with ad-hoc networking and
LORA, a communication technology more suitable for the IoT, interacting with The Thing Network,a global collaborative Internet of Things ecosystem that creates networks, devices and solutions using
LoRaWAN. Finally, it also allows us to collect fine-grained data on the use of resources such as“Consumption monitoring” (n.d.), bandwidth, power consumption, etc., thus supporting theengineering approach introduced in the previous session.

DISCUSSION AND FUTURE WORKThe first edition of the course was given in August 2003 at the University of Stellenbosch, Sudafrica,
during an Erasmus+ teaching mobility exchange. Forty students among more than a hundred
candidates have been selected to participate. Students provided positive feedback on the course
and appreciated the hands-on (see the post on Linkedin https://acesse.one/bAvKP). Sessions 1,2 and 3
have been successfully delivered, while session 4 has been only introduced. In other words, 12hours to deliver all the sessions is overoptimistic; realistically, all the sessions can be delivered in atleast 16 hours.
All the material is publicly available on github (“A Short Course on IoT,” n.d.). The choice ofproviding a VirtualBox appliance with the development environment and all the necessary toolboxes
already deployed and configured has paid off. Likely, the most time-consuming activity in view ofthe heterogeneous OSs used by students (mostly Windows and MacOS in different flavours), has been
setting up the SSH connection with the appliance and the interoperability with a local IDE (e.g.
vscode). To simplify this issue further, the current version of the virtual appliance provides micro, aconvenient text editor that can run on an SSH terminal.
This course is our attempt to summarise a minimal set of topics to introduce students to the
IoT in view of the gained in teaching IoT in the MSC in Computer Engineering at Sapienza
University of Rome (Chatzigiannakis, n.d.) . Several relevant topics, such as security, have not been
considered with the goal of designing a simple and practical course to be delivered in a relatively shorttime. However, supplementary material on interesting issues, such as Kalman filters and machine learningon embedded devices, is already available. Depending on the course's length and the student's background,many extensions can be considered. However, we consider a deep understanding of the concepts
introduced in sessions 1, 2 and 3 crucial to dealing with IoT beyond the maker approach.
The course material is always a work in progress, and we hope to improve it in view of the
feedback provided by attendees, colleagues and experts. Collaborations are very welcome.

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ACKNOWLEDGEMENT
This work is partially supported by the Master's Degree in Industry 4.0 610455- EPP-1-2019-1-MyEPPKA2-CBHE-JP (https://www.ind4-0-eu.my/index.php). The material has also been developed
during an ERASMUS+ ICM TEACHING MOBILITY program of Andrea Vitaletti to the Universityof Stellenbosch in Sudafrica.

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Short Course on IoT. (n.d.). Andreavitaletti.github.io.
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Chatzigiannakis, I. (n.d.). Ioannis Chatzigiannakis Site/Internet Of Things 2023. Ichatz.me. Retrieved
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(n.d.). Docs.espressif.com. Retrieved September 2023, from https://docs.espressif.com/projects/espidf/en/latest/esp32/hw-reference/esp32/get-started-devkitc.html
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FreeRTOS - Market leading RTOS (Real Time Operating System) for embedded systems with Internet of Things
extensions. (2010). Freertos.org. https://www.freertos.org/
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Pereira, F., Correia, R., Pinho, P., Lopes, S. I., & Carvalho, N. B. (2020). Challenges in Resource-Constrained
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ThingsBoard;

thingsboard.

Wokwi - Online Arduino and ESP32 Simulator. (n.d.). Wokwi.com. Retrieved September 2023, fromhttps://wokwi.com/

75Sector Skills Gaps Findings in Industrial Revolution IR4.0

e-proceedings book ICoBTA-IR4.0

Sector Skills Gaps Findings in Industrial Revolution IR4.0
Margaret Chan Kit Yok1* and Ling Siew Ching221
Faculty of Plantation and Agrotechnology, Universiti Teknologi MARA, Cawangan Sarawak, Malaysia
College of Computing, Media and Mathematics, Universiti Teknologi MARA, Cawangan Sarawak, Malaysia

drmchan@uitm.edu.my; lingsc@uitm.edu.my*Corresponding Author

ABSTRACTTechnical skills that include data and IT are no longer enough for employees to compete in the highly
competitive global industry shifting to industrial 4.0. Educational institutions are catching up andadopting the changes, but it is not as quickly as the industries expect. The curriculum has started byadopting the industry requirements from discussion and cooperation with industry to an initial extent.
The required skill is still missing in terms of inter-discipline linkage. Not all graduates have the linkageof their major with IoT, IT, and data analysis. These findings are from focus group discussions acrosspartner countries in the ERASMUS+ KA2 Capacity Building in the Field of Higher Education for the
Project Master’s in Science in Industrial 4.0. In addition, soft skills deficits exist in two areas: social
competence skills and personal skills. They are paramount in the workforce at every level, needing
agility, self-initiated learning skills, critical thinking, complex problem solving, creativity andinnovation, and communication skills. The resistance to the changes in the work process is also
encountered.
Keywords: Industrial
Skill; Personal Skill

4.0;

Data

and

IT

Skill;

interdisciplinary

linkage;

Competence

INTRODUCTION
In 2022, the world’s population reach 7.9 billion, with 29 billion connected devices predicted to interact
with each other, of which around 18 billion will be related to the Internet of Things (IoT). The industry
sector is increasingly becoming more interconnected, digital and flexible. The Fourth Industrial
Revolution (IR4.0) has brought significant social and economic opportunities and challenges, requiring
governments to respond appropriately (Manda & Dhaou, 2019). Cheong and Le (2022) chronologisedthat the Industrial Revolution began with the invention of steam-powered engines and hydraulic power
for the mechanisation of manufacturing in the late 18th century. The arrival of electric power led to thesecond Industrial Revolution, said to occur in the early 20th century, becoming the driver of both mass
production in industry and at home. The third Industrial Revolution was associated with the emergenceof ICT for automation in industry, programmable logic controllers (PLCs) and the development of
personal computers that began in the 1970s. The beginning of the 4th Industrial Revolution (IR4.0) in2016 combined human and technological capabilities in industry, including cloud computing,
beginning with the most technologically advanced countries. The many innovations such as artificial
intelligence, big data analytics, simulation, systems integration, robotics, cloud systems, IoT, andaugmented reality are also referred to IR4.0 as the “digital revolution.”
The World Economic Forum (WEF, 2018, p. vii) revealed that as the IR4.0 unfolds, companies
are seeking to harness new and emerging technologies to reach higher levels of efficiency of production
and consumption, expand into new markets, and compete on new products for a global consumer basecomposed increasingly of digital natives. As workforce transformation is deemed to accelerate, thequestion is how ready business leaders across all industries and regions are to meet the challenges of76this new era of accelerating change and innovation. In the same report, one of the key findings is thegrowing instability of skills. The World Economic Forum’s Readiness for the Future of Production
Report 2018 defines ‘readiness’ as the ability to capitalise on future production opportunities, mitigate
risks and challenges, and be resilient and agile in responding to unknown future shocks. The assessment
measures readiness for the future of production across two different components, Structure of
Production and Drivers of Production, on a scale of 0 (worst score) to 10 (best score), as shown in
Figure 1.
Figure 1
Readiness Diagnostic Model Framework
Future of Production Capabilities
Structure of Production

Complexity

Scale

Drivers of Production
Technology&
Innovation

Human
CapitalGlobalTrade &
Investment

Institutional
Framework

Sustainable
Resources

DemandEnvironment(Source: World Economic Forum’s Readiness for the Future of Production Report (2018, p.5)

Note (Source: World Economic Forum’s Readiness for the Future of Production Report (2018, p.6):
A country’s Structure of Production depends on several variables, including the strategic decisions a country
makes to prioritize sector development across agriculture, mining, industry and services reflecting thecomplexity and scale of a country’s current production baseComplexity: Assesses the mix and uniqueness of products a country can make as a result of theamount of useful knowledge embedded in the economy and the ways in which this knowledge is
combined.
Scale: Assesses both the total volume of manufacturing output within a country (Manufacturing Value
Added) as well as the significance of manufacturing to the economy (Manufacturing Value Added,% of GDP).
Drivers of Production The framework’s Drivers of Production are key enablers that position a country tocapitalize on emerging technologies and opportunities in the future of production.

