bluuebunny/crossref_metadata_2025 huggingface dataset:
Additive Ratio Assessment (ARAS) Method in the Selection of Popular Mobile Games
Authors: Meidelfi Dwiny , Idmayanti Rika , Maulidani Farhan , Ilham Muhammad , Muhlis Farid Alfajr
Published: March 2024

Dealing with using smartphones was growing will increase the sales value of the mobile game industry, both from local and foreign developers. It can impact the increasing number of start-ups trying to get involved in the game industry, as well as a large number of job opportunities for those who want to be strengthened in the development of mobile games. This opportunity become the main sector in the domestic creative industry. In Indonesia, the development of mobile games was deemed necessary to analyze the most popular mobile game products that assist by criteria of game. It shows the market trend currently. The researcher hopes this result can provide for developers to choose the type of game to be strengthened based on certain categories. The mobile games involved in this research are Garena Free Fire, Mobile Legend: Bang Bang, PUBG Mobile, Higgs Domino Island, Ragnarok X: Next Generation, Rise of Kingdoms: Lost Crusade, Roblox, State of Survival: Survive The Zombie Apocalypse, Genshin Impact, Coin Monster, Minecraft, Clash Royale, Clash of Clans, Ragnarok M: Eternal Love, State of Survival: The Walking Dead Collaboration. The research was an Additive Ratio Assessment (ARAS) method with the results of 5 recommended mobile games. The test is carried out with a sensitivity test which shows the criterion of "not containing violence" which is the most sensitive criterion among other criteria.
Analysis of Reservoir Water Discharge at Solar Power Plant Tanjung Raja Village as a Basis for Pico Hydro Power Plant Planning in Paddy-Field Area
Authors: Dinata Yogi , Indriyani Indriyani , Dewi Tresna
Published: March 2024

Energy is an important aspect of life as a whole. The fossil-based energy sources have dwindled over time, in line with population growth and economic growth. One approach to meeting the energy demand is to use renewable energy. According to Presidential Regulation Number 22 of 2017 on National Energy General Plan (RUEN) targets, the share of New and Renewable Energy in total national energy in 2025 will be 25%. The construction of a Hydro Power Plant is expected to reach 3.000 MW by 2025. According to NREEC statistical data from 2016, the potential for constructing a Hydro Power Plant in South Sumatera is approximately 448 MW. This paper investigates the possibility and potential of building Hydro Power Plant from the reservoir of the irrigation water system generated by a Solar Power Plant in Tanjung Raja Village, Muara Enim District, South Sumatra. The water discharge magnitude is measured, and the potential of electricity generated from the discharge is presented. The experimental data shows that water discharge from the reservoir can generate maximum electricity ofÂ  806,6488 watts with a discharge rate of 0.0653 m3/s, and power is 36,7245 watts, with a discharge of 0,0030 m3/s. The average electricity potential is approximately 375.6782 watts, with a discharge average of 0.0304 m3/s. Therefore, the experimental data shows the possibility of hybridizing the Solar Power Plant with Hydro Power Plant, which will be beneficial for the residential area in Tanjung Raja Village, Muara Enim.
Support Vector Regression Approach for Wind Forecasting
Authors: Yamin Mohamad , Giyats Ahmad Fakhri
Published: March 2024

The government policies to fully support the G20 sustainability emphasize increased use of renewable energy. The high penetration of wind energy into power systems poses many challenges for energy system operators, primarily due to the unpredictability and variability of wind energy production. Wind power may not be provided, but accurate forecasting of wind speed and power generation helps grid operators reduce the risk of reduced electricity reliability. Accurately predicting wind speeds over 1 to 24 hours based on these conditions is important for predicting potential energy supply. These short-term forecasts are important to support wind power planning, so the required base load supply for the grid is always guaranteed (even if the wind power output fluctuates significantly). This task demonstrates that the relative forecasting performance of a support vector regression (SVR) wind forecasting system can be improved by systematically selecting and combining related input functions that affect wind speed. Shows the results of data collected in Sidrap, Indonesia, during the six months of 2019. This paper explained key methods of wind forecasting, based on the evaluation of wind speeds and wind speed prediction methods. The RMSE from the SVR shows an 8% - 9% improvement on the RMSE of the persistence forecast every 1 hour. Wind speed estimation using a support vector regression approach has the potential for further development, one of which is determining the potential location of wind-based renewable sources and Wind Energy Conversion System (WECS) can make more efficient.
Development and Evaluation of a Cloud Computing Adoption Framework (CCAFF) for Retail Banks in Bahrain
Authors: Adwan Ehab Juma , Alsaeed Bader Ali
Published: March 2024

