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All Journal Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Pradipto, Dimas
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Arsitektur Sistem Informasi dengan Metode Agile Framework Scrum pada Pelayanan PMKS dan Pemberdayaan Kesejahteraan Sosial Ropianto, Muhammad; Pradipto, Dimas; Jarti, Nanda; Fernandes, Atman Lucky
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.8458

Abstract

This study aims to design and implement a database-based information system for managing Social Welfare Issues (PMKS) and Social Welfare Potential and Resources (PSKS) in XYZ Village, XYZ District. Currently, PMKS and PSKS data management is still carried out using Microsoft Excel, which causes data entry errors, such as names, population registration numbers (NIK), and recipient addresses. In addition, data retrieval is difficult, and reports take up a lot of file storage space. Microsoft Excel is also not well integrated and does not allow for direct monitoring. The information system was developed using the Agile approach with the Scrum method and designed using database-based programming, PHP, and MySQL. In the design stage, the Waterfall method was used to design the system, and modeling using Unified Modeling Language (UML) was used to describe the structure and interactions between system components. The results of this research include needs analysis, system design with UML modeling, and 64 tables that support system development. System testing was conducted using Black Box Testing, which involved the PMKS Admin, PSKS Admin, and Public Services related to PMKS and PSKS data management in XYZ Village, XYZ District. The test results showed that the developed information system was successful and could facilitate data management, information retrieval, and report generation, as well as facilitate direct monitoring. In addition, this system requires data classification on the dashboard to display information in a more executive manner, particularly to support annual reports. To maintain the sustainability of this system, it is necessary to improve human resources in the operation and maintenance of the system so that it can continue to function properly and support the management of PMKS and PSKS in XYZ Village, XYZ District.
Comparative Analysis of Random Forest and Support Vector Machine Algorithms for Predicting Student Retention at Ibnu Sina University pradipto, dimas; Agastya, I Made Artha; Suryadi, Agus
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6298

Abstract

Student retention is a critical challenge facing higher education institutions, including Ibnu Sina University (UIS), where a significant proportion of students risk not completing their studies. Purpose: This study develops and compares predictive models using Random Forest (RF) and Support Vector Machine (SVM) algorithms to classify student retention into three categories: Active, At-Risk, and Inactive. Methods: Administrative data from 2,389 students across 6 study programs (2021/2022–2023/2024 cohorts) were used, encompassing 18 predictor variables including academic performance (GPA, failed credits), demographic, and socio-economic factors. Class imbalance was handled using SMOTE, and hyperparameter optimization was performed via Grid Search with 5-Fold Cross Validation. Results: RF outperformed SVM across all metrics, achieving accuracy of 92.24%, weighted F1-Score of 92.38%, and macro F1-Score of 82.67%, compared to SVM's 87.63% and 87.79%. Feature importance identified Total Failed Credits (0.2847) and Cumulative GPA (0.2134) as the strongest predictors. Novelty: Unlike prior studies focusing solely on academic data, this research integrates non-academic variables (leave history, parental income) and explicitly addresses class imbalance via SMOTE in a multi-class Indonesian higher education context, providing a practical Early Warning System (EWS) framework.
Co-Authors Agastya, I Made Artha Agus Suryadi Fernandes, Atman Lucky Nanda Jarti Ropianto, Muhammad