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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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Reinforcing HIV/AIDS Prevention and Control through a Sustainable Data Management System Demartoto, Argyo; Murti, Bhisma; Pujihartati, Sri Hilmi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1227

Abstract

The digitalization of healthcare services has significantly improved access to data and information regarding HIV/AIDS patients, thereby enhancing monitoring and evaluation efforts. However, discrepancies in data across various providers can lead to inefficiencies and hinder the formulation of comprehensive HIV/AIDS prevention and control programs. Therefore, integrating sustainability into healthcare service digitalization practices is essential. This research aims to explore the reinforcement of HIV/AIDS prevention and control through a sustainable data management system, using Giddens’ structuration theory as the framework. A qualitative research method with an exploratory approach was employed, including data collection through observation, in-depth interviews, Focus Group Discussions (FGD) with key informants, and document processing. The informants included the person responsible for health promotion at the Health Office of Surakarta City, members of the Regional AIDS Commission (KPAD), staff from hospitals and several public health centers (Puskesmas) in Surakarta, field officers, members of the Peer Support Group (KDS), non-governmental organizations (NGOs) involved in HIV/AIDS prevention and control, as well as HIV/AIDS patients and at-risk individuals. The research findings reveal that both people (as the agency) and institutions responsible for health data collection must collaborate to establish a sustainable data system. This collaboration ensures data accuracy and continuity, which, in turn, strengthens HIV/AIDS prevention and control programs. A sustainable data system also supports medication adherence, timely updates on patient information, and various other aspects of HIV/AIDS prevention and control.
Development of a Web-Based Contractor Safety Management System (CSMS): A Case Study of PT. Petromine Energi Trading Usman, Usman; Rokhmat, Nur
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1230

Abstract

This study aimed to develop a Contractor Safety Management System (CSMS) to support the program implemented by PT. Petromine Energi Trading, aligning with the company’s objectives for managing the CSMS within the framework of the Mining Safety Management System (SMKP). The technical approach focused on creating a web-based application using a user-centered design (USD) methodology. The results showed that the integration of Single Sign-On (SSO) in the web-based platform enhanced user convenience by allowing seamless access and interaction with the CSMS. The backend structure of the system was designed to facilitate efficient data management and ensure secure, real-time information processing. The CSMS was also developed to ensure compliance with national regulations and international standards while improving communication and coordination between PT. Petromine Energi Trading and its contractors, supporting the comprehensive management of Occupational Health and Safety (OHS) risks.
IoT-Powered Customer Engagement for Marketing Optimization in Tanzania SMEs Mkali, Khalifa Issa; Sinde, Ramadhani S.; Karokola, Geoffrey R.
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1235

Abstract

Embracing Internet of Things (IoT) technologies presents significant opportunities for enhancing customer engagement. However, Tanzanian Small and Medium Enterprises (SMEs) are confronted with persistent challenges, including limited infrastructure, financial barriers, and low digital literacy. This study proposes an IoT-powered customer engagement platform built using Python-Django specifically tailored for Tanzanian SMEs to generate actionable customer insights and strengthen engagement strategies. A mixed-methods approach, combining surveys, observations, and interviews with SME owners and customers in Dar es Salaam and Arusha, revealed operational inefficiencies and gaps in customer interaction. Based on these insights, a scalable and cost-effective IoT solution was designed to automate feedback collection, monitor customer behavior in real-time, and deliver personalized services. The platform was evaluated through simulated testing, expert reviews, and performance analysis. Results showed a 15 - 20% improvement in customer retention, a 30% reduction in inquiry response time, 99.50% system uptime, and an 80% user-friendliness score. These findings demonstrate the novelty and effectiveness of integrating IoT, real-time monitoring, and marketing optimization in the Tanzania SME context. Overall, the research highlights the transformative potential of IoT in enabling data-driven decision-making, enhancing customer relationships, and promoting sustainable growth in underserved business environments.
What Architecture Students Learn About IoT and Energy Efficiency Harsritanto, Bangun I.R.; Harani, Arnis Rochma; Riskiyanto, Resza; Wahyuningrum, Sri Hartuti
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1237