The Information Technology era supports the adoption of Cloud Computing (CC) on a large scale in many business fields, at which the Financial Institutions (i.e., Banking sector) is not an exception. Several gaps were found during an attempted literature reviewing. While several CC methods, strategies, or frameworks have been proposed or utilized for CC adoption in many economic sectors including the Financial institutes (FI)- Banking, it is still a relatively new initiative in many other countries including Bahrain. While Enterprise Architecture (EA) is claimed to be a leading information and business management discipline to develop an architecture that guides the transformation to CC adoption of an enterprise from a baseline state to a target state. However, there is an evident lack of scholarly articles on the development of CC adoption frameworks from EA perspective. Also, it was found that scholarly articles in the evaluation of CC adoption framework are scarce. To address those gaps, this paper aims at developing and evaluating a CCAFF for the CC adoption in Bahraini Banks by adapting a tailored version of the Open Group Architecture Framework (TOGAF) and embedding a tailored version of an enterprise cloud adoption strategy (ECAS) and then evaluating the CCAFF based on several criterions. A six phased Design Science Research Methodology (DSRM) is employed to design the research report, guide, and develop the CCAFF, while the exploratory nature of the research necessitates the employment of a holistic single Case Study strategy for an FI-01 bank, based on semi structured interviews and document analysis data collection techniques. Alongside, the data was analyzed using pattern matching technique. For the CCAFF evaluation, Delphi technique was employed at which seven experts in two rounded panels contribute to the evaluation findings based on six criterions. The evaluation findings demonstrated promising results in terms of ease of use (86.4%), usefulness (84.6%), decision making support (86.6%), comprehensiveness (85.8%), time efficiency (84.8%), and usage intention (84.8%).
Renovation of Crane Control System of Reach Stalker Ferari 178h1 Using Avr Atmega2560
Authors: Markis Lince , Wardana P.Susetyo , Syawaldipa Ardi
Published: March 2024

Container rental service companies or container parking facilities providers need container transportation equipment such as Crane Reach Stalker to move and arrange containers. Several container services companies in Surabaya such as Citra Mandiri Sejati have a Crane Reach Stalker made by Ferari Italy which experienced damage to the electrical control. After observing the control unit, the damage was in the hardware comparison system and the relay control used. Dealing with these problems, planning and improvement of the total control system were carried out using the ATMega2560 AVR microcontroller. However, the problem becomes complex and requires cooperation with Hydraulic control partners when the entire system must be resolved. From the renovation work on the Ferari 178H1 Load Handling Crane system for a year, it was obtained that the Load Handling control input data in which it was a joystick and a 3 positions button with an output level of 0 and 9 volts. Hydraulic control uses a Solenoid Valve type 4/3 with 2 input control coils, and in overall system testing, the average length of time for the movement of the Piston Crane is 6 to 12 seconds. From the test results using a 40 tons container load, it was stated by the Crane driver that the time used to carry out Load Handling for the Hydraulic type and the Crane's age was suitable and appropriate. Â
Classification of Finger Movements Using EMG Signals with PSO SVM Algorithm
Authors: Pamungkas Daniel Sutopo , Risandriya Sumantri K , Rahman Adam
Published: March 2024

Electromyography (EMG) is a signal produced by human muscles when they contract or relax. This signal is widely used as a controller, for example, to control a robotic arm. This study aims to identify the pattern of finger movement in the form of finger movement using a bracelet-shaped device that has eight EMG sensors. This tool is placed on the lower right hand of a subject to get a signal from the EMG. This study uses the support vector machine (SVM) algorithm combined with the particle swarm optimization (PSO) method. For pattern recognition, the properties of the signal in the time domain are used. From this system, the success of pattern recognition is between 68% to 86%.
Enhancement Support Vector Regression Using Black Widow Optimization for Predicting Foreign Exchange Rate
Authors: Bhagaskara - , Negara Edi Surya
Published: March 2024

Prediction of foreign exchange rates is one of the time series problems that have fluctuating value movements. There are several algorithms that can make predictive models for this problem, one of which is Support Vector Regression (SVR). In this study, foreign exchange rate predictions were made using Hybrid SVR and Black Widow Optimization (BWO). This is done with the aim of improving the performance of the SVR in order to produce a better predictive model for the EUR/USD foreign exchange rate data in 2020. The results of the proposed algorithm get better performance in terms of R2, MSE, RMSE, MAE, and MAPE compared to SVR.
Caribi Mobile Application Based on Radio Frequency Identification (RFID) for Internet of Things (IoT)
Authors: Faridah Linda , Rahayu Andri Ulus , Shopa Rahmi Nur , Sulastri Heni , Hiron Nurul , Nursuwars Firmansyah M S
Published: March 2024

