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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 132 Documents
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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.
Design and development of face recognition-based security system using expression game as liveness detection Yusmanto, Yunan; Ar Rosyid, Harits; Prasetya Wibawa, Aji
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.v8i2.756

Abstract

Face recognition as a security system has undergone significant developments, but challenges in live detection are still a major issue in preventing fraud. Liveness detection is a method that helps face recognition security more resistant to fraud. This research aims to address this issue by developing an innovative security system that integrates face recognition with a facial expression game, enhancing live detection and user engagement. The primary objectives are to ensure seamless integration, maintain a fun and challenging user experience, and demonstrate practical applicability. We applied a Waterfall method in our research to ensure a straightforward approach. We successfully applied this system for the door lock-unlock mechanism, simulating a restricted area. YuNet, a face detection model runs in the web interface and controls the NodeMCU to either lock or unlock the door.  The study concluded 95% success rate from the participants in making facial expressions: Smile, Normal, and Sad. However, expressing Sadness within the 3-second timeframe posed some difficulties. The average duration for completing the mini-game was approximately 16.31 seconds from the start. The integration of a facial expression game as a liveness detection required careful design to balance security and user engagement that is fun to experience. This research underscores the significance of addressing current challenges in biometric security by integrating an interactive element into the live detection process. The developed system contributes to the field by enhancing the robustness and user experience of face recognition security systems, demonstrating potential for broader application in restricted access scenarios.
Maximizing digitalization to maintain consumer loyalty I'tisham, Muhammad; Chusniyah, Tutut; Hakim, Shaqilla
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.v8i2.766

Abstract

In the digital era, mobile applications have become essential tools for companies to foster consumer loyalty by enabling seamless interactions, efficient transactions, and accessible customer service. This study examines the impact of digitalization on customer loyalty through a qualitative case study of Sociolla, focusing on the SOCO application. Data were collected via purposive sampling involving a company representative and a Sociolla consumer and analyzed using thematic analysis, presenting findings narratively. Sociolla demonstrates the strategic use of digital platforms for branding and consumer engagement. The consumer informant reported increased interest, enthusiastic event participation, app downloads, and a clear understanding of Sociolla’s brand identity, highlighting the ecosystem fostered between the company and its customers. These findings underscore the broader applicability of effective digital strategies in engaging customers and enhancing brand loyalty across industries. The study concludes that mobile applications capture customer attention, drive active involvement, and strengthen brand relationships when effectively leveraged. It also identifies a need for future research to quantitatively assess the impact of digital platforms on loyalty, engagement, and brand attachment. However, the study's scope is limited to Sociolla, a cosmetics retail brand with a predominantly female customer base. Expanding research to include diverse demographics, regions, and industries could provide richer insights into the effectiveness of digitalization strategies in maintaining customer loyalty across varied contexts
Analyzing interaction and player experience of game based learning using feature importance based clustering Alfan, Muhammad Bahauddin; Yuhana, Umi Laili; Herumurti, Darlis
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.v8i2.772

Abstract

This study explores the dynamics of the gaming experience and its impact on learning efficiency through digital game-based learning (DGBL). Leveraging the Fingerstroke Level Model-GOMS (FLM-GOMS) for interaction analysis and the In-Game Experience Questionnaire (iGEQ) for player experience assessment, we examine the relationship between game-play mechanics and educational outcomes. Our research incorporates a comprehensive dataset, focusing on 40 features encompassing motivation and efficiency outcomes. Through clustering, we identify distinct player groups exhibiting signif-icant variations in efficiency outcomes and game experiences. We utilized the feature selection technique to identify the crucial features that differentiate groups of students who excel in implementing DGBL from those who do not. Through the Random Forest feature importance method, we have found that FLM-GOMS features and positive player in-game feedback play a pivotal role in determining the effectiveness of DGBL.
Implementation of facial recognition technology in the verification system for api banyuwangi cadets using the haar cascade algorithm Setiawan, Ariyono; Prasetyo, Kukuh Tri; Rusdyansyah, Arief; Ardian, Dede
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.v8i2.778

Abstract

This research aims to enhance the efficiency and security of the identity verification process for cadets at API Banyuwangi through the implementation of facial recognition technology using the Haar Cascade algorithm. In this study, experimental methods and statistical analysis were used to analyze the data obtained from a series of processing stages, including RGB to grayscale conversion, image resizing, and cropping. Data were collected through facial image acquisition using a webcam and processed to train the model and test the success of the verification system. Statistical analysis shows that preprocessing techniques have a significant impact on verification success, while facial recognition methods are also relevant. However, the data are not normally distributed, indicating the need for alternative analytical approaches. Thus, this research provides valuable insights into the potential of facial recognition technology in enhancing efficiency and security in identity management at educational institutions, while also highlighting the need for further research for the development of methods and deeper understanding.

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