The three ASEAN countries, Malaysia, Indonesia and Cambodia benchmarked to the top 5
countries either in the Structure of production or Drivers of production is shown in Table 1. Japan has
the highest Structure of Production score, followed by Korea, Germany, Switzerland and China until
the 5th ranking. On the other hand, the United States performs the best across all Drivers of Production,
followed by the next top five: Singapore, Switzerland, the United Kingdom and the Netherlands. Only
Switzerland is within the top 5 in both components. Malaysia, ranking at 20 in the Structure of
Production and 22 in Drivers of Production, is thus considered doing very well.
Table 1
Readiness for Future Production for Cambodia, Indonesia and Malaysia
Country

Archetypes

Japan
Korea
Germany
Switzerland
China
United States
Singapore
United Kingdom
Netherlands
Malaysia
Indonesia
Cambodia

Leading
Leading
Leading
Leading
Leading
Leading
Leading
Leading
Leading
Leading
Nascent
Nascent

Structure of Production
Score
Rank
9.99
1
8.85
2
8.69
3
8.39
4
8.25
5
7.78
7
7.28
11
7.05
13
6.32
26
6.81
20
5.41
38
3.56
81

Drivers of Production
Score
Rank
6.82
16
6.51
21
7.50
6
7.92
3
6.14
25
8.16
1
7.96
2
7.84
4
7.75
5
6.51
22
4.89
59
3.63
91

Source: World Economic Forum’s Readiness for the Future of Production Report (2018, p. 12)

77

Sector Skills Gaps Findings in Industrial Revolution IR4.0

e-proceedings book ICoBTA-IR4.0

The 4 Archetypes categories based on weighted Structure of Production and weighted Driver of
Production scores from the top 75 countries are derived recognising that each country has its own
unique goals and strategy for production and development and does not receive an overall global
ranking from the World Economic Forum’s Readiness for the Future of Production Report (2018, p.8)
as shown in Figure 2. Malaysia is categorised as a leading Archetype country, while Indonesia andCambodia are categorised as nascent.
Figure 2
The lines dividing the four quadrants were drawn using the average Driver of Production score (5.7) and Structureof Production score (5.7) for the Top 75 countries to create Archetype borders.
Favourable Drivers of Production

High Potential

Leading

Limited current base
Positioned well for the
futureStrong current base
Positioned well for the
futureLarge/Complex
Structure of
Production

Small/ Simple
Structure of
Production

Nascent

LegacyLimited current baseAt risk
for the future

Strong current baseAt risk
for the future

Unfavourable Drivers of
Production(Source: World Economic Forum’s Readiness for the Future of Production Report (2018, p.9)

Vora-Sittha and Chinprateep (2020), in exploring the readiness of the ASEAN Community for
IR4.0 using SWOT (Dess, 2018) and TOWS analysis (Watkins, 2007), reported that only Singapore is
the readiest country to face 4IR. The rest of the ASEAN members, including Malaysia and Indonesia,
are moderately prepared for the world 4IR, except Brunei, Cambodia, Laos, and Myanmar. In 2020,
analysts predict that in the next 10 years, 3.5 million people will be needed to fill specificmanufacturing vacancies with high competencies in emerging technologies such as IoT, digital twins,
and smart factories (Hernandez-de-Menendez et al., 2020). According to Hecklau et al. (2016),
competencies needed in the IR4.0 era range from managing complex manufacturing systems to having
more creativity, strategic thinking, and coordination skills. Deloitte’s Global survey (2018) shows that87% of survey participants in Asia lack confidence in executives’ ability to act as stewards of Industry
4.0, with the Asia-Pacific region standing out as the region with the lowest confidence worldwide.
Thus, Cheong and Li (2020) questioned what role higher education plays in addressing human andtechnological capabilities and enabling all stakeholders to be current in IR4.0 knowledge and use. In
addition, what type of education is best suited to ensuring proficiency?
It is in this context that the consortium of 16 Partners led by Universiti Teknologi MARA wasinvolved in ERASMUS+ KA2 Capacity Building in The Field of Higher Education – master’s degree
in Industry 4.0 / Ind4.0 Project with the problem statement that the educational sector of the Asianpartners from Malaysia, Indonesia and Cambodia is not yet ready to produce Industry 4.0- readygraduates. Educational programmes in target country institutions need to be reinforced in order toprepare students to cope with the technological diversities of the field and the practical implications ofapplying these technologies to existing production and business models. An assessment had to be
conducted on the gap between Asia businesses' need and capability to adopt Industry 4.0.

78

Note:
The consortium partners:
Malaysia: Universiti Teknologi MARA, Universiti Teknologi Malaysia, Universiti Kuala Lumpur
Cambodia: University of Heng Samrin Thbongkhmum, National University of Battambang, National Mean
Chey University, Ministry of Education and YouthIndonesia: Syiah Kuala University, Universitas Sumatera Utara
Italy: Università Degli Studi Guglielmo Marconi, “La Sapienza” University of RomeGreece: Hellenic Open University (Greece), AMC Metropolitan College, Skybridge Partners
Germany: BK Consult

METHODOLOGY
The two Asian partners, Malaysia and Indonesia, used focus groups from the Manufacturing,
Agriculture /Aquaculture and Health sectors. I focus group guidelines on collecting information fromthe field of IR 4.0, focusing on the training needs of their personnel. This was aimed at them
participating in the professional training component of the IR4.0 project that is to be developed at alater project stage. The structure consisted of a series of open-ended questions, the order of which andtheir exact wording were largely up to the interviewer. The interviewees were motivated to answer inas much detail as possible; aspects that the interviewee raised him/herself were further explored. The
data collection took place online by sending a softcopy of the questionnaire to the different participants
via email due to the Movement Control Order enforced during the COVID-19 pandemic in 2019. A
detailed and in-depth qualitative data analysis was carried out for selected questions relevant to the aimof the need analysis. In Malaysia, 7 respondents were from the agriculture sector, 2 from the
manufacturing and 1 from the health sector. In Indonesia, only one respondent came from the
manufacturing, automotive and agriculture sectors.

RESULTS AND DISCUSSION
The responses to the field skills gap exploratory questions by respondents of the four sectors:
Manufacturing, Automotive, Agriculture, and Health are as follows:
Recruitment of skilled employees in the field of Industry 4.0 is an increasing problem. What do
you think should be the ideal profile of an employee in the Field?
Table 2 shows the respondents’ ideal profile of an employee. The common element is that employees
must have very good problem-solving skills, reflected in the manufacturing, agriculture, and healthsectors. The employee should have technical skills and fundamental knowledge with a lifelong learning
attitude in the perspective sectors, as mentioned by the manufacturing and agriculture sectors.
Table 2
Ideal Profile of an employee in the manufacturing, automotive, agriculture and health sectors

•

•
•
•

Manufacturing
Good technical skills
with basic andspecialist knowledge
from a person’s own
speciality or
discipline
Good in the
management area
Very good problemsolving skills.
Data and IT
skills: Control, use,checking of data-••

Automotive
Willingness toface challenges.
Good businessacumen

•
•
•
•
•
•

•

Agriculture
Good communication skill
Leadership
Innovative
Fast life-long learning goal
Integrity
Willingness to work in a hitech environment with apassion for new emerging
technologies.
Graduate technicians
should have exposure to
field automation orAgriculture 4.0-related

Health• Very good problemsolving skill

79Sector Skills Gaps Findings in Industrial Revolution IR4.0

•
•
•
•
•

based systems, data
analysis, data
security/dataprotection, etc.
Social competence:
Interdisciplinary
cooperation
Good communication
skillsLeadershipcompetence
Good decisionmaking
Self-initiated learning
skills•••••e-proceedings book ICoBTA-IR4.0

study with adequate
practical training.
Mandatory fundamental
knowledge in Agriculture
Science, System Science,
Computing andEngineering.
Very good problem-solving
skill
Moving forward attitude
Good communication skills
Commitment anddiscipline

What do you think are the main skills missing from the current employees?
The key findings of the main skills missing from the current employees showed that the current
manufacturing sector employees miss personal skills. In contrast, the automotive and agricultural
sectors miss technological skills, as shown in Table 3. In the health sector, the employees do not havebusiness acumen.
Table 3The main skills missing in an employee are in the manufacturing, automotive, agriculture, and health sectors.
Manufacturing• Management skill• Communication
skill

•

Automotive
Readiness of thegraduates to enter
the work force with
the needed skillset.