Nowadays, the e-commerce industry in Indonesia is growing substantially and expanding annually. This is one of the revolutions that has affected various aspects of life, including animal husbandry. In Indonesia, buying and selling livestock has become routine; however, it is still conducted conventionally. Therefore, a system is required to facilitate online purchasing and selling transactions; thus, farmers can expand their enterprises. This study aimed to develop a mobile application for the online purchase and sale of livestock. The Caribi Mobile Application is built with a system that can provide consumers with accurate information by recording the tracks of each animal that will be sold. Each animalâ€™s age, weight, and recent pictures are included. Tag RFID-based Internet of Things (IoT) system is used in searching for mobile applications. The RFID Tag System is used to carry out two functions at once, namely assistance and security. This application is designed to integrate livestock data from the internet and save it in a database. This studyâ€™s findings demonstrate that the system can read the data related to each farm animal. Additionally, the system has created data on the weights of each animal, which anyone with access to the Caribi Mobile Application can view.
Durability Performance Analysis of Mixture Asphalt Concrete - Base Course (AC-Base) Using Coal Fly Ash as A Filler Substitute
Authors: Archenita Dwina , Alkhairi Wafiq , Rizki Aditiya , - Apwiddhal , - Satwarnirat
Published: March 2024

Highways are land transportation infrastructure whose needs in Indonesia continue to increase, along with the increasing number of vehicle. The top Laston or base coarse (AC-Base) is a pavement foundation consisting of a mixture of asphalt aggregates with a certain ratio mixed and compacted in a hotmix. This coarse is at under binder coarse (AC-BC), not directly related to the weather, but it needs to have stability to withstand the traffic load spread through the vehicle wheels. Base coarse (AC-Base) serves to provide support the surface coarse, reduce strain and stress, spread and continue the load of road construction to the layer below (subgrade). Filler as one of the materials forming the AC-Base layer usually obtained from stone ash whose availability is increasingly difficult to feel in the field implementation. Therefore, a substitute material or substitute material is needed for the filler. One possible substitute material is fly ash. This research will analyze the performance of AC-Base coarse that make use of filler substitution material using fly ash with variation FA-0%, FA-5%, FA-7.5%, FA-10%, FA-12.5% and FA-15%. The samples made by Marshall Method will give the Optimum Asphalt Content (OAC) value on each mix variation. While the performance of this AC-Base mixture in increasing density, durability, and stability in the pavement mixture using the Marshall Immersion method (MI). The analysis result shows that the OAC value at 0% fly ash variation is 6,82%, at 5% fly ash variation is 6,87%, at 7.5% fly ash variation is 6,85%, at 10% fly ash variation is 6,84%, at 12.5% fly ash variation is 6,82%, and at 15% fly ash variation is 6.79%. While the performance of AC-Base mixture indicated by the MI value meets the specifications, and the MI value obtained tends to increase along with the increase in the percentage of fly ash. The mixture with 15% fly ash shows the better durability performance (99,75%) compared to other variations 0%, 5%, 7,5%, 10% and 12,5% (96,44%, 97,51%, 97,71%, 98,36% and 99,54%).
Automatic Water Level and Pressure Control System Prototype Design Using Programmable Logic Controller and Human Machine Interface
Authors: Hajar Ibnu , Damiri Dhami Johar , Sitorus Meyharth Torsna Bangkit
Published: March 2024

Water level control and pressure control are implemented in many at several industrial processing such as water dam, petrochemical processing plant, pharmacy and food processing plant, overhead tank, waste processing plant, electricity plant, etc. This study analyses a simulation and a needed components for automatically water level control system implementation using programmable logic controller (PLC). The water level control using PLC is designed to control the water level and prevent waste the water from a water tank. PLC is one of devices of automated controller which mostly used. Use of PLCs in industries are indispensable to control parameters which required high precision. In this research studies water level control system using PLC as primary controller and human machine interface (HMI) as remote controller that could visualize state of a system work (plant) in real time on computer screen and could gather field data as well. Main device for this research is Omron PLC CP2E type and touchscreen HMI Omron. At the prototype is used a water tank and a water container (to be one with the water resource), and in the HMI is showed the pages of home, control panel, and plant. From the result of the diagram ladder and the HMI design are got the system run well. The application from this research could be learning media in the system control laboratory for the subject of industrial control system and the others, related to it.
Impact the Classes’ number on the convolutional neural networks performance for image classification
Authors: Ali Amna Kadhim , Abdullah Abdulhussein Mohsin , Raheem Sabreen Fawzi
Published: March 2024

