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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 733 Documents
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Utilizing Random Forest Method for Predicting Student Dropout Risk in Madrasah Environments Mahsun, Muhammad; Hariyadi, M. Amin; Harini, Sri
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1364

Abstract

The phenomenon of school dropout represents a crucial issue with negative impacts on educational institution performance, social stability, and national development. Consequently, the early detection of high-risk students constitutes a strategic preventive measure. This research aims to develop an accurate predictive model using a Machine Learning approach. The study employed a comparative evaluation using classification algorithms, with the primary focus being the performance analysis of the Random Forest Classifier. The dataset utilized, comprising 1,763 student records, underwent a rigorous data pre-processing phase, including data cleaning, variable transformation, and class imbalance handling, to ensure high-quality input. The model was trained using a Random Seed configuration of 75 to guarantee experimental reproducibility and consistency in evaluation results. Experimental findings indicate that the Random Forest algorithm provided the best performance, achieving an accuracy of 82.0% and a precision of 83.8%. This superior performance confirms the model's effectiveness in identifying the key determinants of dropout, stemming from both students' internal and external factors. Based on these results, the research recommends the application of Random Forest as a Decision Support System instrument to facilitate targeted interventions, including medical support, economic assistance, and academic counseling. Future research is advised to integrate historical counseling data to further enhance the prediction sensitivity of the model.
Taxpayer Classification Using K-Means Clustering to Support CRM Strategy Development: Case Study of Prabumulih City Samsat Tammam, Bimmo Fathin; Ibrahim, Ali; Indah, Dwi Rosa; Oklilas, Ahmad Fali; Utama, Yadi
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1365

Abstract

Effective management of taxpayer data is crucial for enhancing compliance and optimizing regional revenue. This study addresses the limited use of data-driven taxpayer segmentation in local Samsat institutions by applying K-Means Clustering to support targeted Customer Relationship Management (CRM) strategies. A dataset of 3,999 motor vehicle taxpayer records from September 2025 was processed through feature selection, scaling, and clustering. The analysis identified three distinct taxpayer groups based on payment timeliness, compliance consistency, and vehicle age. Cluster validity was confirmed using the Davies-Bouldin Index, yielding a value of -41.327 for k = 3, supported by ANOVA for statistical significance. The findings highlight how clustering can reveal taxpayer behavior patterns, guiding personalized services and compliance programs. This study's novelty lies in integrating clustering outcomes with practical CRM strategies for public agencies, offering a data-driven approach to improve taxpayer engagement and regional revenue. However, the study is limited by its focus on a single-period dataset and vehicle-related attributes.
Hybrid Random Forest Regression and Ant Colony Optimization for Delivery Route Optimization Aurelia, Reni; Rahman, Abdul
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1376

Abstract

The transportation of goods in Indonesian cities is increasingly challenged by urbanization, congestion, diverse road characteristics, and environmental factors, reducing the effectiveness of conventional distance-based routing. This study enhances delivery route optimization by integrating travel-time prediction using Random Forest Regression (RFR) with a metaheuristic routing process using Ant Colony Optimization (ACO). Using OpenStreetMap (OSM) data for Palembang, experiments were conducted on five simulated customer locations in Zone 1. Road attributes such as segment length, road type, and estimated speed were used to train the RFR model, whose predicted travel times served as dynamic costs in the ACO heuristic. The RFR model achieved high predictive accuracy (R² = 0.98; MSE = 8.81), and the ACO-based optimization produced an efficient route of 29.58 km with a total travel time of 148 minutes. However, the experiment is limited to a single zone, a small number of customers, and the removal of real traffic variables—where all actual speed variations, congestion levels, and time-dependent traffic conditions were simplified or omitted, causing the model to rely solely on static road attributes. Future work will incorporate real-time traffic data, expand testing to multiple zones, and use larger datasets to improve scalability and operational applicability.
Optimized K-Means Clustering for Web Server Anomaly Detection Using Elbow Method and Security-Rule Enhancements Trianto, Rahmawan Bagus; Muin, Muhammad Abdul; Vikasari, Cahya
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1391