Agriculture• Ability to handle and masterautomation technology
such as remote sensingbased monitoring
techniques.
• Attitude with outdated
knowledge• The slow learning process
to take control of newtechnology in the field with
Agriculture 4.0 application.
• Self-driven attitude• Critical thinking• Resistant to the changes ofwork process• Big data analysis ability inusing the knowledge ofprogramming andmodelling.

Health• Business acumen

Are you familiar with the term “soft skills”? Can you name some of them?
The respondents' familiarity with the term “soft skills” was mapped from levels 3 and 4 of the Global
Skills Taxonomy to represent skills of interest to organisations across sectors based on the Future of
Jobs Report 2023 (World Economic Forum, 2023). Table 4 shows respondents' familiarity with theterm ‘soft skills’ in the manufacturing, automotive, agriculture, and health sectors categorised basedon skill taxonomy.
The terms of the respondents were mapped into two components of the Skill Family: Attitudes andSkills, knowledge, and abilities. Under the Attitude category, 3 skills clusters were identified: Ethics,
Self-Efficacy and Working with others. Under the Skills, knowledge, and abilities, 2 skills clusters
were identified: Cognitive Skills and Management Skills.

80

Table 4
Familiarity with the term ‘soft skills’ by respondents in the manufacturing, automotive, agriculture and health sectors categorised based on skill taxonomy at Level 1 and Level 2of the Global Skill Taxonomy
Skill family(Level 1)
Attitudes

Skill Cluster(Level 2)

Manufacturing

Automotive

Agriculture

HealthIntegrity
Work Ethic
Flexibility
Interpersonal
Positive Attitude
Professionalism-

Responsibility
Work Ethic
Dependability
EmpathyFlexibility-

Working with others

Communicating Skill
Courtesy
Teamwork

Communication skill
Leadership
Teamwork

Communication skill
Adaptability

Cognitive skills

Problem Solving-

Management Skill

Management
Time management-

Communication skill
Adaptability
Leadership
Teamwork
Critical thinking
Decision making
Problem solving
Organisational
Time management

EthicsSelf-efficacy

Skills,knowledge, andabilities

Flexibility
Interpersonal-

Problem Solving-

81Sector Skills Gaps Findings in Industrial Revolution IR4.0

e-proceedings book ICoBTA-IR4.0

Are you satisfied with the soft skills and competencies of your employees?
Table 5 shows the perception of satisfaction with employees' soft skills and competencies. The
agriculture sector showed the lowest level of satisfaction, followed by the manufacturing sector, while
respondents from both the automotive and health were satisfied.
Table 5Perception of Satisfaction by the Soft Skills and Competences of Employees
Manufacturing
Moderately Satisfied

Automotive
Satisfied

Agriculture
Not satisfied tomoderately satisfied

HealthSatisfiedIf you could summarise in 3 words the profile of a successful employee, what skills would you put
first?
Individual respondents have different opinions, and no trend was based on the Global Skill Taxonomy
recognised, as shown in Table 6.
Table 5
Summary of a 3-word profile of a successful employee
Manufacturing• Smart, Agile,
Collaborative• Smart intechnical
competency• Skilful, fast, andFlexible

Automotive
Possess the skill
needed,willingness to facechallenge, withgood businessacumen.

Agriculture• Communication skills, problemsolving, and innovation.
• Thinking outside the box• Knowledgeable, ethical, selfmotivated.
• Dare to try.
• Attitude, Soft Skills, Expertise• Hardworking and innovative• Obey your Boss.
• Knowledge, commitment, and time
management

HealthInnovator, creative,
adaptability

The key findings of focus groups showed that the current health sector employees lack businessexpertise, which will help them be more efficient, while in the manufacturing sector, more management
skills are needed; in the agricultural sector, the current employees lack automation and technology skills.
One of the major challenges is the “Old” Employees because they are.
• still in the mind of belief in the old era of technology.
• wants to be in a comfort zone.
• Either they are naive in using the technology or object to use it.
One of the representatives of low development of IR4.0 is the retention of the aged and old generationof employers followed by employees. Therefore, the awareness and actions to reshape new andadvanced skills and competency are left behind (Salleh et al., 2022)

CONCLUSION
The focus group discussion unanimously indicated that existing programmes are not advocating for the
Ind4.0. There is a lack of skills and competencies needed in IR4.0. One barrier is the mindset of theaged and old generation of employees and their reluctance to change and adapt.

82

ACKNOWLEDGEMENT
This paper is part of the deliverable of Work Package 1 of the ERASMUS+ KA2 Capacity Building In
The Field Of Higher Education – Master Degree in Industry 4.0 / Ind4.0 Project.

REFERENCES
Cheong, K.C. and Li, R. (2022). The role of Malaysia higher education in IR4.0. Journal of International andComparative Education, Volume 11 Issue 2 ISSN 2232-1802 doi: 10.14425/jice.2022.11.2.0913
Deloitte Insights (2018). The Fourth Industrial Revolution is here – are you ready? Copyright © 2018 Deloitte
Development LLC.
Dess, G. (2018). Strategic Management. United States: McGraw-Hill. ISBN 9781259927621
F. Hecklau, M. Galeitzke, S. Flachs H. Kohl, Holistic approach for human resource management in Industry 4.0,
Procedia Cirp, 54, 2016. pp. 1–6.
M. Hernandez-de-Menendez, R. Morales- Menendez, C. A. Escobar, M. McGovern, Competencies for industry
4.0, International Journal on Interactive Design and Manufacturing (IJIDeM) 14, No 4, 2020, pp. 1511–
1524.
Lee,

C.C. (2023) Mapping soft
https://elearningindustry.com/

skillsforcompetency-basedlearning.

eLearning

Industry

Manda MI, Ben Dhaou S (2019). Responding to the challenges and opportunities in the 4th Industrial revolution
in developing countries. In: Proceedings of the 12th international conference on theory and practice ofelectronic governance, pp. 244253. https://dl.acm.org/doi/abs/10.1145/3326365.3326398
Salleh, N.I., Ijab, M.T. & Hashim, Noramiza Hashim (2022 ). A Review on Industrial Revoltuion 4.0 (IR4.0)
Readiness Among Industry Players. S.-C. Haw and K. Sonai Muthu (Eds.): CITIC 2022, 10, pp. 216–231,
2022. https://doi.org/10.2991/978-94-6463-094-7_18
Vora-Sittha, P. and Chinprateep, A (2021). Readiness of the ASEAN Community for the 4th Industrial Revolution.
Asian Social Science, Vol. 17 (2). ISSN 1911-2017 E-ISSN 1911-2025. doi:10.5539/ass.v17n2p31
Watkins, M. (2007). From SWOT to TOWS: Answering a Reader’s Strategy Question. Harvard Business Review,
March 27.
World Economic Forum in collaboration with A.T. Kearney (2018). Readiness for the Future of Production Report2018. World Economic Forum’s System Initiative on Shaping the Future of Production.
World Economic Forum (2018).The Future of Jobs Report. World Economic Forum’s System Initiative on
Shaping the Future of Production.
World Economic Forum (2023). Future of Jobs Report 2023. https://www.weforum.org/reports/the-future-ofjobs-report-2023/

83

A Roadmap to Industrial Revolution 4.0

e-proceedings book ICoBTA-IR4.0

A Roadmap to Industrial Revolution 4.0: Preparing Students’
Sustainable Skills via STEM Education
Ling Siew Eng1*, Azlina Bujang2*, and Mohd Syahiran Abdul Latif 3*
1,2

College of Computing, Media and Mathematics, Universiti Teknologi MARA, Cawangan Sarawak, Malaysia
3
Academy of Contemporary Islamic Studies, Universiti Teknologi MARA, Cawangan Sarawak, Malaysia,
lingse@uitm.edu.my; azlina80@uitm.edu.my; syahiran@uitm.edu.my*Corresponding author