Deep learning was developed as a realistic artificial intelligence technique that takes in numerous layers of information and produces the best results in various classes. Deep learning has demonstrated excellent performance in several areas, particularly picture grouping, division, and recognition. The convolutional neural network (CNN) is one of the algorithms that relies on deep learning in its work. It has proven its effectiveness in classifying images with high efficiency in medical images and their diagnoses, face recognition, and other different fields. In this paper, the focus was on images to alert new researchers to their effects on the performance of CNN in terms of the number of classes that existed within the database, in addition to the impact of incorrect classification of images by the source on the classification result and the necessity of adopting reliable and correct sources of data to avoid inaccurate results. A group of face images has been used, and three experiments on them were conducted using all existing classes with reduction. The results showed a significant improvement in the performance of the algorithm whenever the number of classes was reduced. The best result was when only two classes were chosen for classification, reaching a validation accuracy of 85%.
Leveraging Social Media Networks' Impact on Technopreneurship
Authors: Isa Khairunesa , Palpanadan Sarala Thulasi , Saparudin Intan Farhana , Zainol Nur Zainatul Nadra , Syahrudin -
Published: March 2024

Technology has become an integral part of society, substantially contributing to its many facets. With the advent of technology, media entrepreneurship in Malaysia has expanded significantly. The younger generation, specifically Generation Z, relies heavily on social media for communication, information inquiry, and online purchasing and selling. This change has had a significant impact on the Malaysian field of technopreneurship. Therefore, the purpose of this study is to examine the relationship between the factors that influence Generation Z's use of social media for business and their propensity to engage in technopreneurship. This is a quantitative study that uses a questionnaire to collect demographic information, factors influencing Generation Z to use social media for business, and their motivations or aspirations to partake in technopreneurship activities. 280 respondents from Generation Z technopreneurs were selected through random sampling. The software Statistical Package for the Social Sciences (SPSS) was used to perform a descriptive analysis of the obtained data. According to the study's findings, each factor has a significant relationship with the motivation to engage in technopreneurship
Identification of Bioethanol Quality for Motorcycle Fuel
Authors: Angraini Tuti , Aidha Zas Ressy , Anton - , Kurniadi Dedi , Prabowo Cipto
Published: March 2024

The availability of crude oil as a raw material for vehicle fuel is dwindling and limited in nature. One of the renewable energies worth developing is bioethanol, which is one of the alternative fuels that can be used as a biofuel and can be processed from plants containing starch and glucose. In this research, the entire bioethanol identification system in a sugar cane drip distillation apparatus was examined. The distillation process using MQ3 and MQ135 sensors resulted in an alcohol percentage of 42% and 46%. The maximum temperature measured by a thermocouple during distillation was 88°C, while the minimum temperature recorded was 87°C. This study utilized a backpropagation artificial neural network method to identify the detected bioethanol. The architecture of the artificial neural network included 2 input nodes, 4 neuron nodes, and 2 output nodes. The training and testing results showed that the formed backpropagation was able to identify and differentiate the detection of bioethanol according to the given inputs with a success rate of 88.86% for detected bioethanol and 94.86% for undetected bioethanol..
Design and Validation of Structural Causal Model: A focus on SENSE-EGRA Datasets
Authors: Ayem Gabriel Terna , Nsang Augustine Shey , Igoche Bernard Igoche , Naankang Garba
Published: March 2024

Designing and validation of causal model correctness from a dataset whose background knowledge is gotten from a research process is not a common phenomenon. In fact, studies have shown that in many critical areas such as healthcare and education, researchers develop models from direct acyclic graphs without testing them. This phenomenon is worrisome and is bound to cast a dark shadow on the inference estimates that many arise from such models. In this study, we have design a novel application-based SCM for the first time using the background knowledge gotten from the American university of Nigeria (AUN), Yola, on the letter identification subtask of Early Grade reading Assessment (EGRA) program on Strengthen Education in Northeast Nigeria (SENSE-EGRA) project dataset, which was sponsored by the USAID. We employed the conditional independence test (CIT) criteria for the model’s correctness validation testing, and the results shows a near perfect SCM.
Capacity Building for Farming System Digitalization Using Farming Management System
Authors: Hidayat Rahmat , Amnur Hidra , Alanda Alde , Yuhefizar  , Satria Deni
Published: March 2024

Agriculture is one of the most important areas of human activity around the world. As the population increases, it is necessary to increase agricultural production. In the age of information technology, information plays a key role in people's lives. Agriculture is rapidly becoming a highly development-intensive industry where farmers need to collect and evaluate a large amount of information in their business processes to become more efficient in production and communicate information accordingly. Modernizing agriculture requires technological know-how for the efficient use of agricultural inputs. It deals with factors such as ecological footprint, product safety, labor welfare, nutritional responsibility, plant/animal health and welfare, economic responsibility and local market presence. They cover almost all stages in the production chain concerning day-to-day agricultural tasks, transactional activities for all stakeholders involved, and support for information transparency in the food chain. The use of information technology and network infrastructure currently enables the application of technology in agricultural business processes, but there is no standardized solution to enable interoperability and integration among services and stakeholders. Farming Management System (FMS) is expected to be a solution and standard in the use of technology in agriculture. Farming management system is a management system specifically designed to assist farmers or farm managers in managing their farming operations more efficiently and effectively. This system usually consists of integrated software and hardware to monitor and collect data from various aspects of agriculture, such as irrigation, fertilization, pesticide spraying, etc.
Visual Branding of Sikapak Timur Village Pariaman as Media Promotion Based on Visual Complexity Effect on Social Media
Authors: Firosha Ardian , Gusman Taufik , Sumema Sumema , Rotama Hendra
Published: March 2024

