bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classical and Deep Learning Time Series Prediction Techniques in the Case of Indonesian Economic Growth
Authors: Bunyamin Hendra , Meyliana  
Published: March 2021

Abstract
Gross Domestic Growth (GDP) as a proxy of an economic growth in one country is defined as the market value of services and goods produced by the country for a year. As the GDP of one country gets higher and higher through the years, this indicator points out that in general the economic growth of the country is growing; specifically, the values of services and goods accumulated in the market are increasing. To determine what indicators of a country that affects one’s economic growth remains an open question. Therefore, this research attempts to study those indicators and particularly utilize them to predict the economic growth. To answer those questions, this research employs diverse time series techniques ranging from classic time series analysis to machine learning and deep learning. Subsequently, our dataset comprises World Development Indicators (WDI) of Indonesia from 1962 to 2016. By measuring Root-Mean-Square Error (RMSE), we show Seasonal Autoregressive Integrated Average (SARIMA) and Convolutional LSTM give the the best performance from classical and deep learning techniques respectively. Our analysis shows SARIMA’s performance is boosted by its ability to capture trend and seasonality of the dataset; equally important, Convolutional LSTM equipped with convolutions as part of reading input into LSTM units significantly boost the performance over either Convolutional or LSTM networks. Furthermore, our analysis points out that the indicator which most strongly contribute to predict GDP is CO<sub>2</sub> emission. This result agrees to the fact that some countries with high CO<sub>2</sub> emission also has high GDP as well; also this finding should warn Indonesian government of the increasing CO<sub>2</sub> pollution.
Classifying Medical Document in Bahasa Indonesia using Semi-Supervised Learning
Authors: Fudholi Dhomas Hatta , Juwairi Kiki Purnama 
Published: March 2021

Abstract
The medical domain has always been an all-time important domain since healthiness is everyone’s purpose. People find medical document resources in the sea of data and information, such as the web. To support information retrieval and knowledge dissemination through the web, we analyze the use of semi-supervised learning to classify medical-related documents. The semi-supervised learning technique is chosen to show the possibilities of creating good classifiers with limited human supervision. In this research, we use the Naïve Bayes and Pseudo Labeling technique. We analyze different labeled:unlabeled data ratios of the training dataset in the experiment, starting from 4:3, 3:4, 2:5, and 1:6, to see the semi-supervised learning performance with different levels of human supervision. We get a relatively similar result in terms of classification average accuracy (81%-83%). Interestingly, in one experiment, the highest accuracy of the 1:6 ratio (85%) outperforms the 2:5 ratio (82%) and has the same accuracy as the 4:3 (85%). However, the standard deviation of the accuracy in the 1:6 ratio is the highest, amongst others (4.183). Finally, semi-supervised learning can be used to create a great classifier model of the medical domain in Bahasa Indonesia with less human supervision.
Analysis and Designing Enterprise Architecture of PT. Adigas Jaya Pratama on Sales and Service Function Using TOGAF Framework
Authors: Rachmandany Alfian Mulya , Utama Rd. Lugina L. , Lubis Muharman , Ambarsari Nia 
Published: March 2021

Abstract
PT. Adigas Jaya Pratama is a company that distributes various products of Pertamina, which focus on delivering LPG (Liquefied Petroleum Gas) to the distributors. There are still various problems occurred in term of service level agreement (SLA), which in many case merely based on promise, while at the same time there are also the incapability of monitoring and checking for updating items in the inventory and the storages. Meanwhile, the process of handling customer complaints often causes the decreasing in the level of satisfaction and loyalty to the services provided. To overcome these problems, the solution made must take explicit concern to the harmony between business and information technology aspects. It is necessary to design an enterprise architecture (EA) with the TOGAF framework to provide a suitable blueprint for the development of sales and service function that can present respective ability for the company to have well performance leading to the growth and profit. This research provide the respective artefacts from every design stage including preliminary phase to the migration planning, which is expected to be applied in accordance with the business needs of the company to provide solutions for the existing problems.
The Implementation of User Centered Design Methods on Public Service Mapping Websites
Authors: Sasmito Ginanjar Wiro , Fikri Hidayattullah M. 
Published: March 2021