ABSTRACTThe Fourth Industrial Revolution (InD4.0) needed a workforce in digital technologies, automation,and data-driven processes. Individuals need sustainable skills to excel in rapidly evolving industries
and workplaces to thrive in this era. STEM education is a gateway to acquiring sustainable skills. The
skills are closely linked to the 21st-century skills, knowledge, and competencies crucial for individuals
to thrive and succeed in the modern world. The Malaysia Education System prepares students tobecome a competent workforce for InD4.0, guided by the Malaysia Education Blueprint. Malaysia
Higher Education also nurtures sustainable skills as a continued effort to reinforce the skills acquiredat the school level. This paper outlines the initiatives in STEM education, provides an overview of thecurrent status, and presents plans for STEM education to prepare a skilled workforce for InD4.0 in
Malaysia.
Keywords: Sustainable skills; 21st century skills; STEM education, InD4.084

e-Proceedings Book ICoBTA-IR4.0

Impact of Embedded-TCP Key Exchange for Fragmented Segment

Impact of Embedded-TCP Key Exchange for Fragmented Segments
Suherman1*, Emerson P. Sinulingga2, Ali H. Rambe3, Tigor H. Nasution4, and Marwan Al-Akaidi5
1,2,3,4

Universitas Sumatera Utara, Medan 20155, Indonesia
5
Al-Farahidi University, Baghdad, Iraq1

suherman@usu.ac.id*Corresponding Author

ABSTRACT
Many encryption methods are offered in the application layer. Meanwhile, security offered by thetransport layer protocol is limited to securing the end-to-end connection, not the transport layer
payload. This paper proposes key exchange using the pass protocol (TPP) method embedded in the
TCP header. To avoid a compromised key, a new one is periodically sent using the previous TCP
window. Certain mechanisms should be applied when the new key is not successfully received so thatthe content decryption process is not disrupted. Evaluation through multiple link simulation shows
the security possibility of the proposed method increases, although some delays are injected.
Keywords: Embedded three-pass protocol ; WiMAX; TCP; Secure network
INTRODUCTION
Although TCP serves internet packets with a reliable end-to-end connection, the protocol does notguarantee the packet's safety against attack. It relies on the upper layer for packet security. The
protocol has had some security issues (Gont & Bellovin, 2012). In 2012 and Gratzer, and in 2016, the
QUIC protocol was introduced to enhance transport security, which evolved into HTTP/3(Radhakrishnan, 2022). Research has found that QUIC performed best with small-capacity web
resources (Chatzoglou et. al., 2023). A report also found that the Bleichenbacher attack was able tocompromise the key (Christopher et. al., 2014; Oran et. al., 2023).
The embedded three-pass protocol (TPP) in the TCP header has been previously proposed(Suherman et. al., 2021; Xiong et. al., 2020). Even though it does not relate to QUIC, which has adifferent frame arrangement from TCP, the idea is useful as the additional load in the header cancontribute to packet security. The three TPP messages inserted in the TCP handshaking process makesecurity a concern at the beginning of the connection. Figure 1 shows how the embedding process
occurs. The TCP call establishment inserts the encryption key Ak through the TPP messages. Thesemessages are transported in SYN, SYN-ACK and ACK.

85

Impact of Embedded-TCP Key Exchange for Fragmented Segment

e-Proceedings Book ICoBTA-IR4.0

Figure 1
TPP schema in TCP Header

Once the key has been successfully exchanged, the key can be used for all communication. or
only for certain windows. The next key can be piggybacked to the acknowledgement packet. The
problem appears when fragmented segments are sent using different packets, experience
retransmission, and use different keys. This paper analyses two arrangements for two scenarios. The
schemas are similar keys for fragmented segments and keys for different windows, as shown in
Figure 2.
Figure 2
Dynamic keys toward the fragmented segment

86e-Proceedings Book ICoBTA-IR4.0

Impact of Embedded-TCP Key Exchange for Fragmented Segment

EVALUATION METHOD
As in Figure 3, a multiple-links simulation was developed using the NS2 simulator. The TCP/full is
employed, and a simple one-time-pad encryption was employed to show how the encryption was
performed. External traffic background was introduced using unreliable datagram protocol (UDP)
streams, but devices are not shown. The application was set for video bit rates, from 275 kbps to 500kbps.
Figure 3
Multiple link simulation

SIMULATION RESULTS
Figure 4 shows the average delay for Normal TPP, embedded TPP with different keys for fragmented
segments and similar keys for fragmented segments. It is clearly shown that similar keys forfragmented segments yield lower delay. Delay differences are higher when the traffic rate increases.
On average, similar keys produce a 16% lower delay than if all packets from the same segment areassigned different keys.
Figure 4
TCP stream performances for Embedded TPP in the MAC layer

In terms of security, TCP streams for fragmented segments are less protected than the ones
using different keys as shown in Table 1.

87Impact of Embedded-TCP Key Exchange for Fragmented Segment

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Table 1
Security Protection
SignalEmbedded
TPP in TCP Header
Wireless link
Other links

TCP streams unfragmented

Yes/All

Yes/All

TCP streams fragmented

Yes/Less

Yes/Less

CONCLUSION
This paper examined the impact of embedded TPP into the TCP header on fragmented segments. In
terms of delay, keeping the same keys for packets from the same segment reduces the end-to-end
connection delay. However, this increases the risk for subsequent packets within the same segment.
Some applications may accept separate packets from the TCP process, which makes it suitable foralways-change keys. However, another application requires a single segment of packets to be sent atonce from the transport layer, which suits similar keys. There may be other security solutions for thelater application.
REFERENCES
Chatzoglou, E., Kouliaridis, V., Karopoulos, G., & Kambourakis, G. (2023). Revisiting QUIC attacks: A
comprehensive review on QUIC security and a hands-on study. International Journal of Information
Security, 22(1), 347-365.
Christopher, M., Juraj, S., Eugen, W., Jӧrg, S., Erik, T., & Sebastian, S. (2014). Revisiting SSL/TLS
Implementations: New Bleichenbacher Side Channels and Attacks . In Proceedings of the 23rd USENIX
Security Symposium (USENIX Security 14), San Diego, California (pp. 733-748).
Gont, F., & Bellovin, S. (2012). Defending against Sequence Number Attacks. RFC 6528.
Gratzer, F. (2016). QUIC-Quick UDP Internet Connections Florian. Future Internet (FI) and Innovative
Internet Technologies and Mobile Communications (IITM), 39-46.
Oran, S., Koçak, A., & Alkan, M. (2022). Security Review and Performance Analysis of QUIC and TCP
Protocols. In 2022 15th International Conference on Information Security and Cryptography(ISCTURKEY) (pp. 25-30). Ankara, Turkey.
Radhakrishnan, R. (2022). One-Way Delay Mechanism for Maintaining Latency Service Level
Agreement [Doctoral dissertation, Universiteit van Amsterdam].
Suherman., Deddy, S., Syafruddin, H., & Marwan, A. A. (2021). Embedding the Three Pass Protocol Messages
into Transmission Control Protocol Header. Indonesian Journal of Electrical Engineering and Computer
Science, 22(1), 442-449.
Xiong, G., Han, S., Zhang, Y., Zhu, Y., & Fwu, J. K. (2020). Low latency contention based scheduling request.
(U.S. Patent No. 10555322). U.S. Patent and Trademark Office. https://patents.justia.com/patent/10555322

88

e-Proceedings Book ICoBTA-IR4.0

Universal Low Power Wireless Sensor Node with Energy Harvesting

Universal Low Power Wireless Sensor Node with Energy
Harvesting
Emerson Pascawira Sinulingga 1*, Dani Gunawan 2, Dwi Budi Prasetyo 3, Soeharwinto 4,
Suherman 5, Tigor H. Nasution 6 and Abdul Risyal Nasution 7
1,4,5,62

Electrical Engineering Department, Universitas Sumatera Utara Indonesia
Department of Information Technology, Universitas Sumatera Utara Indonesia
3,7Mandike Instrumnets, Indonesia
emerson.sinulingga@usu.ac.id