Visual Complexity Effect is a term used in psychology to describe how people react to complex visual information. Typically, the more complicated or complex the visual information, the more difficult it is for people to process and understand it. The effects of visual complexity can also affect reaction times, cognitive abilities, and decision making. In everyday life, data visualization and complex graphs can be a challenge for many people, especially if they are not used to that type of information. Therefore, graphic designers and application developers try to minimize visual complexity so that it can be understood and used easily by users.
COVID-19 Social Distancing Tracking and Monitoring System (SDMOS-19)
Authors: Binti Abdullah Nurafrina Arrysya , Kamarudin Nur Diyana Binti , Makhtar Siti Noormiza , Mat Jusoh Ruzanna , Alanda Alde
Published: March 2024

The Coronavirus disease (COVID-19), stemming from the SARS-CoV-2 virus, has garnered global concern as a virulent infectious ailment. Recognized as an epidemic by the World Health Organization (WHO), the persistently mutating virus sustains its transmission within communities. Individuals have been advised to uphold a safe interpersonal distance, notably around five feet, to mitigate its spread during social interactions. Addressing this imperative, an innovative automated social distancing detection system is conceived, leveraging the Convolutional Neural Network (CNN) algorithm. This system operates on two distinct input modes: static images and recorded videos recorded on closed-circuit television (CCTV). Remarkably, the proposed automated system adeptly quantifies and surveils the extent of social distancing among individuals in densely populated settings. A sophisticated framework accurately discerns social distancing compliance, delineating between hazardous and secure intervals via distinct red and green bounding box indicators. The culmination of this endeavor reveals an impressive 90% detection accuracy for both input modes. Notably, this proposed system holds substantial promise for implementation within sprawling premises such as expansive shopping malls or recreational parks. Seamlessly enforcing automated safety distance assessment expedites real-time insights to security departments and other relevant authorities. Consequently, the efficacy of citizens in upholding safe interpersonal distances can be promptly evaluated and, if necessary, corrective measures can be expeditiously instituted. This automated system ensures public health and safety maintenance, particularly in difficult circumstances.
Architectural Visualization Approach Using Google SketchUp and Lumion on the Development of Maritime Tourism
Authors: Akib Haedar , Ismail - , Rahman Edi Suhardi , Haedar Ahmad Wahidiyat , Saleh Sirajuddin , Rizal Muhammad
Published: March 2024

This study aims to portray the strategy and design of marine tourism development through a data visualization approach using Google SketchUp and Lumion. The type of research conducted is Research and Development (R&D), employing the ADDIE approach (Analysis, Design, Development, Implementation, and Evaluation). The findings of this study reveal that the miniature design of Pulau Sembilan on Larea-rea Island incorporates local cultural themes in the construction of Rulan (Nine House), with a visualization of the roof following the design of the traditional Karampuang house. Additionally, the researcher presents designs concerning the jetties of Larea-rea Island and the product facility designs. Ultimately, contributes to development of Rulan (Nine House) and the efficient land utilization contributes to the region's income enhancement in the tourism sector of Larea-rea Island. It is recommended that future research on marine tourism development should involve a more comprehensive analytical framework that considers social, economic, and environmental aspects. This will help in understanding the overall impacts of marine tourism development. It is also important to investigate the ecological impacts of tourism and come up with effective mitigation strategies to prevent harm to the marine environment. Another interesting research area is to integrate the perspectives of local communities and their participation in the planning and implementation of tourism development. This will ensure the sustainability of such projects and gain community acceptance.
Reducing Cognitive Bias of Pre-Service History Teachers through Augmented Reality
Authors: Karima Elfa Michellia , Nurlizawati Nurlizawati , Firza Firza , Abidin Nur Fatah , Ibrahim Yusuf
Published: March 2024

