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Sudah terakreditasi SINTA 2. Editorial Office of Bulletin of Social Informatics Theory and Application Association for Scientific Computing and Electrical, Engineering (ASCEE)-Indonesia Section Jln. Supriyadi, Kel. Surodakan, Kec. Trenggalek, Kota Trenggalek, Propinsi Jawa Timur, 66316 Indonesia Email: businta.2017@gmail.com
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Bulletin of Social Informatics Theory and Application
Published by Association for Scientific Computing, Electronics, and Engineering
ISSN : 26140047     EISSN : 26140047     DOI : https://doi.org/10.31763/businta.v6i2.601
Core Subject : Science, Social,
Computer Science & IT Social Sciences
Bulletin of Social Informatics Theory and Application (ISSN 2614-0047) is an interdisciplinary scientific journal for researchers from Computer Science, Informatics, Social Sciences, and Management Sciences to share ideas and opinions, and present original research work on studying the interplay between socially-centric platforms and social phenomena. Bulletin of Social Informatics Theory and Application is the first Asia-Pacific journal in social informatics. The journal aims to create a better understanding of novel and unique socially-centric platforms not just as a technology, but also as a set of social phenomena and to provide a media to help scholars from the two disciplines define common research objectives and explore methodologies. Bulletin of Social Informatics Theory and Application offers an opportunity for the dissemination of knowledge between the two communities by publishing of original research papers and experience-based case studies in computer science, sociology, psychology, political science, public health, media & communication studies, economics, linguistics, artificial intelligence, social network analysis, and other disciplines that can shed light on the open questions in the growing field of computational social science. To that end, we are inviting interdisciplinary papers, on applying information technology in the study of social phenomena, on applying social concepts in the design of information systems, on applying methods from the social sciences in the study of social computing and information systems, on applying computational algorithms to facilitate the study of social systems and human social dynamics, and on designing information and communication technologies that consider social context.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 16 Documents
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Search results for , issue "Vol. 8 No. 2 (2024)" : 16 Documents clear
Knowledge graph completion for scholarly knowledge graph Taufiqurrahman, Taufiqurrahman; Wiharja, Kemas Rahmat Saleh; Wulandari, Gia Septiana
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.657

Abstract

Scholarly knowledge graph is a knowledge graph that is used to represent knowledge contained in scientific publication documents. The information we can find in a scientific publication document is as follows: author, institution, name of journal/conference, and research topic. A knowledge graph that has been built is usually still not perfect. Some incomplete information may be found. To add the missing information, we can use knowledge graph completion, which is a method for finding missing or incorrect relationships to improve the quality of a knowledge graph. Knowledge graph completion can be carried out on a scholarly knowledge graph by adding new entities and relationships to produce further information in the scholarly knowledge graph. The data added to the scholarly knowledge graph are only other papers of first author entity, the research field of first author entity, and a description of the conference/journal entity. The result shows that the scholarly knowledge graph was completed by adding 81% correct data for other papers of first author entity, 80.3% correct data for the research field of first author entity, and 53.9% correct data for the description of the conference/journal.
Enhancing Lae-Lae Island sustainability: computer vision based waste detection and analysis Nasir, Arnold; Syariati, Kasmir; Suardi, Citra; Sundoro, David; Lordianto, Reinaldo Lewis
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.665

Abstract

In our study, "Enhancing Lae-Lae Island Sustainability: Computer Vision-Based Waste Detection and Analysis," we investigate novel approaches to address plastic pollution challenges in coastal ecosystems, focusing on Lae-Lae Island. Through a multidisciplinary approach, we uncover valuable insights for effective waste management and environmental conservation. Spatial analysis identifies concentrated plastic pollution hotspots, offering actionable data for targeted cleanup strategies. Temporal trend analysis reveals waste accumulation patterns, facilitating adaptive waste management decisions. Furthermore, we examine the impact of environmental factors on waste density, aiding in proactive pollution mitigation. Central to our research is the evaluation of computer vision technology, which demonstrates high precision, recall, and an F1-score of approximately 87.8%. These results signify the technology's potential to revolutionize waste detection and monitoring, enabling efficient resource allocation, real-time surveillance, and rapid pollution response. In conclusion, our study provides a data-driven framework for sustainable plastic waste management on Lae-Lae Island, offering insights applicable to coastal regions worldwide. By embracing technology and innovation, we pave the way for cleaner, more resilient coastal ecosystems, underscoring the importance of proactive environmental stewardship.
Data mining for forecasting community mobility denpasar city with long short-term memory method Setiawan , I Wayan Agus Hery; Triandini, Evi; Suniantara , I Ketut Putu; Kuswanto , Djoko
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.670