ABSTRACT
Sensor nodes with tethered batteries have the main issue of energy limitation and short lifetime.
Laboratory and educational purposes also require the universal sensor node to have more flexibility
and capabilities for various IoT applications. This paper uses solar panels to develop a universal
wireless sensor node with low-power components and energy harvesting. Gyroscope and solar
panel charging were added in the sensor node to track sensor nodes in bad environments andincrease the sensor node lifetime. The proposed sensor node worked as expected, and the voltage
rate of batteries was reduced by 0.33 mV/minute without energy harvesting from solar panels. The
sensor node lifetime was estimated at 140 hours or 6 days. However, the sensor node batteries will
charge if a solar panel is applied. The voltage of sensor node batteries was gradually increased
from 7.76 volts to 8.12 volts, an interval of 300 minutes; therefore, the energy limitation frombatteries can be solved.
Keywords: Sensor node; Gyroscope; Solar panel; Batteries voltage; Lifetime

INTRODUCTION
Wireless sensor networks (WSN) and the Internet of Things (IoT) have been popular in every
country, including Indonesia. The maker space has recently supported the WSN technology for IoT
platforms in Indonesia (Yasirandi et. al., 2020). The Wireless sensor network (WSN) commonly
applies to the monitoring system, such as detecting air quality and water and soil pollution (GarcíaOrellana et. al., 2019; Lin et. al., 2017). Wireless sensor networks are widely used in IoT platforms
for diverse applications, including manufacturing, process control, smart cities, smart buildings,
healthcare, transportation, energy and beyond (Atzori et. al., 2010). The WSN and IoT are comprisedof sensor nodes that collect the information by sensor and send the information to another node in awireless network. Each sensor node includes a radio transceiver, power supply, processing unit,
sensors, and power management (García-Orellana et. al., 2019).
Sensor nodes with tethered batteries have the main issue of energy limitation and short
lifetime. The energy harvesting technology and routing protocol have been presented to resolve thisproblem (Tang et. al., 2017; Sittivangkul et. al., 2014; Sravan et. al., 2007; You et. al., 2021;
Nguyen et. al., 2014). However, the hardware sensor node also played an important role indeveloping the low-power sensor node, such as using a low-power regulator, low-power transceiver,and other low-power components for the sensor node (Nguyen et. al., 2014). However, the sensor
node is often designed as a semi-permanent prototype using Arduino and other circuits withcomplicated wiring for laboratory and educational purposes (Munir et. al., 2018; Nasution et. al.,2017; Nasution et. al., 2020). The semi-permanent sensor nodes are easily fragile if applied to WSN
and IoT applications. Therefore, the universal sensor node with more flexibility and capabilities was
proposed as the solution. The universal wireless sensor node was developed with low-power

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components and energy harvesting using solar panels. The sensor node was analysed by measuring
each component's quiescent current and voltage batteries without and with a charging system.

THE QUIESCENT CURRENT
The quiescent current test was applied to measure the power usage of each component in the idle
sensor node before the sensor node developed. Figure 1 shows the quiescent current test using anoscilloscope for highly accurate current measurement. A load resistor (R) with a resistance of 1Ω wasadded in a negative line to measure the quiescent current. Therefore, the quiescent current (I) was
equal to the oscilloscope's read voltage (V).
The measured quiescent current of each component in the sensor node consists of a regulator,
microcontroller, real-time clock, memory card, gyroscope, and GPRS/GSM Module. The
microcontroller ATMega328P and real-time clock (RTC) needed a supply voltage of 5 volts, thememory card and gyroscope needed a supply voltage of 3.3 volts, and the GPRS/GSM Module
SIM800L needed a supply voltage of 4.1 volts. The low-power regulator MP1584 has been applied in
the sensor node to adjust the source voltage from batteries to sensor node components. The quiescentcurrent of regulator MP1584 was measured with a source voltage of 8.4 volts, equal to the maximum
voltage of two-cell lithium-ion batteries.
The block diagram of the proposed sensor node can be seen in Figure 2. The GPRS/GSM
Module SIM800L, memory card, gyroscope, and RTC were connected to microcontroller
ATmega328P, and input power of components has been supplied from two cell lithium batteries
through regulator MP1458. The gyroscope and solar panel charging were added to the proposed
sensor node to track sensor nodes in bad environments and increase the sensor node lifetime.
Figure 1
Measurement of quiescent current of components

Figure 2
The block diagram of the proposed sensor node
Input/output pin for sensors

Memory Card
LCV125Microcontroller
ATmega328P

Real Time ClockDS3231
Regulator MP1584
5 volt, 4.2 volt, 3.3 volt
Input/output pin
for batteries

90

GPRS/GSM
Module SIM800L
Gyroscope
MPU-6050

Regulator MP1584, Op-Amp
LM358, and regulator AMS1117
Input pin for solar panel

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Universal Low Power Wireless Sensor Node with Energy Harvesting

The proposed sensor node uses the microcontroller ATmega328P; it's most popular in Arduino
for sensor node application (Munir et. al., 2018; Nasution et. al., 2017; Nasution et. al., 2020). The
ATmega328P has 8 8-bit data buses with a speed of 16 MHz and supports sleep mode for power
efficiency (Atmel, 2015). In the proposed sensor node, the ATmega328P was programmed toactivate components alternately when processing data and force the components to sleep mode when
the components do not process data.
GPRS/GSM Module Sim800L was chip and easy to find in the market; Sim800L works on
quad-band (850 MHz, 900 MHz, 1800 MHz and 1900 MHz), supports micro-SIM card, has a smaller
socket for additional antenna, and supports sleep mode for power efficiency (SIMCom, 2015). The
lower quiescent current of Sim800L was obtained in measurement due to the additional
omnidirectional antenna inserted in the test with a good signal.
RTC DS3231 was added in the proposed sensor node to keep accurate track of time. The
DS3231 is a serial RTC driven by a temperature-compensated 32 kHz crystal oscillator that provides
a stable and accurate reference clock while maintaining an RTC accuracy of ±2 minutes per year
from -40°C to +85°C (Maxim, 2015). In the proposed sensor node, the RTC works to wake up the
microcontroller ATmega328P when the system is in sleep mode; the wake cycle has been
programmed previously by ATmega328P. RTC support an internal battery to backup power if thesensor node is in sleep mode. The regulator HT733 was added to adjust the voltage of backup powerfrom the main batteries to RTC when the sensor node is in sleep mode.

PROPOSED SENSOR NODEThe circuit layout of the proposed sensor node was designed on a layer-printed circuit board, and viahole techniques have been applied to connect the two-layer circuit for the fabricated circuit board of
the sensor node. Figure 3(a) shows some components assembled on the upper side of the sensor nodecircuit board, including regulators 5 volt and 3.3-volt, wireless components, gyroscope, andmicrocontroller. The memory card, RTC, regulators 4.1 volts, and transistors P2222A wereassembled on the backside.
The voltage of sensor node batteries was measured without charging from the solar panel, as
shown in Figure 3(b). The power source of the sensor node is from two-cell lithium-ion batteries witha voltage of 8.17 volts, and the battery voltage was gradually decreased to 8.12 volts at intervals of(".$%&".$')
153 minutes. Therefore, the voltage rate in the battery sensor node was reduced
𝑣𝑜𝑙𝑡/
$)*
𝑚𝑖𝑛𝑢𝑡𝑒, or equal to 0.33 𝑚𝑉/𝑚𝑖𝑛𝑢𝑡𝑒. If the minimum voltage in batteries to the sensor node
working is 5.4 volts for two-cell batteries, the sensor node lifetime can be estimated to be 140 hours
or 6 days.
(8.17 − 5.4) 𝑣
𝑀𝑎𝑥 𝑣𝑜𝑙𝑡𝑎𝑔𝑒 − 𝑀𝑖𝑛 𝑣𝑜𝑙𝑡𝑎𝑔𝑒== 8394 𝑚𝑖𝑛𝑢𝑡𝑒(0.33 × 10&* ) 𝑣/𝑚𝑖𝑛
𝑣𝑜𝑙𝑡𝑎𝑔𝑒 𝑟𝑎𝑡𝑒
Figure 3
Prototype sensor node (a) final assembly and (b) Measurement of node sensor batteries

91Universal Low Power Wireless Sensor Node with Energy Harvesting(a)e-Proceedings Book ICoBTA-IR4.0(b)

Figure 4
Proposed sensor node applied with solar panel

Figure 5
Measurement of node sensor batteries with solar panel

Figure 4 shows the sensor nodes were tested with a solar panel for monitoring and control
energy harvesting. Figure 5 shows the voltage changes of the sensor node battery from two-cell
lithium-ion batteries with a voltage of 7.76 volts, which gradually increased to 8.12 volts at intervalsof 300 minutes. Applying the proposed sensor node with energy harvesting using solar panels
effectively solved energy limitations from batteries due to the batteries charging when it has a lower
voltage.