Abstract
Public services are basically held to increase the level of community satisfaction. To achieve the satisfaction, a city / country will be demanded the quality of professional public services. One of them is the city of Tegal. Tegal is one of the cities in Indonesia. In order to implement a smart city, the city government of Tegal strives to provide services to the public quickly, effectively and efficiently. The public services of Tegal City Government have been running quite well, but information on services has not been integrated between Regional-Owned Enterprises (BUMD) and the private sector. Therefore a website-based mapping media was created in the form of clustering of Tegal city public services, both organized by the government, BUMD and the private sector are integrated each other and can be accessed and used easily by the people of Tegal city. This website can provide the closest route and is equipped with a comparison chart of public service categories in the city of Tegal. This application was built with the User Centered Design method so that it can be adjusted to the needs of the user and speed up the completion time, which only takes 105 days or 15 weeks. The results of this mapping website test were carried out by using Black Box Testing with the equivalence partitions method, which is the effectiveness value for all ID Test results is 83%, and the user test results are carried out by using the Usability Testing method that applied the SUS scale, with a value of 89% or excellent.
Acquiring Commonsense Knowledge during Collaborative Storytelling
Authors: Dy Jilyan , Brito Celina , Tan Vincent , Lola Julius , Ong Ethel 
Published: March 2021

Abstract
Conversational agents that engage children in collaborative storytelling need a collection of domain knowledge to formulate its responses. However, the current manual processes of collecting, annotating and extracting data from a corpus of unstructured text is time-consuming that often impedes the population of a knowledge base. An alternative is to design the agent as a teachable peer that can continuously learn from its human users. In this paper, we describe a storytelling agent that can expand its domain knowledge base by learning new assertions from its story-based conversation with children. We also use conversations as a means of validating the acquired knowledge. Results showed that the agent can achieve a recall value from 83% to 94% in extracting capabilities, property and instance relations while encountering difficulty in identifying assertions that describe the location of story events. Analysis of the conversation logs showed that the performance of the agent is affected by how children describe the characters and events in their stories.
Restaurant Recommendation System Based on User Ratings with Collaborative Filtering
Authors: Munaji Achmad Arif , Emanuel Andi Wahju Rahardjo 
Published: March 2021

Abstract
Recommendation systems are widely used as a reference in marketing products or businesses. The number of choices given sometimes makes a person confused in making choices. The recommendation system provides a solution in overcoming this. Collaborative filtering is a fairly popular algorithm used in recommendation systems that are based on references and information obtained from users. User rating is a variable that is widely used as a reference in establishing a recommendation system. User rating can influence other users in making their choice of the same product. The purpose of this study is to conduct an experiment in the application of a collaborative filtering algorithm and use the Pearson correlation function as a method used to see the level of user similarity in making a restaurant recommendation system. The methods used in this research are: (1) datasets preparation, (2) datasets filtering, (3) collaborative filtering, (4) pearson correlation, (5) recommendations, and (6) evaluation. Collaborative filtering combined with the Pearson correlation produces recommendations that are suitable for users based on characteristics or the same level of interest between users, making it easier for users to make choices. This is also indicated by a small error rate based on the RMSE results.
Identifying User Characteristics of the Top Three E-Wallet Services in Indonesia
Authors: Saputri Anggraeni Dias , Pratama Ahmad R 
Published: March 2021

Abstract
Electronic wallet (e-wallet) services have been the most commonly used electronic payment (e-payment) method in Indonesia within the past few years. By using survey data from 409 e-wallet users collected in the first half of 2020 and analyzed with descriptive statistics and logistic regression models, this study explores the top three e-wallet services in this country (i.e., GoPay, OVO, and DANA) to identify the user characteristics of the top three e-wallet which currently being used in Indonesia and to investigate which user groups, if any, tend to go with one e-wallet service over another. The results show that GoPay has the largest user base but OVO is the one with the most loyal user base while DANA has the most different user base of them all. The findings in this study can complement and enrich the literature on the use of ICT in the financial sector in Indonesia and shed light on the use of e-wallet and e-payment industry in this country. Apart from laying out some ground work for further research, the insight from this study can also be used by each e-wallet service to help make decisions as part of their marketing strategy.
Indonesian Sign Language Recognition using YOLO Method
Authors: Daniels Steve , Suciati Nanik , Fathichah Chastine 
Published: March 2021

Abstract
Sign language is a form of communication commonly used by people with hearing impairment or people with speech impediments. Not all ordinary people understand the language. The translation of sign language into the alphabet/text automatically will facilitate the communication of the deaf with ordinary people. This research aims to develop a sign language recognition system that can process input from video data using You Only Look Once (YOLO) in real-time. YOLO is an object detection method based on Convolutional Neural Network (CNN), which is accurate and fast. Retraining the Yolov3 pre-trained model is performed with adjustments to the number of channels and classes according to the sign language recognition requirement. In this research, we collect datasets independently based on the Indonesian Sign Language (BISINDO). In the experiment using image data, the system achieves 100% precision, recall, accuracy, and F1 score. While using video data, the system’s performance gets precision 77.14%, recall 93.1%, accuracy 72.97%, and F1 score 84.38%, with a speed of 8 fps.
Data Quality Assessment on Lecturer Primary Data: A Case Study on Higher Education Database at Ministry of Education and Culture Republic of Indonesia
Authors: Prasetyo Rizky T. , Ruldeviyani Yova , Purnamasari Editha D. , Wibowo Apriliana F. 
Published: March 2021