Abstract

Energy consumption has become a major concern within academic communities, affecting everyone from undergraduate students to senior professors. In response, architectural schools in Indonesia have begun incorporating energy efficiency education into the curriculum for first-year students across various courses. One of the newest mandatory courses at the university is the Internet of Things (IoT). In the architecture department, this course not only covers the fundamentals of IoT but also emphasizes designing IoT solutions that support energy efficiency. This article describes the process by which architecture students integrate energy efficiency concepts into their IoT designs. A case study conducted during the Spring semester of 2025 in IoT classes for second-year students, using the updated teaching syllabus, highlights the positive outcomes. The results demonstrate that early-stage architectural education can significantly raise awareness of energy efficiency in both IoT and building design.
Global Research Trends and Map on Machine Learning Applications in Stunting Detection in Vulnerable Populations: A Bibliometric Analysis Bachri, Otong Saeful; Widodo, Catur Edi; Nurhayati, Oky Dwi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1248

Abstract

Stunting and malnutrition continue to be significant public health challenges, particularly in low-income and rural populations. With the growing reliance on data-driven strategies in public health, machine learning (ML) has emerged as a promising tool for identifying, classifying, and predicting conditions related to undernutrition. This study presents a bibliometric analysis of global research from 2019 to 2025, focusing on the application of ML techniques—such as clustering, support vector machines (SVM), and random forest—in addressing malnutrition and stunting. A total of 417 Scopus-indexed publications were analyzed using Biblioshiny (R) to assess research trends, key themes, influential authors, prominent journals, and thematic evolution. The analysis reveals a consistent growth rate of 10.72% in publications, with notable contributions from China and other low- and middle-income countries. Keyword mapping highlights that “machine learning,” “spatial analysis,” and “stunting” are central to the research, although they remain areas for further development. Thematic evolution indicates a shift towards more integrated, context-aware approaches, with a growing focus on built environments and vulnerable populations. The study concludes that while ML holds significant promise for advancing decision-making in child health and nutrition, its impact will depend on continued methodological refinement and effective implementation within public health systems.
Real-time Multi-Screen Cheating Detection using K-Means Clustering Purwanto, Yudhi Setyo; Putra, Rakhmadi Irfansyah
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1262

Abstract

Ensuring academic honesty during online exams is becoming more and more challenging with students taking advantage of multiple screens and mirrored monitors. This research presents a privacy-sensitive, real-time multi-screen behavior detection model that does not rely on cameras or biometric sensors. The system tracks hardware and behavioral signals like screen-switch rate, focus-loss activity, idle time, and display change events. Utilizing K-Means clustering (k = 3), these metrics are segregated into three categories: Normal, Suspected, and Cheating. Implemented in Python and tested on simulated and real datasets, the model registered a silhouette score of 0.27 and showed discriminative behavior segregation through clustering analysis. Testing against a labelled dataset produced balanced accuracy of more than 80 percent, supported by confusion matrix and performance curve research. Findings show that hardware monitoring and activity-based could be an effective, camera-free means of detecting cheating in online examinations. The approach is privacy-respecting, computationally light in real time, and has understandable output for administrative exam. Drawbacks include the focus to date on Windows platforms and the need for more comprehensive cross-platform testing. Future studies will examine multimodal integration and larger scales to further increase detection accuracy and transferability.
Actor-Critic Reinforcement Learning for Personalized STEM Learning Path Optimization Hatta, Muhammad; Magdalena, Lena; Putra, Dwi Pasha Anggara; Runa, Yohanes Michael Fouk; Irfansyah, Ananda; Valentino, Fernando
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1270

Abstract

This study addresses the critical need for adaptive learning in non-formal education settings, particularly Community Learning Centres (PKBM) in Indonesia, where student heterogeneity and limited resources challenge conventional teaching methods. We developed a personalized learning path optimization model using Actor-Critic Reinforcement Learning (RL) to enhance STEM competency development. The novel framework integrates cognitive, affective, and personality features to dynamically adjust material difficulty based on real-time analysis of student cognitive states (quiz performance, completion rate) and affective conditions (emotional level), moving beyond static predictive approaches. Experimental results on a synthetic dataset demonstrate that the Actor-Critic agent achieves statistically significant higher rewards (-2.92 vs -3.01, p<0.05) and greater output stability compared to a random baseline. Although the absolute reward difference is modest, it reflects more consistent adaptive policy performance, despite limited effect size (Cohen's d=0.0317). Feature importance analysis confirms that quiz_score and emotion_level are the dominant factors influencing adaptive recommendations, while personality traits show negligible impact. The framework offers a viable pathway for scalable, personalized learning in resource-constrained environments. Future work should validate the model with real-world student data and refine reward functions to strengthen practical impact.
Sentiment Analysis on Coretax Data Using SVM and Random Forest with SMOTE and Tomek-Link Oktafiandi, Hery; Winarnie, Winarnie; Ramadhan, M. Fajar; Panjaitan, Febriyanti
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1279