CONCLUSION
The proposed sensor node was worked as expected, and the estimated voltage rate of batteries
reduced 0.33 𝑚𝑉/𝑚𝑖𝑛𝑢𝑡𝑒 and six days estimated lifetime without energy harvesting. Furthermore,
periodically activating the sensor node every hour could increase lifetime. The sensor node withenergy harvesting using solar panels has more lifetime due to the energy limitation from batteries thathave been overcome.

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Universal Low Power Wireless Sensor Node with Energy Harvesting

REFERENCES
Atmel ATmega328P. (2015). 8-bit AVR Microcontroller with 32K Bytes In-System Programmable Flash[Datasheet].
https://ww1.microchip.com/downloads/en/DeviceDoc/Atmel-7810-AutomotiveMicrocontrollers-ATmega328P_Datasheet.pdf
Atzori, l., Iera, a., & Morabito, G. C. (2010). The internet of things: a survey. Computer Network, 54(15),2787–2805.
García-Orellana, C.J., Macías-Macías, M., González-Velasco, H. M., García-Manso, A., & Gallardo-Caballero,
R. (2019). Low-Power and Low-Cost Environmental IoT Electronic Nose Using Initial Action Period
Measurements. Sensors (Bazel, Switzerland), 19(14), 3183.
Lin, Z., Wang, W., Yin, H., Jiang, S., Jiao, G., & Yu, J. (2017). Design of Monitoring System for Rural
Drinking Water Source Based on WSN. International Journal of Advanced Network, Monitoring andControls, 2(4), (pp. 289-293).
Maxim Integrated. (2015). DS3231 Extremely Accurate I2C-Integrated RTC/TCXO/Crystal. [Datasheet].
https://datasheetspdf.com/pdf-file/1081920/MaximIntegrated/DS3231/1
Munir, M. M., Rizqika Akbar, S., & Bhawiyuga, A. (2018). Implementation of Wireless Sensor Nodes for
Monitoring Farmland Based 802.15.4 Protocol. In 2018 International Conference on Sustainable
Information Engineering and Technology (SIET), (pp. 240-246).
Nasution, T. H., Dika, S., Sinulingga, E. P., Tanjung, K., & Harahap, L. A. (2020). Analysis of the use of
SEN0161 pH sensor for water in goldfish ponds. In IOP Conference Series: Materials Science andEngineering, 851(1), ,pp. 012053, May 2020, doi: 10.1088/1757-899x/851/1/012053.
Nasution, T. H., Muchtar, M. A., Siregar, I., Andayani, U., Christian, E., & Sinulingga, E. P. (2017). Electricalappliances control prototype by using GSM module and Arduino. In 2017 4th International Conferenceon Industrial Engineering and Applications (ICIEA), (pp. 355-358).
Nguyen, M. -S., & Le-Trung, Q. (2014). Low-power and cost-effective wifi sensor motes for wirelessembedded Internet applications. In 2014 International Conference on Advanced Technologies forCommunications (ATC 2014), (pp. 441-445).
Sravan, A., Kundu, S., & Pal, A. (2007). Low Power Sensor Node for a Wireless Sensor Network. In 20th
International Conference on VLSI Design held jointly with 6th International Conference on Embedded
Systems (VLSID’07), (pp. 445-450).
Sittivangkul, T., Saadi, M., Wuttisittikulkij, L. (2014). Design and Development of a Wireless Sensor Mote
Prototype for Laboratory Usage. In 29th International Technical Conference on Circuit/Systems
Computers and Communications (ITC-CSCC), (pp. 678-681).
SIMCom. (2015). SIM800H & SIM800L Hardware Design V2.02 Smart Machine Smart Decision [Datasheet].
https://datasheetspdf.com/pdf-file/989664/SIMCom/SIM800L/1
Tang, W., Zhang, K., & Jiang, D. (2017). Physarum-inspired routing protocol for energy harvesting wirelesssensor networks. Telecommunication Systems, 67(4), 745–762.
Yasirandi, R., Lander, A., Sakinah, H. R., & Insan, I. M. (2020). IoT Products Adoption for Smart Living inIndonesia: Technology Challenges and Prospects. In 2020 8th International Conference on Information
and Communication Technology (ICoICT), (pp. 1-6).
You, S., Eshraghian, J. K., Iu, H. C. & Cho, K. (2021). Low-Power Wireless Sensor Network Using Fine-Grain
Control of Sensor Module Power Mode. Sensors, 21(9), 3198.

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Rural Internet Access Distribution Using Regression Model

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Rural Internet Access Distribution Using Regression Model
Suherman1*, Emerson P. Sinulingga2, Ali H. Rambe3, Tigor H. Nasution4,
Alvin C. Pardede5, and Marwan Al-Akaidi6
1,2,3,4,5

Universitas Sumatera Utara, Medan 20155, Indonesia
6
Al-Farahidi University, Baghdad, Iraq1

suherman@usu.ac.id*Corresponding Author

ABSTRACTInternet access has been widely deployed from growing existing PSTN providers and evolving cellular
network providers. Internet availability has reached rural areas, enabling connectivity everywhere,
every time, and everyone. However, price considerations and speed demands are major reasons forhomes, small offices, and rural schools to search for alternative providers. Some small internet
providers for those rural demands serve internet connections via digital radio links with limited
capacity, but the price and speed are attractive. Providers lease internet connections to a larger
company via fibre optics or radio connections in such systems. Subsequently, through a small local
area network and PC-based routers, the company distributes internet connections to rural users using
low-rate digital radios. All transceivers’ antennas are installed in a single antenna tower, forming a
local base station. Some techniques are available to be implemented for distributing the bandwidth tousers. As the attention is on rural applications, this paper examines the use of regression models frompast data for current internet connections.
Keywords: Rural internet services; Bandwidth scheduling; Statistical model

INTRODUCTION
Internet access for rural areas has been in great demand as most activities now partially rely on online
activities, including education jobs, as well as the demands of the Industrial Revolution 4.0. In someplaces, such as in Indonesia, rural areas have been covered by cellular network providers. However, as
speed demand increases, a more dedicated network is required. Some smaller internet providers
specialise in rural internet services (Svistova et al., 2022). This ubiquitous network access allows
access to internet servers through distance digital radio networks to distribute internet access. However,these small companies often face difficulties distributing internet bandwidth for their customer, which
is set through their routers or control network. In practical cases, decisions should be taken carefully,
as the efficient bandwidth allocations for each customer determine profits. On the other hand,
bandwidth allocation should satisfy customer demands, and complaints should be avoided.
Resource scheduling becomes important when limited resources should be shared among users.
The algorithm should determine the task and resource distribution as fast as possible and with as low
energy consumption as possible. The scheduling may apply quality of services (QoS) if service
differentiation is demanded (Sharma et al., 2023). Scheduling aims to determine which resource andtask to execute for a given time slot. The algorithm can be categorised as static and dynamic algorithms(Kumar et al., 2019). The static algorithm requires details on the number of tasks, duration and timing,
the available resources, memory, processing power and energy consumption so that the execution
setting can be implemented at the front stage. However, the dynamic systems that change in the numberof requests, duration, and available resources make this setting unsuitable for most cloud computing
scenarios.

