Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics
Satria Abadi
Sultan Idris Education University
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparative Evaluation of Machine Learning Models with Class Imbalance Techniques for Employee Turnover Prediction Rudi Setiawan; Gatot Tri Pranoto; Zed Abdullah; Satria Abadi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1732

Abstract

Employee turnover prediction remains challenging in Human Resource (HR) analytics because class imbalance can reduce the ability of machine learning models to identify employees at genuine risk of leaving. This study develops and evaluates a comprehensive machine learning framework that balances minority-class detection and false-positive control. A publicly available HR dataset containing demographic, organizational, performance, and training-related attributes was analyzed using seven algorithms: Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, XGBoost, LightGBM, and CatBoost. Cost-sensitive learning and three resampling methods, SMOTEENN, ADASYN, and Tomek Links, were compared through stratified 10-fold cross-validation. Performance was evaluated using ROC-AUC, PR-AUC, Balanced Accuracy, Matthews Correlation Coefficient, G-Mean, Sensitivity, and Specificity, followed by threshold adjustment and SHAP analysis. Original LightGBM achieved the highest discrimination performance (ROC-AUC = 0.5975 ± 0.0546; PR-AUC = 0.2020 ± 0.0426), while cost-sensitive LightGBM produced the most balanced results (Balanced Accuracy = 0.5221 ± 0.0303; MCC = 0.0499 ± 0.0685). SHAP identified Department Type, Current Employee Rating, Training Cost, and Age as key predictors. Overall, integrating cost-sensitive learning, threshold optimization, and explainability improved model interpretability and practical utility for evidence-based HR decision-making processes in employee retention management and planning.
A Multimetric Evaluation of Ensemble and Neural Models for Imbalanced Credit Scoring on the German Credit Dataset Rino; Aditiya Hermawan; Elfindah Princes; Satria Abadi; Umarkhon Mukhamedov
Journal of Information System and Informatics Vol 8 No 5 (2026): October
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i5.1825

Abstract

Credit scoring models must balance global discrimination, minority-class detection, probability quality, and computational efficiency, particularly when default cases are less frequent than non-default cases. This study benchmarks Logistic Regression, Random Forest, XGBoost, and a Multilayer Perceptron using the German Credit Dataset, which contains 1,000 borrower records. All models were evaluated using a consistent preprocessing, partitioning, and imbalance-aware training protocol. Performance was assessed through ROC-AUC, PR-AUC, Balanced Accuracy, precision, recall, F1-score, Brier Score, and computational time. The results indicate moderate predictive performance across the evaluated models. XGBoost achieved the highest global discrimination with a ROC-AUC of 0.764, whereas the MLP obtained the highest PR-AUC of 0.378 and the lowest Brier Score of 0.123, indicating the best native probability quality among the evaluated models. No post-hoc probability calibration was applied. However, the MLP produced lower recall at the selected classification threshold. Logistic Regression required the least computation but recorded the lowest ROC-AUC and PR-AUC. Exploratory SHAP analysis was used to identify influential borrower attributes. These benchmark-specific findings demonstrate that model selection depends on the intended decision objective rather than architectural complexity alone.
Co-Authors Aditiya Hermawan Elfindah Princes Gatot Tri Pranoto Rino Rudi Setiawan Umarkhon Mukhamedov Zed Abdullah