Abstract

Anomaly detection in web server environments is essential for identifying early indicators of cyberattacks that arise from abnormal request behaviors. Traditional signature-based mechanisms often fail to detect emerging or obfuscated threats, requiring more adaptive analytical approaches. This study proposes an optimized anomaly detection model using K-Means clustering enhanced with engineered security-rule features and the Elbow Method. Two datasets were used: a small dataset of 3,399 log entries from one VPS and a large dataset of 223,554 entries collected from three VPS nodes, all sourced from local production servers of the Department of Computer and Business, Politeknik Negeri Cilacap. The preprocessing pipeline includes timestamp normalization, removal of non-informative static resources, numerical feature scaling, and TF-IDF encoding of URL paths. Domain-driven security features entropy scores, encoded-payload indicators, abnormal status-code ratios, and request-rate deviations were integrated to improve anomaly separability. Experiments across five model configurations show that combining larger datasets with rule-based features significantly enhances clustering performance, achieving a Silhouette Score of 0.9136 and a Davies–Bouldin Index of 0.4712. The results validate the effectiveness of incorporating security-rule engineering with unsupervised learning to support early-warning threat detection in web server environments.
Stakeholder Analysis for Enhancing Ethics in Software Development: A Scoping Review Marebane, Senyeki Milton; Mnkandla, Ernest
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1263

Abstract

Stakeholder analysis has become a crucial means for achieving software development ethical goals. This scoping review study aims to provide an overview of the extent to which stakeholder analysis has been applied to enhance ethics in software development. The study explores research studies that have been published by IEEE, ACM, Science Direct, AIS and Journal of Business Ethics. PRISMA-ScR was employed to achieve the objective of the study. Only six research studies published from 1999 to 2015 met the selection criteria for data extraction and analysis. The results show that the focus of stakeholder analysis is on moral impulse, risk identification, stakeholder interactions, stakeholder classification and impact level. Furthermore, stakeholder analysis for enhancing ethics in software development is prevalent to empower the voiceless stakeholders, risk management and stakeholder mapping and quantification for measuring stakeholder impact on projects. The analysis of the results reveals several research gaps such as unavailable empirical studies beyond 2015, concentration only on requirements engineering and lack of studies on stakeholder analysis on emergent technologies. The implications of this study point to the need for more guidance and expanded use of stakeholder analysis across the complete software process to benefit software teams and ongoing research to harness the potential of this theory on enhancing ethics in the emergent technologies.
Evaluating ICT Project Sustainability Using Business Intelligence and Fuzzy AHP Mogale, Gwendoline Moshishi; Esiefarienrhe, Bukohwo Michael
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1276

Abstract

This study explores persistent sustainability challenges in ICT projects despite the widespread availability of project management tools. A mixed-method approach combining inspections, observations, and a structured questionnaire was used to evaluate the six key framework attributes drawn from existing literature. The rating response from the experienced and professional ICT projects field experts, which were based on a three-point scale, were further analyzed using Microsoft’s MicroStrategy dashboard (BI). The rating outcomes predominantly revealed the outcome as follows: Value Proposition (100% - agree), Strategy (92% - agree), Governance (92% - agree), Environmental Risk Assessment (50% disagree, but 42% agree), Time Management (83% - neutral, but 17% agreed), and Resource Capacity (92% - neutral, but 8% agreed). Subsequently, Fuzzy Analytic Hierarchy Process (F-AHP) model was applied to determine the relative priority of each attribute/factor. The usage of MS MicroStrategy dashboards (BI) enabled transparent visualization of expert ratings, while F-AHP provided structured prioritization and consistency validation through Saaty’s 10% rule. The integration of BI and F-AHP resulted in a study’s VSGETR framework which offers a scalable, data-driven model tailored to public sector governance, bridging strategic objectives with operational execution. Findings in this study suggest that embedding this framework into project policies and evaluation checklists can significantly improve sustainability oversight and resource monitoring in ICT initiatives.
Integrating Diversity, Equity, and Inclusion into Systems Analysis and Design Education Segooa, Mmatshuene Anna; Barber, Connie S
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1277