Abstract
The quality of an organization’s data is an important component that can influence various policies taken in technical and strategic terms. This study aims to analyze the quality of basic data for lecturers on resource information systems whose data is integrated with the Higher Education Database (PDDikti). Measurement of data quality based on the rules that have been set in the main unit of higher education data management which is then determined in this study includes 4 dimensions of data quality, namely completeness, validity, accuracy, and currency. The application of quantitative methods by processing data and then analyzing the quality of the data using qualitative methods based on the dimensions that have been determined for the basic data quality assessment for lecturer in every tertiary institution in Indonesia. The results showed that the quality of the data had been resolved by 91.94% so that it can be used as a basis for decision making according to predetermined standards. The results of this data quality research can be used as supporting information in policy making in terms of developing and improving data quality in the scope of higher education, especially those related to the quality of basic data for lecturers.
Image Captioning with Attention for Smart Local Tourism using EfficientNet
Authors: Fudholi Dhomas Hatta , Windiatmoko Yurio , Afrianto Nurdi , Susanto Prastyo Eko , Suyuti Magfirah , Hidayatullah Ahmad Fathan , Rahmadi Ridho 
Published: March 2021

Abstract
Smart systems have been massively developed to help humans in various tasks. Deep Learning technologies push even further in creating accurate assistant systems due to the explosion of data lakes. One of the smart system tasks is disseminating ‘users needed information’, which is crucial in the tourism sector to promote local tourism destinations. In this research, we design a local tourism specific image captioning model, which will later support the development of AI-powered systems that assist various users. The model is developed using a visual Attention mechanism and uses the state-of-the-art feature extractor architecture EfficientNet. A local tourism dataset is collected and used in the research and two different captions: captions that describe the image literally and captions that represent human logical responses when seeing the image. The two kinds of captions make the captioning model more humane when implemented in the assistance system. We compared two different models using EfficientNet architectures (BO and B4) with other well-known VGG16 and InceptionV3. The best BLEU scores we get are 73.39 and 24.51 for the training set and the validation set, respectively, using EfficientNetB0. The captioning result using the developed model shows that the model can produce logical caption for local tourism-related images.
Does it make you sad? A lexicon-based sentiment analysis on COVID-19 news tweets
Authors: Suryadi Dedy 
Published: March 2021

Abstract
This research utilizes a lexicon-based sentiment analysis to reveal the emotions conveyed by various news media and analyze the differences among the media. NRC Affect Intensity Lexicon is utilized; it provides the emotions of words in 8 categories of emotions. The lexicon is modified to reflect the context of the news, i.e., COVID-19 pandemic. Tweets are collected from four Indonesia’s news media and three international English-language news media. Each tweet from a news media is assigned a total emotion score for each of the 8 emotions. The scores are averaged for each day to obtain the Daily Emotion. Based on the Daily Emotion, the Dominant Daily Emotion may be identified, i.e., the emotion with the highest average score in a particular date. The Dominant Daily Emotion visualization shows that the dominant emotions in Indonesian-language media are Sadness and Trust, while Fear and Trust are considerably more pronounced in English-language media. Furthermore, among Indonesian-language media, kompascom is significantly more intense than the others in Joy, Sadness, Fear, and Trust. Among English-language media, XHNews is more intense than others in conveying Fear, Sadness, and Trust; while XHNews and timesofindia are both the most intense in Joy. The sentiment analysis in this research concludes that the Emotion Mix of news media are significantly different. Furthermore, news media also convey significantly different levels of emotion intensity in reporting COVID-19 news.
Designing and Developing an Agricultural Product Sales Application Catalog with a Hybrid Application Development Framework
Authors: Wibowo Argo , Delima Rosa , Antonius Rachmat C 
Published: March 2021