Abstract

This study is motivated by the increasing adoption of digital tax platforms in Indonesia, particularly Coretax, which enables online tax reporting and payment. Understanding user sentiment is crucial for evaluating system effectiveness and identifying areas for improvement. However, sentiment data is often imbalanced, making it challenging to detect the sentiments of the minority class. This research evaluates the performance of Support Vector Machine (SVM) and Random Forest (RF) in classifying sentiment from Coretax related reviews collected between March and September 2025 from Twitter, YouTube, and the DJP application. Lexicon-based labeling and preprocessing were applied, followed by class balancing using Tomek-Link, SMOTE, and SMOTE-Tomek techniques. On the original data, SVM achieved an accuracy of 98.56%, while Random Forest reached 98.43%, both performing strongly on the majority class. However, minority class detection was improved through SMOTE and SMOTE-Tomek, albeit with a slight decrease in overall accuracy due to the risk of overfitting. The novelty of this study lies in its focus on Coretax 2025 data and a comparative analysis of multiple resampling techniques, providing practical insights into improving sentiment analysis performance on imbalanced digital tax data.
Leveraging Artificial Intelligence for Enhanced Operational Efficiency in the Telecommunications Industry: A Case Study from Zimbabwe Taderera, Kudzai; Zvikaramba, Alocate
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1167

Abstract

This study uses one telecommunications company as a case study to investigate the potential of artificial intelligence (AI) to enhance operational efficiency in Zimbabwe's telecommunications industry. Despite global advancements in AI adoption, its integration within Zimbabwe remains limited, particularly in addressing inefficiencies such as high operational costs, poor service quality, and outdated infrastructure. The research is grounded in the TOE framework, the RBV model, and the DOI theory. A quantitative approach was adopted, and data were collected from 117 respondents using structured questionnaires. Analysis was conducted using descriptive statistics, factor analysis, Pearson correlation, regression modelling, and ANOVA to assess AI adoption levels, its impact on efficiency, and the barriers to integration. Findings indicate that while AI adoption is still emerging, it has already led to improved service delivery, reduced downtime, and enhanced resource utilisation. However, several barriers persist, including financial constraints, regulatory uncertainty, infrastructure deficits, and limited technical expertise. The study proposes a five-pillar strategic framework focusing on technological readiness, supportive policy, capacity building, financial planning, and stakeholder collaboration to guide sustainable AI implementation. In conclusion, the research underlines that with targeted strategic investments and institutional support, AI can significantly transform operational efficiency in Zimbabwe's telecom sector. The findings offer practical insights for industry leaders, policymakers, and researchers seeking to drive digital transformation in emerging economies for operational efficiency.
Web-Based Wind Speed Forecasting System Using Prophet Siregar, Elvan Dito; Putri, Raissa Amanda
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1240

Abstract

The research undertaken has the central purpose of creating as well as applying a digital platform accessible through the internet, which is structured specifically to anticipate variations in wind velocity by employing the Prophet algorithm as the analytical framework. The system addresses the need for accurate and accessible forecasting tools in Medan, where highly variable wind patterns affect transportation, agriculture, and disaster mitigation. The research methodology consists of several stages including data collection from BMKG Medan, preprocessing through cleaning and aggregation of daily measurements into monthly averages, forecasting using the Prophet model, system development, and evaluation. Prophet was selected due to its ability to capture trend and seasonal components effectively with minimal parameter tuning. The system was developed using Laravel, MySQL, and Chart.js, integrating Prophet through Python to generate interactive visualizations and downloadable reports. The effectiveness of the predictive framework was measured by means of the Root Mean Square Error (RMSE = 0.19) and Mean Absolute Error (MAE = 0.15), validating the suitability of the method for producing consistent monthly forecasts of wind velocity. The system provides stakeholders such as disaster management agencies, marine operators, and agricultural planners with a practical platform for accessing accurate and timely forecasts. The findings further demonstrate the novelty of integrating Prophet forecasting with a web-based information system equipped with visualization and reporting features, thereby enhancing usability, accessibility, and decision-making support for regional meteorological applications.

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