Abstract

Diversity, Equity, and Inclusion (DEI) represent the involvement of different groups supported equally and fairly while their differences are acknowledged and recognized. Many organizations, including higher education institutions, have adopted the notion of DEI by introducing the phenomenon through their institutional strategies. However, there is limited evidence of the inclusion of DEI content in information systems (IS) education. The paper aimed to develop a framework to integrate DEI in Information Systems Analysis and Design education and projects. A survey to investigate how organizations actively include DEI content in the different stages of the System Development Lifecycle (SDLC) methodology was administered. Results identified that 23-40% of organizations represented address DEI at some stage of the SDLC. Practical implications for instructors include well-informed and prepared students as well as improved System Analysis & Design (SA & D) curriculum. The practical implications for information systems (IS) practitioners are resulting system designs that are culturally sensitive, end user perspective driven, and accessible for all users. Bridging IS education with industry practice through a framework for DEI in the system design space brings new insights into system design, better preparing students to face DEI content in their careers across all industries. Significantly raising awareness that problem solving and IS solutions should meet the needs of society with different backgrounds and cultures.
Oil and Gas Production Forecasting Based on LSTM Model: A Case Study of PT Pertamina Hulu Rokan Zone 4 Billan, Angel Caroline; Ependi, Usman
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1285

Abstract

This study addresses the critical need for accurate oil and gas production forecasting to support strategic decision-making in Indonesia’s energy sector. PT Pertamina Hulu Rokan Zone 4 (PHR Zona 4), a key player in national energy production, frequently encounters technical and external operational challenges. To tackle these issues, this research proposes a deep learning-based predictive model using the Long Short-Term Memory (LSTM) architecture, structured in an encoder-decoder format and enhanced with an attention mechanism. The model was trained and tested on historical oil and gas production data from PHR Zona 4, evaluated under two data-splitting scenarios: 80:20 and 90:10. Model performance was assessed using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results from the 80:20 scenario showed RMSE of 5.83, MAE of 5.54, MAPE of 1.71%, and R² of -1.97, suggesting difficulties in capturing extreme data fluctuations. However, the 90:10 scenario demonstrated significantly improved performance with RMSE of 0.42, MAE of 0.36, MAPE of 0.11%, and R² of 0.00, indicating better trend prediction stability. The novelty of this study lies in the integration of attention mechanisms within the LSTM encoder-decoder framework for oil and gas time series forecasting, offering enhanced accuracy and robustness. This research provides a valuable foundation for future improvements in predictive analytics and operational efficiency in the oil and gas industry.
Automating Performance-Based Budgeting Using a Knowledge-Based System Rantung, Tessa Vatma; Utami, Ema
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1286

Abstract

This study investigates the implementation of a Knowledge-Based System (KBS) integrated with the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to automate performance-based budgeting in a public university environment. The integration of Fuzzy AHP enhances the system’s ability to manage uncertainty and subjectivity in expert assessments, resulting in more consistent prioritization of performance indicators and improved decision accuracy. Data was obtained through interviews, questionnaires, and field observations, supported by institutional financial and performance reports. The developed system architecture—comprising a knowledge base, inference engine, and user interface—enables structured, transparent, and knowledge-driven budgeting analysis. The findings show that the system strengthens objectivity, coherence, and strategic alignment in the budgeting process while promoting accountability and efficiency in financial management. For university finance managers and administrators, this system provides a practical decision-support tool that facilitates data-based resource allocation and enhances institutional performance monitoring. The novelty of this research lies in the combination of Fuzzy AHP and KBS methodologies, offering an innovative model for intelligent, performance-oriented financial management in higher education institutions.
Assessing Smart Service Adoption in South African Townships: An Extended UTAUT Framework Nojila, Olebogeng Hellen; Chukwuere, Joshua; Gorejena, Karikoga
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1294

Abstract

The concept of smart cities has emerged globally in response to rapid urban migration. However, in South Africa, many citizens still live on the peripheries of urban centers due to spatial and socio-economic inequalities stemming from apartheid, which displaced and marginalized township and rural populations. This study explores the factors influencing the adoption and acceptance of smart services in South African townships and assesses the moderating effects of the Unified Theory of Acceptance and Use of Technology (UTAUT) variables. To enhance the UTAUT framework, the study incorporates trust, self-efficacy, and perceived risk as additional constructs. A random survey was distributed to township residents, with a targeted sample size of 384. A total of 472 valid responses were analyzed. The findings reveal that social influence, trust, perceived risk, income, and education significantly determine smart service adoption. Furthermore, age, gender, income, and education were found to moderate user behavior, impacting both acceptance and practical use of these services. The results offer valuable insights for policymakers and service providers in townships, highlighting the importance of understanding the roles of social influence, trust, security, income, and education. These insights can guide the development of inclusive smart services, tailored awareness campaigns, secure technologies, and targeted digital skills programs, ensuring that smart service initiatives are equitable and effective in township contexts.

Page 66 of 74 | Total Record : 733

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