Cognitive biases can be problematic and dangerous in history learning. This study aimed to identify the extent to which the independent variables of the case study method and augmented reality influence the dependent variables, as well as evaluate the strength and direction of the relationship between these variables in reducing the cognitive biases of pre-service history teachers. The method used is multiple linear regression to identify the extent to which the application of the case method and the use of augmented reality in learning affect the dependent variable under study. The results showed that augmented reality contributed to the understanding of the history of prospective pre-service history teachers more than the case study method. The effect of the case study method was 7.6% on historical knowledge, and augmented reality media had a 13.9% effect on historical experience. Lecturers can use augmented reality in learning for prospective pre-service history teachers to increase student understanding of history learning material and reduce cognitive biases. This research has implications for using technology and digitalization in history learning for prospective pre-service history teachers to understand history, conceptions, and past events and reduce bias. Understanding history is essential for prospective pre-service history teachers. Prospective pre-service history teachers must also understand a historical event broadly and from various perspectives. Technology-based learning in history learning is one of the right ways to avoid cognitive bias.
Coordination of The Apprenticeship Industrial Program with The Siakama Application
Authors: Yustisia Henny , Andreas Laras Oktavia , Apdeni Risma , Heriyadi Bambang , Weriza Jusmita
Published: March 2024

This research aims to examine the implementation of the SIAKAMA application in the Apprenticeship Industrial Program. This program was created as a SIAKAMA application to overcome hurdles during the monitoring and evaluation stages. At the monitoring stage, supervising lecturers and field supervisors can use the SIAKAMA application to monitor all Apprenticeship Industrial program student activities in the field, resulting in a good and smooth communication and coordination system. At the evaluation stage, the supervising lecturer and field supervisors in the SIAKAMA application can conduct assessments based on student activities in the field, including daily evaluations and final assessments after the Apprenticeship Industrial Program has been finished. This study employs a quantitative descriptive technique, the Research & Development method, and the 4D development model. A sample of Apprenticeship Industrial Program students from five departments of the Faculty of Engineering, Padang State University, was used in this study. The SIAKAMA application was found to be valid with a value of 0.876, practical with a value of 78.67, and effective with a value of 81.22% after data analysis using SPSS 25. This suggests that implementing the SIAKAMA application to enhance the work competency of Apprenticeship Industrial Program students is viable. The Apprenticeship Industrial Program model represents a modification of the Three Set of Actor development model, yet it hasn't been incorporated with the Industrial Revolution 4.0. Engaging in this Program enables students to acquire 4C skills, including Creativity and Innovation, Critical Thinking and Problem Solving, Communication, and Collaboration.
Comparative Analysis of Machine Learning Algorithms for Health Insurance Pricing
Authors: Bau Yoon-Teck , Md Hanif Shuhail Azri
Published: March 2024

Insurance is an effective way to guard against potential loss. Risk management is primarily employed to protect against the risk of a financial loss. Risk and uncertainty are inevitable parts of life, and the pace of life has led to a rise in these risks and uncertainties. Health insurance pricing has emerged as one of the essential fields of this study following the coronavirus pandemic. The anticipated outcomes from this study will be applied to guarantee that an insurance company's goal for its health insurance packages is within the range of profitability so that the insurance company will also choose the most price-effective course of action. The US Health Insurance dataset was utilized for this study. This health insurance pricing prediction aims to examine four different types of regression-based machine learning algorithms: multiple linear regression, ridge regression, XGBoost regression, and random forest regression. The implemented model's performance is assessed using four evaluation metrics: MAE, MSE, RMSE, and R2 score. Random forest regression outperforms all other algorithms in terms of all four evaluation metrics. The best machine learning algorithm, random forest, is further enhanced with hyperparameter tuning. Random forest with hyperparameter tuning performs better for three evaluation metrics except for MAE. To gain further insights, data visualizations are also implemented to showcase the importance of features and the differences between actual and predicted prices for all the data points.
Fuzzy Soft Set Clustering for Categorical Data
Authors: Yanto Iwan Tri Riyadi , Apriani Ani , Wahyudi Rofiul , WaiShiang Cheah , Suprihatin - , Hidayat Rahmat
Published: March 2024

Categorical data clustering is difficult because categorical data lacks natural order and can comprise groups of data only related to specific dimensions. Conventional clustering, such as k-means, cannot be openly used to categorical data. Numerous categorical data using clustering algorithms, for instance, fuzzy k-modes and their enhancements, have been developed to overcome this issue. However, these approaches continue to create clusters with low Purity and weak intra-similarity. Furthermore, transforming category attributes to binary values might be computationally costly. This research provides categorical data with fuzzy clustering technique due to soft set theory and multinomial distribution. The experiment showed that the approach proposed signifies better performance in purity, rank index, and response times by up to 97.53%. There are many algorithms that can be used to solve the challenge of grouping fuzzy-based categorical data. However, these techniques do not always result in improved cluster purity or faster reaction times. As a solution, it is suggested to use hard categorical data clustering through multinomial distribution. This involves producing a multi-soft set by using a rotated based soft set, and then clustering the data using a multivariate multinomial distribution. The comparison of this innovative technique with the established baseline algorithms demonstrates that the suggested approach excels in terms of purity, rank index, and response times, achieving improvements of up to ninety-seven-point fifty three percent compared to existing methods.
Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy
Authors: Nguyen Tien Han , Paramasivam Prabhu , Dong Van Huong , Le Huu Cuong , Nguyen Duc Chuan
Published: March 2024

