Data Science: Journal of Computing and Applied Informatics https://talenta.usu.ac.id/JoCAI <p align="justify"><span class="" lang="en"><span class=""><strong>Data Science: Journal of Computing and Applied Informatics (JoCAI)</strong> is a peer-reviewed biannual journal (January and July) published by TALENTA Publisher and organized by Faculty of Computer Science and Information Technology, Universitas Sumatera Utara (USU) as an open access journal. It welcomes full research articles in the field of Computing and Applied Informatics related to Data Science from the following subject area: Analytics, Artificial Intelligence, Bioinformatics, Big Data, Computational Linguistics, Cryptography, Data Mining, Data Warehouse, E-Commerce, E-Government, E-Health, Internet of Things, Information Theory, Information Security, Machine Learning, Multimedia &amp; Image Processing, Software Engineering, Socio Informatics, and Wireless &amp; Mobile Computing.</span></span></p> Talenta Publisher en-US Data Science: Journal of Computing and Applied Informatics 2580-6769 <div id="coptf"> <p align="justify">The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Data Science: <span id="result_box" class="" lang="en"><span class="">Journal of Informatics Technology and Computer Science (JoCAI) and Faculty of Computer Science and Information Technology as well as TALENTA Publisher Universitas Sumatera Utara</span></span> as publisher of the journal.</p> <p align="justify">Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, will be allowed only with a written permission fromData Science: Journal of Informatics Technology and Computer Science (JoCAI).</p> <p align="justify">The Copyright Transfer Form can be downloaded <a href="https://drive.google.com/file/d/1nu9WWwxBdnpPiLNCXBQmpQgTIaBAFrq8/view?usp=sharing" target="_blank" rel="noopener">here</a>.&nbsp;<br>The copyright form should be signed originally and sent to<a href="https://talenta.usu.ac.id/JoCAI/jcontact"> the Editorial Office</a> in the form of original mail or scanned document.</p> </div> Blockchain Implementation on Subsidised LPG Distribution in Gas Supply Chain (Case Study: Medan) https://talenta.usu.ac.id/JoCAI/article/view/16624 <p>This study examines the potential of private blockchain technology on the Multichain platform for the implementation of a subsidised gas distribution information system in Medan. The objective is to enhance transparency, security, and reliability. The data were collected via a literature review and documentation analysis, and the system was developed using the waterfall methodology. The Multichain-based architecture ensures secure, transparent, and traceable transactions, thereby reducing the incidence of fraud and discrepancies. The results demonstrate that the architecture fulfils the requisite criteria, establishing a robust framework for gas distribution. This validates the effectiveness of Multichain-based private blockchain for improved efficiency and reliability in Medan's subsidised gas distribution system.</p> Habibie Satrio Nugroho Copyright (c) 2025 Data Science: Journal of Computing and Applied Informatics https://creativecommons.org/licenses/by-sa/4.0 2025-07-15 2025-07-15 9 2 1 17 10.32734/jocai.v9.i2-16624 Spatial Clustering Analysis of Stunting in North Sumatra Based on Environmental Factors Using K-Means Algorithm https://talenta.usu.ac.id/JoCAI/article/view/17179 <p>This research aims to analyze the spatial grouping of stunting events in North Sumatra based on environmental factors using the K-Means algorithm. The data used in this research includes the incidence of stunting, environmental factors (such as access to health services, living environment conditions, water use and sanitation), and spatial data (geographical coordinates). The data comes from Basic Health Research (RISKESDAS 2018, then processed and normalized. The elbow method and silhouette analysis are used to determine the optimal number of clusters, resulting in four different clusters. The application of the K-Means algorithm produces the following cluster characteristics: Cluster 1, with good environmental conditions and access to health services, shows low levels of stunting; Cluster 2, with moderate environmental conditions, shows moderate levels of stunting; Cluster 3, which is characterized by poor living conditions and limited access to health services, has levels high stunting; and Cluster 4, with varied environmental conditions but very limited access to health and sanitation services, also shows a high stunting rate. Validation using the Silhouette Coefficient produces an average score of 0.65 which indicates good clustering quality shows that environmental factors, access to health services, and sanitation conditions have a significant impact on the incidence of stunting. Based on these findings, policy and intervention recommendations are focused on Clusters 3 and 4, which have high stunting rates. The interventions carried out include increasing access and quality of nutrition, health services, sanitation conditions, economic empowerment, and health education.</p> Fanny Ramadhani Copyright (c) 2025 Data Science: Journal of Computing and Applied Informatics https://creativecommons.org/licenses/by-sa/4.0 2025-07-15 2025-07-15 9 2 18 25 10.32734/jocai.v9.i2-17179 Safety Measures For Special Care Individuals At The Bureau Of Fire Protection In Bolinao, Pangasinan, Philippines:Basis For A Plan Of Action https://talenta.usu.ac.id/JoCAI/article/view/21837 <p>This study assessed the implementation of fire safety measures by the Bureau of Fire Protection (BFP) in Bolinao, Pangasinan, focusing on the needs of special care individuals, including persons with disabilities, the elderly, and those with mobility or cognitive impairments. Using a descriptive-comparative and correlational research design, data were gathered from 150 special care individuals and caregivers, and 20 BFP personnel through validated questionnaires. The results revealed that while special care individuals generally perceived the level of fire safety implementation as high across awareness, preparedness, facilities, training, and policy compliance, BFP personnel rated them more moderately, highlighting gaps in training and infrastructure. Statistical analysis showed significant discrepancies in perception and underscored the need for targeted interventions. The study concludes with a proposed action plan aimed at enhancing inclusive fire safety protocols, training, and equipment for vulnerable populations.</p> Abelardo S. Mayugba Copyright (c) 2025 Data Science: Journal of Computing and Applied Informatics https://creativecommons.org/licenses/by-sa/4.0 2025-07-31 2025-07-31 9 2 26 30 10.32734/jocai.v9.i2-21837