94e-Proceedings Book ICoBTA-IR4.0

Rural Internet Access Distribution Using Regression Model

Some static scheduling (Beaumont et al., 2022) includes heuristic round robin (RR), first in, firstout (FIFO), and the shortest job first (SJF). Compared to static scheduling, the dynamic algorithm canbalance share among nodes. Dynamic scheduling (Wen et al., 2022) mostly outperforms static, forinstance, the heterogeneous earliest finish time (HEFT), weighted least connection (WLC) and theclustering-based heterogeneous with duplication (CBHD). This paper examines the dynamic
bandwidth allocation by regressing the past bandwidth requirement.

RESEARCH METHOD
Network Configuration
To examine the flow of bandwidth distribution among rural internet users from a single basestation, the following network configuration is used to fit the rural radio network model, as shown in
Figure 1. Each internet user is connected to the server at the distribution point via a radio link. The
distribution point connects all networks to the internet. Since there are many ways to implement a pointof distribution, one component is assumed to be responsible for bandwidth allocation. In practice, thebandwidth allocation algorithm can be set in the router or a PC dedicated to routing. However,
implementing the proposed method to any device may not be relevant as the marketed device allows
limited access to its firmware. Simulation is the most appropriate way.
Figure 1
The evaluated network configuration

DatasetThe dataset is obtained from Kaggle (www.kaggle.com) with modifications that suit traffic
generations from various rural networks. The traffic is classified based on the source of links. One
radio link represents one group of users. Therefore, users from the same group represent the traffic
source from that group. Table 1 shows the sample representation of the dataset.
Table 1
Sample of the internet traffic95

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This dataset comprises 125,381 internet data that come from 20 links. The data statistic is not ina normal distribution but skewed with the right tail and a bit fat with kurtosis 31.39. The subsequent
data has a 0.42 correlation, which means it does not contain many stationary points. These statisticalparameters are shown in Figure 2.
Figure 2
Sample of the internet traffic

(a)

(b)

Statistical Analysis
Since data distribution is not balanced, the transformation using the first difference is applied to omit
outlier impact. As a result, skewness drops, and even dough kurtosis increases slightly, but the
distribution becomes more normal.

Figure 3
Sample of the transformed Internet traffic96

e-Proceedings Book ICoBTA-IR4.0(a)Rural Internet Access Distribution Using Regression Model(b)

Bandwidth Allocation Algorithm
Bandwidth allocation in the router or dedicated PC is performed based on the execution cycle.
So, in that execution cycle, the scheduler checks all user requests and stays in the buffer. If the first in,
first out (FIFO) algorithm is chosen, then traffic with the lowest time stamp will be the priority, andthe latter will be the priority. All queued requests are executed continuously until the transported bytes
in that time slot are full. This cycle will be performed repeatedly at all evaluated times. This is a
simplification of the queue system within the router. There may be multiple servers. However, thisassumption is valid since the outgoing internet connection, as depicted in Figure 1, is only a single
channel.
For the proposed algorithm, as mentioned in the first section, the algorithm will continue toobserve the recorded data and create a model for future allocation. The model in this paper uses linear
regression, autoregression, and ARIMA, where the recorded traffic is split into training and test
datasets. The incoming traffic is then predicted using the model. The prediction is then compared to
the real data from the data test.
Performance Parameter Evaluation
If the requested data exceeds the allocated bandwidth, there will be a delay increment at least
twice for that queued data. Otherwise, the delay is assumed to have a 1-unit delay. If this condition
occurs subsequently, the probability of dropped packets will increase. The dropping condition should
satisfy the following: buffer size is lower than the queued packets. Therefore, delays and dropped
packets will be the performance parameters. Bandwidth utility will also be evaluated to measure how
efficiently the algorithm uses the available bandwidth.

SIMULATION RESULTS
The historical data of internet connections of the evaluated links are analysed using statistical models
such as linear regression, autoregression, and ARIMA. As a result, various mean square errors are
obtained for original and transformed data, as depicted in Figure 4.
Linear regression with all data is presented but shows under-predicted values for the required
allocated bytes. The error values are 416.349 for the original data and 449.185 for the transformed
values. Even autoregression and ARIMA yield lower errors, but the predicted allocated bytes are notgood enough to achieve the expected performances.

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Figure 4
The allocated bytes predictions using linear regression, autoregression and ARIMA

Linear reg mse 416.349

Autoregression mse 449.185

Autoregression mse 356.732

Autoregression mse 120.542

Autoregression mse 171.089

Autoregression mse 131.94

The predicted bytes are applied to the bandwidth allocation algorithm as in the following snippet:

As a result, when traffic is lower than the available bandwidth, the regression-based scheduler
performed worse than the legacy schedulers, such as first in, first out (FIFO) and round robin (RR), as
depicted in Table 2. FIFO performs best as the scheduler necessarily performs no sorting. Statistics
mean square errors cause networks to be under or over-utilized unnecessarily. The prediction process
also injects additional processing time.
Table 2
Performance when incoming traffic is less than the available bandwidth

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Performance parameter

FIFO

RR

Statistical prediction

Normalized predicted delay (unit*)

1.512

1.877

2.085

Number of loss packets during observations

25

35

1025

*Unit is measured as one transmission frame length

If the bandwidth is less than the incoming traffic, the delay increases significantly,
followed by a spike in loss as the buffer is overflowed (Table 3). Again, the regression-basedscheduler performed the worst.
Table 3
Performance when incoming traffic is higher than the available bandwidth
Performance parameter

FIFO

RR

Statistical prediction

Normalized predicted delay (unit*)

1.765

2.45

4.52

Number of loss packets during observations

214

276

5665

Further modification is then applied; instead of modelling the historical connections,regression is applied to predict the time when incoming traffic is higher than the available
bandwidth even though dough statistical processing increases delay and is still not better than
FIFO and RR (data is not shown), further applications such as backup link preparation as in
Figure 5 may utilise this prediction to enhance network performance.
Figure 5
Further work

CONCLUSION
This paper has simulated the rural internet connection bandwidth allocation using regression
methods such as linear regression, autoregression and ARIMA on historical data. The
predictions did not contribute to performance enhancement. They worsened the performances.
However, when predictions were applied to determine when a network jam happens, theperformance could improve as overflow traffic larger than the available bandwidth needs
solutions. For instance, further work may utilise the prediction results by adding more
bandwidth when traffic contention occurs.
REFERENCES
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Svistova, J., Harris, C., Fogarty, B., Kulp, C., & Lee, A. (2022). Use of telehealth amid the COVID-19 pandemic:
experiences of mental health providers serving rural youth and elderly in Pennsylvania. Administration
and Policy in Mental Health and Mental Health Services Research, 49, 530-538.
Sharma, S., Kushwaha, A., Alizadeh, M., Varghese, G., & Gumaste, A. (2023). Tuneman: Customizing Networks
to Guarantee Application Bandwidth and Latency. ACM Transactions on Internet Technology, 23(1), 126.
Kumar, M., Sharma, S.C., Goel, A., & Singh, S.P. (2019). A comprehensive survey for scheduling techniques incloud computing. Journal of Network and Computer Applications, 143, 1–33.
Beaumont, O., Legrand, A., & Robert, Y. (2022). Static scheduling strategies for heterogeneous systems.
International Symposium on Computer and Information Sciences, 18-22. CRC Press.
Wen, X., Lian, X., Qian, Y., Zhang, Y., Wang, H., Li, H. (2022). Dynamic scheduling method for integrated
process planning and scheduling problem with machine fault. Robotics and Computer-Integrated
Manufacturing, 77, 102334.
Internet traffic data sets, Kaggle,com doi: 10.34740/kaggle/dsv/5658579

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Empowering Educators

Empowering Educators: An MSc Curriculum for Industry 4.0
Vassiliadis Bill1*, Kameas Achilles2*, Panagiotakopoulos Theodor3, and Stefani Antonia4
1,2,3,4

Hellenic Open University, DAISSy Group, School of Science and Technology, Greece
bb@eap.gr, kameas@eap.gr; panagiotakopoulos@eap.gr; astefani@upatras.gr*Corresponding Author