Abstract

Denpasar City has a high potential for community mobility, this is supported by many public facilities. High and highly volatile human mobility causes the transmission of the COVID-19 virus to spread very quickly, so forecasting is needed to find out a picture of future community mobility using data mining techniques. Data mining is the process of solving problems by analyzing data that already exists in the database. Denpasar City community mobility data for the period September 1, 2021 – October 31, 2021 show that most of the high mobility is in the junior high school sector. The Long Short-Term Memory method was chosen as a method that can assist in forecasting community mobility. Long Short-Term Memory has the advantage of dealing with missing gradient problems and can be used on all types of data patterns, whether trend, cyclical, seasonal, or horizontal patterns. Hyperparameter tests were carried out including LSTM_units representing the number of Long Short-Term Memory units in each layer, Dropout, and Optimizer to obtain the optimal prediction method. this combination yields a total of 45 methods. The best hyperparameter obtained is at LSTM_units of 128, Dropout of 0.1, and Optimizer is Adam. The results obtained with this hyperparameter are the Root Mean Square Error (RMSE) value of 971,438687. This method results in forecasting the mobility of the people of Denpasar City from November 1, 2021 to November 7, 2021, reaching 9.550 total checkins which is close to the actual value of 10.219
An efficient and interactive android-based neighborhood management Junianto, Haris; Saputra , Dhanar Intan Surya; Saputro, Rujianto Eko
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.681

Abstract

This research aims to design and develop a prototype Android-based Neighborhood Association management information system application using the Agile approach to assist Neighborhood Association administrators in real-time administrative processes and information dissemination. The Agile approach was selected to enhance flexibility and responsiveness in application development, enabling adjustments to potential user needs and changes that may occur during the development process. The application is expected to improve service quality and governance transparency at the Neighborhood Association level while facilitating residents' access to information and interaction with Neighborhood administrators. The application development process employs the Agile approach, involving the development team in iterative cycles to meet user requirements. Research results demonstrate the achievement of research objectives, with the application capable of managing resident data, Neighborhood Association finances, event scheduling, and Neighborhood Association news. The Agile approach used in the application's development provides the flexibility needed to adapt to changing user requirements, offering a solution to the challenges faced by Neighborhood Association administrators in performing their duties. This aligns with Agile principles, emphasizing user collaboration and responsiveness to changes.
Innovative CNN approach for reliable chicken meat classification in the poultry industry Anraeni, Siska; Mustari, Muhid; Ramdaniah, Ramdaniah; Kurniati, Nia; Mubarak, Syahrul
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.686

Abstract

In response to the burgeoning need for advanced object recognition and classification, this research embarks on a journey harnessing the formidable capabilities of Convolutional Neural Networks (CNNs). The central aim of this study revolves around the precise identification and categorization of objects, with a specific focus on the critical task of distinguishing between fresh and spoiled chicken meat. This study's overarching objective is to craft a robust CNN-based classification model that excels in discriminating between objects. In the context of our research, we set out to create a model adept at distinguishing between fresh and rotten chicken meat. This endeavor holds immense potential in augmenting food safety and elevating quality control standards within the poultry industry. Our research methodology entails meticulous data collection, which includes acquiring high-resolution images of chicken meat. This meticulously curated dataset serves as the bedrock for both training and testing our CNN model. To optimize the model, we employ the 'adam' optimizer, while critical performance metrics, such as accuracy, precision, recall, and the F1-score, are methodically computed to evaluate the model's effectiveness. Our experimental findings unveil the remarkable success of our CNN model, with consistent accuracy, precision, and recall metrics all reaching an impressive pinnacle of 94%. These metrics underscore the model's excellence in the realm of object classification, with a particular emphasis on its proficiency in distinguishing between fresh and rotten chicken meat. In summation, our research concludes that the CNN model has exhibited exceptional prowess in the domains of object recognition and classification. The model's high accuracy signifies its precision in furnishing accurate predictions, while its elevated precision and recall values accentuate its effectiveness in differentiating between object classes. Consequently, the CNN model stands as a robust foundation for future strides in object classification technology. As we peer into the horizon of future research, myriad opportunities beckon. Our CNN model's applicability extends beyond chicken meat classification, inviting exploration across diverse domains. Furthermore, the model's refinement and adaptation for specific challenges represent an exciting avenue for future work, promising heightened performance across a broader spectrum of object recognition tasks.
Sentiment analysis of Indonesian government policy in the era of social commerce: public perception and reaction Sugiarti, Sugiarti; Arsi, Primandani; Subarkah, Pungkas; V, Jay
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.710