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.
Software Agent Simulation Design on the Efficiency of Food Delivery
Authors: Ismail Shahrinaz , Mostafa Salama A , Baharum Zirawani , Erianda Aldo , Jaber Mustafa Musa , Jubair Mohammed Ahmed , Adiya M. Hasmil
Published: March 2024

Food delivery services have gained popularity since the emergence of online food delivery. Since the recent pandemic, the demand for service has increased tremendously. Due to several factors that affect how much time additional riders spend on the road; food delivery companies have no control over the location or timing of the delivery riders. There is a need to study and understand the food delivery riders' efficiency to estimate the service system's capacity. The study can ensure that the capacity is sufficient based on the number of orders, which usually depends on the number of potential customers within a territory and the time each rider takes to deliver the orders successfully. This study is an opportunity to focus on the efficiency of the riders since there is not much work at the operational level of the food delivery structure. This study takes up the opportunity to design a software agent simulation on the efficiency of riders' operations in food service due to the lack of simulation to predict this perspective, which could be extended to efficiency prediction. The results presented in this paper are based on the system design phase using the Tropos methodology. At movement in the simulation, the graph of the efficiency is calculated. Upon crossing the threshold, it is considered that the rider agents have achieved the efficiency rate required for decision-making. The simulation's primary operations depend on frontline remotely mobile workers like food delivery riders. It can benefit relevant organizations in decision-making during strategic capacity planning.
Building a measure of legal knowledge for selecting talented people and its relationship with some basketball shooting skills
Authors: أحمد نجم عبدالله، خالد محمود روؤف،  حمود نجم حسن
Published: March 2024

The research aims to build a measure of legal knowledge for selecting talented people and to identify the relationship of legal knowledge to some of the basketball shooting skills of the players of the Sports Talent Centers in Iraq affiliated to the Ministry of Youth and Sports. As well as getting to know the level of legal (theoretical) knowledge in international law (2020) they have، and the researchers assumed that there are statistically significant differences between the acquisition of legal knowledge for the selection of talented people and some basic skills in basketball. The researchers used the descriptive approach in both survey and correlational methods for their suitability.The research community is represented by the players of the Sports Talent Centers in Iraq .affiliated to the Ministry of Youth and Sports، which number (7) centers (Baghdad، Diyala، Hilla، Diwaniyah، Najaf، Karbala، Salah al-Din (Tuz Khurmatu)) and by (140) emerging players in all centers As for the research sample (the building sample)، it consisted of (130) players after excluding the reconnaissance sample، which numbered (10) players، and thus the sample became (100%) of the total research community. The researchers used the following statistical methods: the arithmetic mean، standard deviation، percentage، ease factor، difficulty factor، and coefficient of distinction By looking at the research results، the researchers reached the following conclusions:.The legal knowledge test showed the players' true legal experiences.1- 2- There is a correlation with positive statistical significance between legal knowledge and some basketball shooting skills ..In light of the conclusions، the researchers reached a set of the following recommendations : 1- Approving the legal knowledge test for basketball players and disseminating its results ..2-Increasing legal knowledge awareness in centers for sports talent in Iraq . 3- Conducting annual cognitive tests for the athletes of the Sports Talent Centers by coaches and addressing their weaknesses as they are the cornerstone for the growth and prosperity of sport in the country، because knowledge is the weapon of progress .
The most important physiological variables for selecting the cubs of the specialized schools (Sports Talent Center) in football
Authors: عبدالله عباس محمد،  حسين حمزه نجم، صفاء حاتم جليل
Published: March 2024

In order to achieve the objectives of the research, represented in selecting the cubs' players according to the most important physiological variables of football in the specialized schools for the 2018-2019 sports season, the researchers had to take a set of measures for the purpose of knowing the factors that saturated in the battery, and for this the descriptive method was used and the research sample determined the players The football cubs number (30) players representing different specialized schools. The statistical bag (spss) has been used to process statistical data after taking the tests, and in light of the results, the following conclusions were reached:1- In light of the specific measurements and tests, the young football players were selected.2-By applying the inferred equations to the cubs' players, taking into consideration the standard scores, a selection process was carried out for the distinguished ones.
Uncovering Language in Business Communication Space: Variation between Formal and Informal Language in Mandailing Natal
Authors: Junita Irawati
Published: March 2024