ABSTRACT
We stand on the brink of the Fourth Industrial Revolution, often termed Industry 4.0; education
challenges evolve in tandem with the rapid advancements in technology and industrial practices.
Educating for Industry 4.0 is not merely about imparting knowledge on new technologies such as theInternet of Things (IoT), artificial intelligence, or robotics. It fosters a paradigm shift in thinking,nurtures adaptability, and cultivates the skills to integrate and navigate within hyper-connected
systems. The transition demands a reimagining of curricula, pedagogical methodologies, and the tools
we employ, all while addressing the widening skills gap and preparing learners for an evertransformative job market. As we plunge deeper into this era, the challenges of equipping individuals
with the right skills and mindset for Industry 4.0 become more intricate and imperative (Akdur, 2022).
As the landscape of Industry 4.0 broadens, educators and industry professionals alike must recognisethat integrating these advanced technologies is not just about equipment or software but about shaping
the minds that will drive this revolution forward. Fusing traditional learning with these innovativetechniques can lead to a richer, more immersive educational experience, aptly preparing learners forreal-world challenges. Further complicating the matter is the ever-evolving nature of Industry 4.0.
What is considered avant-garde today may be standard tomorrow, emphasising the importance ofcontinuous learning and adaptability. Educational institutions must foster a culture of lifelong
learning, where curricula are not static but dynamically adjust to the pulse of industry innovations.
Collaboration between academia and industry becomes pivotal, ensuring the skills taught are relevant
and in demand. Moreover, as we progress into this cyber-physical era, ethical considerations takecentre stage. The integration of AI into various sectors means decisions once made by humans now
have machine influencers. Educators will be responsible for instilling a solid ethical foundation,
ensuring that as we leverage these technologies, we focus on the greater good, prioritising humanity,
equity, and sustainability (Eriksson et al., 2021). This paper presents insights gained from designing
an MSc for Industry 4.0 Erasmus+ Project, a CBHE project that sought to align with the emerging
trend of creating a paperless educational ecosystem tailored for Industry 4.0. As the global academic
community pivots towards digitising university teaching resources, our initiative takes on a two-foldrole: curating and disseminating educational content specific to Industry 4.0. One of the primarychallenges encountered is standardising the procedures adopted in designing and developing suchspecialised content. The Industry 4.0 Master's degree aims to train Theoretical, Natural, Engineering,Computer Science, and Information Technologies graduates to address the new challenges posed by
the ever-increasing globalisation in production, manufacturing, and service provision. These
challenges are primarily rooted in technological know-how and a high degree of automation using
modern production paradigms incorporating individuality, flexibility, and reconfigurability. The
growing interconnectedness and rise in cooperation between man and machine change how products
are produced and lead to new products and services. These challenges are met by the increasingly
complex interconnections between machines, raw workpieces, finished products, transport units, andcomputers made possible by the Internet of Things, Cyber-physical Systems, Social Networks, Cloud
Computing, Big Data Analytics, and Cognitive Computing: a new industrial revolution named Industry
4.0. In this context, the Master's Programme encompasses the different sources of knowledge andexperience required by Industry 4.0. It combines the diversity of expertise of leading European
Universities. It offers education oriented to a multi-disciplinary understanding through experts from101

Empowering Educators

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complementary fields in a research-oriented environment with close cooperation with the industry.
The program is structured to provide fundamental knowledge of the discipline (core courses) andspecialisation (orientation courses) for vertical and horizontal value-creation chains of four key
industries: Manufacturing, Agriculture, Aquaculture, and Pervasive Health. These are the main
objectives of the program. The program lasts 18 months for full-time and 24 months for part-timestudents. Courses are arranged as a mix of theory and application in project-led education using a
blended mode of delivery (face-to-face and distance learning training). Placements at key industrial
players are also offered as an option at the later stages of the program. Students also can performresearch by writing a dissertation that will help them further develop the critical skills needed for a
career in Industry 4.0. The MSc in Industry 4.0 program trains students to acquire communication
skills via project and practical work. Furthermore, the need for research on innovative products andservices improves students' entrepreneurship and enterprise skills, and their intellectual property
awareness is enhanced. The curriculum and structure educate students on sustainable development,
thinking critically and effectively, and being more creative and collaborative, especially in problemsolving. Therefore, the main goal is to prepare the future workforce for all the aspects of the newindustrial revolution named Industry 4.0. Given that the project's delivery model is based on virtual
tutor presence, the digital educational material is meticulously crafted to incorporate advanced
pedagogical and e-learning techniques. This ensures that learners can grasp intricate concepts ofIndustry 4.0 even in the absence of face-to-face instruction. For a high-stakes domain like Industry
4.0, it is crucial to have educational materials that empower students to meet the pre-defined learning
objectives. Beyond process standardisation, evaluating the entire educational journey becomes
essential. This involves understanding the tutors' goal-setting mechanisms, the surrounding content
design in line with those goals, stakeholders' consumption patterns, and the overall effectiveness of thedelivery model. Current methodologies take inspiration from HOU, a pioneering institution in distancelearning. As we learned from the project, teaching skills for Industry 4.0 demands a multifaceted
approach that imparts technical knowledge and nurtures adaptability, ethics, and a problem-solving
mindset. The chosen methodologies must reflect the complexities and possibilities of thistransformative era. Given the hybrid nature of Industry 4.0, where physical and digital realms
converge, a blended learning approach—mixing traditional face-to-face instruction with online
learning—seems fitting. This might involve theory-based classroom sessions integrated with online
simulations, enabling students to grasp complex cyber-physical systems. Real-world applications andproblem-solving also drive industry 4.0. PBL mirrors this by immersing students in hands-on projects
that require solution-oriented thinking. Learners could undertake projects where they design an
intelligent factory layout or develop an AI-driven supply chain solution, fostering both teamwork andpractical skills. Adaptive Learning: Given the dynamic nature of Industry 4.0, learning must be flexible
and tailored to individual needs. Using AI-powered educational platforms, content can be adjusted inreal time based on the learner's performance, ensuring they are always engaged and challenged. One
aspect to consider when AI and automation play pivotal roles in decision-making is that the ethicalfoundation becomes paramount. Case-based discussions can be integrated, exploring dilemmas inautomation or AI bias, ensuring students can make balanced decisions in real-world scenarios. To
understand the intricacies of cyber-physical systems, students can benefit from the immersive
experiences of these technologies. VR could transport students inside a digital twin of a factory, while
AR might overlay crucial data onto physical processes, providing in-depth insights. Industry 4.0
envisions a world where humans and machines work in tandem. Preparing students for this requires
them to collaborate with AI. Assign tasks where AI provides data or suggestions, and students must
interpret, validate, or integrate this information into their projects. Industry 4.0 is inherently
interdisciplinary, merging fields like engineering, data science, and business. Curricula should bedesigned to break silos, fostering collaboration between different disciplines to encourage holistic
understanding. Industry 4.0 is a gateway to an era dominated by cyber-physical environments in theworkplace. This evolution necessitates a transformative pedagogical approach and delivery method
shift across all EQF levels. The pronounced need for robust work-based and hands-on learningcomponents within Industry 4.0 curricula bolstered this hypothesis. A central research query arises:
How do individuals acquire skills in an ecosystem where humans, machinery, and artificial intelligenceintertwine? While foundational hard skills might remain consistent with traditional curricula,integrating artificial intelligence — or the 'machine in the loop' — is poised to take on heightened102

e-Proceedings Book ICoBTA-IR4.0

Empowering Educators

significance in these cyber-physical contexts. This evolution may spotlight a range of emerging subskills, from collaborating within hybrid human-AI teams and refining communication with continuallyadvancing AI to cyber-physical ethics, resource allocation, time management in mixed teams, and risk
mitigation. While there is a broad consensus within the academic sphere regarding the core skills
essential for Industry 4.0, we advocate for an innovative approach to imparting these competencies.
This could encompass novel learning methodologies and cutting-edge mediums for educationaldelivery, potentially leveraging advancements like augmented reality, virtual reality, academic digitaltwinning, and beyond (Iniesto et al., 2021).

Keywords: Industry 4.0; Pedagogical framework; Pedagogical tools; Pedagogical methodologies;
Practicum; Employability; Evaluation; Quality assessment1031st International Conference on Business and Technological Advancement inIndustrial Revolution 4.0 (ICoBTA-IR4.0) 2023 e-Proceedings Book Industrial Revolution 4.0:
Challenges for a Sustainable Future

UNIVERSITI TEKNOLOGI MARA CAWANGAN SARAWAK(online)