Abstract
The use of e-commerce systems continues to increase in Indonesia, but the field facts show that e-commerce in agriculture is still relatively low compared to other fields. The problems faced are limited e-commerce infrastructure, lack of the farmers' knowledge on the benefits of e-commerce, and application design, which is challenging to use. Those problems cause a lack of e-commerce use in this field. Besides, the characteristics of technology users in agriculture also have a considerable influence. In this paper, a mobile-based application is developed to facilitate farmers in promoting agricultural products sales. One of the problems faced in developing mobile applications is the diversity of mobile device platforms. A mobile application can ideally run on multiple platforms. This paper contributes to building an application by using a hybrid method for application development to be used on the Android and IOS platforms. The application built aims to display agricultural products of farmers. The filter feature also makes it easier to find agricultural products in this application. The application is built using the Flutter framework to use one source of the same code on several mobile operating systems at once. There are some slight differences between the output versions of the Android and iOS platforms. Although there are some subtle differences, the similarity rate reaches 90%.
The Physarum Algorithm with Adaptive Power Control: A Routing Optimization to Overcome Obstacle Problems in IoT Network
Authors: Laagu Muh Asnoer , Asvial Muhamad 
Published: March 2021

Abstract
The existence of obstacles on the Internet of Thing (IoT) network area is the biggest challenge in the IoT network infrastructure. The obstacle in the IoT network can be a wall or the absence of a router in a certain area. Routing optimization to facing obstacles problems has been done a lot, however, no routing algorithm that can overcome these obstacles. This paper proposed Physarum Routing based Adaptive Power Control (PR-APC), the routing optimization algorithm to resolve obstacles problems in the IoT network by controlling and maximizing the available power. This paper compares the routing optimization algorithm Physarum inspired Routing Protocol (P-iRP) and Lightweight and Energy-Aware Routing (LEAR). As a result, PR-APC can minimize the number of hops that are passed when sending data packets from the source node to the final destination node. PR-APC takes 3 hops with a delay of 6.4ms, while P-iRP takes 9 hops with a delay of 10.55ms, and LEAR 8 hops with a delay of 9.19ms. PR-APC optimizes the last node before the obstacle to be given strong power and be able to send data packets to the final destination node. P-iRP and LEAR in their routing decisions use parameter energy saving and will take a detour to avoid obstacles.
Formal Verification of the Authentication and Voice Communication Protocol Security on Device X Using Scyther Tool
Authors: Fikri Muhamad Al , Ramli Kalamullah , Sudiana Dodi 
Published: March 2021

Abstract
In the current era, the ownership of strategic information and the ability to effectively manage it has become a significant advantage. Reflecting on the experience of attacks on strategic communications in Indonesia, including the tapping of the former President Susilo Bambang Yudhoyono’s conversation through the cellular network and President Jokowi’s official residence, Indonesia has begun paying more attention to security in this sector. Device X is one of the secret strategic communication tools used in Indonesia. The XYZ Agency initiated the use of this device. As of 2020, there have been 1,284 units of Device X widely used by the army, police officers, and other strategic agencies in Indonesia. In its 5 years of operation, the XYZ Agency has researched the algorithm security used in Device X. However, there has never been a study of thwe security regarding the authentication and communication protocols of this device. This research aims to make a security analysis of voice communication and authentication protocols of Device X. The research was implemented using Scyther Tool as a formal verification approach. The analysis focuses on guaranteeing the confidentiality of information and authentication with four criteria, namely, secrecy, aliveness, synchronization, and agreement. The experimental results demonstrate that the authentication and voice communication protocol of Device X satisfy the secrecy criteria for transmitted confidential information but does not satisfy the criteria of aliveness, synchronization, and agreement on several entities involved in the protocol. Thus, it can be claimed that the authentication and voice communication protocol of Device X is provably secure based on the confidentiality aspect of information but is not secure in terms of authentication.
Blockchain-based Model for Health Information Exchange: A Case for Simulated Patient Referrals Using an Electronic Medical Record
Authors: Perez Alison O. , Domingo-Palaoag Thelma 
Published: March 2021

Abstract
Health information exchange (HIE) has been recognized as an effective mechanism to improve service delivery. The benefits are compounded with the introduction of electronic medical records (EMR). Exchanging information between health facilities remain a challenge due to the absence of an integrated health information system and issues pertaining to data integrity. The emergence of blockchain has provided an innovative solution to data sharing challenges in security, transparency and accessibility. This paper aims to develop a framework for health information exchange between health facilities with the use of EMR and blockchain-based network. The EMR was developed with functionalities such as adding and updating patient profile and health records, referring patient to another facility and monitoring patients referred. These functionalities have been simulated using hypothetical test cases. The simulation has demonstrated blockchain-related functions such as registration of health facility in the network, defining consensus rules and adding patient referral transactions in the network. The description of the proposed framework, role of blockchain and key features of the prototype EMR is summarized in the conclusion part.