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All Journal International Journal Software Engineering and Computer Science (IJSECS) Jurnal Ilmiah Ilmu Komputer dan Teknologi Informasi (JIKOMTI)
Purnama Magribi, Wahyu
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Computer Science & IT

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Arsitektur Sistem Presensi Berbasis QR: Studi Kasus Bank bjb KCP Samsat Kota Bekasi Purnama Magribi, Wahyu; Muhammad Fazly Qusyairy
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

The employee attendance system at Bank BJB KCP Samsat Kota Bekasi was previously done manually using paper attendance sheets, which had the potential to cause irregularities, lack of discipline, and errors in attendance records. To overcome these problems, this study aims to develop a computerized QR Code-based attendance information system using smartphones. This system is designed to simplify the attendance process, improve data accuracy, and minimize fraud in employee attendance recording. The system development utilizes Android Studio for the front end and a web-based design method with the CodeIgniter framework, MySQL database, and XAMPP server for the back end. Key features of the system include user login based on access levels, QR Code scanning for attendance, employee data management, manual attendance, attendance history, daily/monthly/annual report summaries, and report export to PDF format. The implementation results show that the system can replace manual processes with more efficient, faster, and well-documented procedures. System testing was conducted through alpha and beta testing, as well as black box testing, which confirmed that the system's functionality operates as expected. With this system in place, it is hoped that employee attendance processes will become more transparent, accountable, and supportive of improved discipline in the workplace.
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 Fazly Qusyairy, Muhammad Muhammad Fazly Qusyairy Saputra, Tino