This research delves into the intricate dynamics of formal and informal language usage within the realm of business communication in Mandailing Natal. Mandailing Natal, characterized by its cultural and linguistic diversity, serves as a distinctive backdrop for comprehending the nuanced role of language in business interactions. Employing a method of language variation analysis, this study scrutinizes various business communication texts, including formal letters, emails, and face-to-face conversations. The objective is to unravel the disparities in the utilization of formal and informal language. The findings of the research reveal that within the business communication space, the deployment of formal and informal language not only mirrors organizational hierarchy but is intricately linked to the local cultural context. Formal language predominantly governs official documents and written communication, while informal language prevails in face-to-face interactions and everyday situations. Furthermore, language variation extends to the incorporation of local dialects and distinctive expressions unique to Mandailing Natal, contributing cultural richness to business communication.
The Role of Communication and Employee Engagement in Promoting Inclusion in the Workplace: A Case Study in the Creative Industry
Authors: Afridah  , Muhlisah Lubis
Published: March 2024

This research examines the role of communication and employee engagement in promoting inclusion in the workplace, with a specific focus on the creative industry. The study utilizes a case study approach to gain insights into the strategies and practices employed by organizations in fostering an inclusive work environment. The research objectives are twofold: first, to explore the communication channels and mechanisms utilized by organizations to promote inclusion, and second, to investigate the impact of employee engagement on fostering a culture of inclusion. A mixed-methods approach is employed, combining qualitative interviews with employees and management, as well as quantitative surveys to gather comprehensive data. The findings of the study highlight the crucial role of communication in promoting inclusion. Organizations within the creative industry employ diverse communication channels, such as town hall meetings, intranet platforms, and diversity training programs, to facilitate open dialogue and exchange of ideas. Effective communication practices contribute to enhanced employee understanding, collaboration, and mutual respect, thereby fostering an inclusive work environment. Furthermore, the study reveals that employee engagement plays a pivotal role in promoting inclusion. Engaged employees exhibit higher levels of commitment, motivation, and willingness to embrace diversity. They actively contribute to the creation of an inclusive culture by participating in diversity initiatives, providing feedback, and advocating for inclusive practices. The implications of this research that organizations in the creative industry should prioritize effective communication strategies and employee engagement to foster inclusion. By creating an inclusive work environment, organizations can harness the benefits of diversity, such as increased creativity, innovation, and employee satisfaction.
Factors affecting elementary school principals’ management function: the case of Patikul District, Ministry of Basic, Higher and Technical Education-Sulu
Authors: Awaluddin Rasul , Sabdani-Asiri Masnona
Published: April 2024

The study investigated the factors that influence the management functions of elementary school principals among elementary school teachers in Patikul East District, Ministry of Basic, Higher, and Technical Education-Sulu, during the school year 2023-2024. The researchers collected data from 100 participants using non-probability sampling method called purposive sampling. They used statistical analysis techniques such as weighted mean, standard deviation, t-test for independent samples, one-way ANOVA, and Pearson’s r to analyze the data. Here are the key findings of the study: 1. Most of the teachers in the sample were female, married, and aged between 30 years and below or 41-50 years old. They mostly held bachelor’s degrees and had 26 years or more of teaching experience. 2. On average, the management functions of elementary school principals were moderately affected by communication, leadership styles, and decision-making processes. 3. The variables of age, gender, civil status, educational attainment, and length of service did not significantly mediate the assessment of elementary school teachers regarding the extent of elementary school principals’ management functions in terms of communication, leadership styles, and decision-making processes. 4. The group of teachers who assessed the extent of factors affecting elementary school principals’ management functions in Patikul district, Ministry of Basic, Higher, and Technical Education in Sulu, found that decision-making was of moderate extent. This group of teachers also assessed communication and leadership styles as moderate extent. 5. The study supports Henri Fayol’s administrative management theory, which emphasizes the importance of proper planning, organizing, commanding, coordinating, and controlling in the management functions of a principal in an educational setting.
Hvordan kan digital dokumentation blive en vej til sammenhæng i borgerforløb?
Authors: Jensen Julie Duval
Published: March 2024

Artiklen bygger på et phd.-studie og en efterfølgende workshop med kommunale
sundhedsprofessionelle. Studiet har undersøgt digital dokumentationspraksis i sundheds- og
ældreområdet i en
dansk kommune. Studier viser, at der er mange fordele ved digital dokumentation.
Adgang til information i dokumentationssystemer understøtter behandling og ydelser af høj
kvalitet,
foruden at det sikrer patientsikkerheden ved at reducere medicinfejl.
Imidlertid peger forskning også på, at digital dokumentation kan have utilsigtede effekter, og
at
implementering af teknologi er udfordrende. I en dansk kontekst er det dog uvist, hvordan
digital
dokumentationspraksis påvirker sammenhæng i borgerforløb.