Applying Machine Learning Algorithms to Predict Employee Turnover Intention: A Comparative Model Analysis

Siti Hadiaty Yuningsih
Fahmi Sidiq (Master of sains program, Faculty of Pharmacy, university Sultan Zainal Abidin, Kampung Gong Badak, 21300, Terengganu)
Yasir Salih (Department of Mathematics Education, Faculty of Education, Red Sea University, SUDAN)

International Journal of Quantitative Research and Modeling
Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)

Employee turnover represents a major challenge for organizations because it increases recruitment and training costs, disrupts operational continuity, and reduces organizational performance. Although machine learning has been widely applied to employee attrition prediction, most studies focus on comparing algorithms using the complete feature set, with limited attention to the predictive contribution of different employee information domains. This study aims to identify the most informative attribute domains for turnover prediction, compare the performance of ANN, RF, and SVM, and evaluate whether reduced-domain models can achieve performance comparable to full-feature models. The study utilized the IBM HR Analytics Employee Attrition dataset containing 1,470 employee records. Thirty predictive attributes were organized into six conceptual domains: Personal Information, Job Characteristics, Compensation, Work Environment, Career Development, and Relationship & Supervision. Twelve domain-based model configurations were developed and evaluated using ANN, RF, and SVM. Model development employed SMOTE to address class imbalance and repeated 10-fold cross-validation, while final evaluation was conducted on an independent holdout validation dataset. The results show that multi-domain models consistently outperform single-domain configurations. Compensation and Career Development emerged as the strongest standalone domains, while Work Environment was present in all top-performing models. The highest validation accuracy was achieved by M0-SVM (84.01%), whereas M11-SVM achieved comparable performance (82.65%) using only 16 attributes. M11-ANN produced the highest ROC AUC (0.782), indicating superior discriminative capability. Feature importance analysis identified OverTime, MonthlyIncome, Age, TotalWorkingYears, and YearsAtCompany as the most influential predictors. These findings demonstrate that domain composition is as important as algorithm selection in employee turnover prediction and highlight the importance of work environment, compensation, and career development factors in supporting data-driven employee retention strategies.