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All Journal International Journal Software Engineering and Computer Science (IJSECS) Jurnal Ilmiah Ilmu Komputer dan Teknologi Informasi (JIKOMTI)
Fazly Qusyairy, Muhammad
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Computer Science & IT

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Systematic Literature Review: Web-Based Payroll Information System Software Development Methods Fazly Qusyairy, Muhammad; Maulana, Imron Rizki; Putri Kamilah, Salwa
JIKOMTI : Jurnal Ilmiah Ilmu Komputer dan Teknologi Informasi Vol. 2 No. 2 (2025): JIKOMTI: DESEMBER 2025
Publisher : Universitas Sains Indonesia Publishing

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Abstract

This research aims to conduct a systematic review of software development methods used in creating web-based payroll information systems, focusing on two primary methodologies: Waterfall and Agile. Payroll information systems are a crucial component of human resource management, demanding reliability, security, and easy access via a web platform. Through a Systematic Literature Review (SLR) approach, this study gathered and analyzed various research discussing the application of Waterfall and Agile methods in developing web-based payroll systems. The review's findings indicate that the Waterfall method is frequently employed in projects with stable and clearly defined requirements. In contrast, the Agile method is chosen for its flexibility in quickly accommodating changing user needs. Security aspects, integration with other systems, and efficient data management are key focuses in payroll system development. This research concludes that the choice of development method must align with project characteristics and organizational needs to ensure web-based payroll systems are developed effectively and efficiently. These findings can serve as a reference for developers and researchers in designing optimal payroll information systems.
Data Mining for Predicting Creditworthiness in Credit Card Approval: A Systematic Literature Review Purnama Magribi, Wahyu; Fazly Qusyairy, Muhammad; Saputra, Tino
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6618

Abstract

The growing volume of credit card applications has led financial institutions to seek faster and more reliable methods in the approval process. Manual evaluation is not only time-consuming but also susceptible to human error, which can result in poor credit decisions and measurable financial losses. This study conducts a Systematic Literature Review (SLR) to examine data mining techniques applied to creditworthiness prediction. Five research questions were formulated to identify: (1) commonly used data mining techniques, (2) frequently used datasets, (3) performance evaluation metrics, (4) algorithms with the strongest performance, and (5) recurring challenges and practical recommendations. A structured search across three academic databases — Scopus, Google Scholar, and GARUDA — yielded 8 relevant articles (7 primary experimental studies and 1 secondary study) published between 2021 and 2025. The findings show that Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors are the most widely applied methods. Tree-based algorithms such as Decision Tree and Random Forest consistently yield high accuracy, while K-Nearest Neighbors also delivers strong results in specific experimental settings. Naïve Bayes appears most frequently across studies, and its performance can be improved through metaheuristic approaches such as Particle Swarm Optimization (PSO). Standard evaluation metrics include accuracy, precision, recall, F1-score, and AUC-ROC. The review underscores the importance of data preprocessing, class imbalance handling, and hyperparameter tuning in building reliable prediction models — findings with direct implications for financial institutions seeking to reduce non-performing loan rates.
Co-Authors Maulana, Imron Rizki Purnama Magribi, Wahyu Putri Kamilah, Salwa Saputra, Tino