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All Journal Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK) Journal of Applied Computer Science and Technology (JACOST)
Stefanie Quinevera
Universitas Widya Dharma Pontianak
Computer Science & IT Economics, Econometrics & Finance Mathematics Social Sciences

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Klasifikasi Pemohon Pinjaman dengan Hyperparameter Tuning dan Teknik Penyeimbangan Data Donata Yulvida; Stefanie Quinevera; Ricky Mardianto; Steven Joses
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/krjtrh05

Abstract

Loan classification is a critical component of credit risk management, as it categorizes loans based on risk levels and supports the financial stability of banks, where loan-related income represents a substantial share of assets. Effective classification aims to ensure secure asset allocation, minimize credit risk, and prevent potential repayment issues. This study enhances loan classification performance through two strategies: hyperparameter optimization of Decision Tree and Random Forest algorithms, and data balancing techniques to address class imbalance. Experimental results show that the Decision Tree achieves 89.21% accuracy with an F1-Score of 70.17%, while the Random Forest demonstrates higher performance, reaching 94.04% accuracy and an F1-Score of 79.75%. Random Oversampling reduces bias toward majority classes by improving model sensitivity, while hyperparameter tuning with GridSearchCV identifies optimal parameter settings, thereby strengthening predictive performance. The findings highlight that combining data balancing with hyperparameter optimization effectively improves accuracy and F1-Scores. These approaches are not limited to the algorithms tested but can also be applied to other classification methods, offering broader potential for enhancing credit risk prediction in banking.
Analysis Design and Development of a Web-Based Human Resource Information System Using Agile Methodology at CV XYZ Stefanie Quinevera; Desta Ovilini; Desiana Dian Malasari
SIMAK Vol. 24 No. 01 (2026): Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK)
Publisher : Faculty of Economics dan Business, Atma Jaya Makassar University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35129/simak.v24i01.678

Abstract

This study aims to analyze and design a web-based Human Resource Information System (HRIS) at CV XYZ, a company engaged in the distribution of fast moving consumer goods. The current human resource management process is still conducted manually, leading to inefficiencies, data inconsistencies, and delays in report generation. Data were collected through interviews, observation, documentation review, and literature study. The collected data were analyzed using a descriptive analysis approach to identify system requirements and existing problems. The system was developed using the Agile software development approach and modeled using Unified Modeling Language (UML). The results of this study produce an integrated HRIS that supports employee data management, attendance, payroll, leave, and shift scheduling within a single platform. The proposed system provides a structured solution for managing human resource data and demonstrates the potential to improve efficiency, accuracy, and accessibility of information. The system can also serve as a reference model for similar organizations facing comparable human resource management challenges.
Co-Authors Desiana Dian Malasari Desta Ovilini Donata Yulvida Ricky Mardianto Steven Joses