Abstract

This research explores public sentiment towards the Indonesian government’s policies in the era of social commerce, based on Minister of Trade Regulation No. 31 of 2023. Sentiment analysis was conducted on a dataset comprising 1013 tweets on Twitter, employing various machine learning algorithms, including Naïve Bayes, Logistic Regression, Random Forest, SVM, and KNN. The results reveal that the Support Vector Machine (SVM) algorithm achieved the highest accuracy rate of 87%, outperforming other algorithms. Analyzing public sentiment towards the mentioned government policies, positive sentiment accounted for 20.2%, while negative sentiment reached 79.8%. This suggests that the policies, as outlined in the regulation, did not elicit a positive response from the public. Recommendations for future research include expanding the dataset and incorporating diverse data sources beyond Twitter for enhanced accuracy. This study contributes valuable insights into public sentiment analysis, particularly in the context of social commerce policies, providing a foundation for further investigations and policy adjustments.
LEACH algorithm analysis and simulation using MATLAB Siyu, Maruly Widjaya; Fattah, Farniwati; Gaffar , Andi Widya Mufila
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.735

Abstract

Research on Wireless Sensor Network (WSN) began to be carried out to meet various industrial needs including defense, health, environmental surveillance, and others. However, there are several obstacles in WSN, namely the problem of energy consumption which is the object of research by many researchers. The solution offered in this paper is to use the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a hierarchical protocol where this protocol focuses on saving energy use on WSN. This study used Matlab 2023 simulation software which used several measurement parameters to determine tissue life time, average residual energy, and throughput. The research scenario uses a homogeneous topology with three network sizes, namely 500 x 500, 750 x 750, and 1000 x 1000. Then also used three conditions for the number of sensor nodes, namely 100 nodes, 150 nodes, and 200 nodes. The results showed that the smaller the tissue size, the longer the life time and if the network size is wider, the network life time is shorter. The number of data packets transmitted depends on the number of active sensor nodes and sufficient energy to transmit.
Digitalization of information systems and educational laboratory management in higher education institutions Fauzi, Rochmad; Ar Rosyid, Harits; Herwanto , Heru Wahyu
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.740

Abstract

This study aims to develop an Information and Educational Laboratory Management System application based on SIONLAP. SIONLAP is designed and developed following educational institution elements' duties, needs, and functions. The system is developed to convert manual procedures, forms, and workflows into digital formats. Workflow processes can be optimized and automated through the implementation of SIONLAP. Documents and records generated by SIONLAP will be in digital data form, which can facilitate data processing and strategic analysis for planning, organizing, implementing, documenting, monitoring, reporting, evaluating, and developing educational laboratories, thereby improving management and continuous services in support of the implementation of the Tri Dharma of Higher Education. The research method refers to the waterfall method, with testing using the black box method. The results of the SIONLAP 2.0 application research show that it 1) provides more user-friendly user access management capabilities to facilitate users in higher education institutions with multi-role functions; 2) simplifies the data management and information workflow of equipment inventory; and 3) offers a laboratory asset rental feature as a means for higher education institutions to generate revenue from their laboratory assets
Reinforcement learning and meta-learning perspectives frameworks for future medical imaging Huda, Nurul; Windiarti , Ika Safitri
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.741

Abstract

In the envisioned landscape of medical imaging in 2044, this research explores the integration of advanced AI techniques, specifically reinforcement learning (RL) and meta-learning, to address persistent challenges in disease diagnosis and treatment planning. Leveraging vast amounts of imaging data, deep learning models have demonstrated significant advancements in tasks such as tumor detection and organ segmentation. However, existing approaches often face limitations in adapting to evolving patient characteristics and data scarcity. By incorporating principles from RL and meta-learning, this study aims to develop dynamic, adaptive AI systems capable of optimizing imaging protocols, enhancing diagnostic accuracy, and personalizing treatment strategies for individual patients. The research conducts a comprehensive review of existing literature on RL and meta-learning in healthcare proposes novel methodologies for integrating these techniques into medical imaging workflows, and evaluates their efficacy through empirical studies and clinical validation. The ultimate goal is to contribute to the advancement of medical imaging technologies, paving the way for more personalized and efficient healthcare solutions in the future
Analyzing the Indonesian sentiment to rohingya refugees using IndoBERT model Arifin, M Zainal; Maulana, Sandy Yunan; Noertjahyana, Agustinus; Mohamed Asghaiyer, Asghaiyer
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.749

Abstract

This study aims to analyze public sentiments towards Rohingya refugees in Indonesia using the IndoBERT model. We collected sentiment data from social media platforms and news articles, followed by preprocessing techniques including tokenization, cleaning, case folding, stemming, and filtering. Sentiment labels were assigned using the InSet lexicon, and the IndoBERT model was trained with these labeled data. Our findings reveal that the predominant sentiment is negative, with 65% of the sentiments classified as negative, 20% as neutral, and 15% as positive. The model demonstrated robust performance with an accuracy of 87%, precision of 85%, recall of 83%, and an F1 score of 84%. This research addresses a gap in sentiment analysis studies related to refugee issues and provides valuable insights into public perceptions, which could inform policies and interventions aimed at improving refugee integration and support systems in Indonesia.

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