Stay or Leave? Predicting Employee Retention With Hybrid Deep Learning Models: Penelitian

Yessica Fara Desvia; Wafiqah Yasmin Azhar; Supriyadi Supriyadi; J. Rolles Herwin Sihombing; Nindy Faoziyah

doi:10.31004/jerkin.v4i4.6345

Authors

Yessica Fara Desvia Universitas Horizon Indonesia
Wafiqah Yasmin Azhar Universitas Horizon Indonesia
Supriyadi Supriyadi Universitas Horizon Indonesia
J. Rolles Herwin Sihombing Universitas Horizon Indonesia
Nindy Faoziyah Universitas Horizon Indonesia

DOI:

https://doi.org/10.31004/jerkin.v4i4.6345

Keywords:

Employee Retention, Hybrid Deep Learning, XGBoost and Deep Neural Network (DNN), Employee Turnover Prediction, SHAP Explainability

Abstract

Employee retention is a major challenge for organizations in the digital era because high turnover impacts productivity, operational costs, and organizational performance. This study proposes a Hybrid Deep Learning model based on XGBoost and Deep Neural Network (DNN) to predict employee retention using the HR_comma_sep dataset. This approach combines tree-based machine learning and deep learning to capture nonlinear relationships and complex decision patterns. Data preprocessing is performed through feature scaling and categorical encoding before model training. The hybrid architecture is built by integrating the probability outputs of XGBoost and DNN in the meta-classification layer. Evaluation using Accuracy, Precision, Recall, F1-Score, and AUC-ROC shows that the hybrid model has better prediction and generalization performance than conventional methods. SHAP Explainability is used to identify the main factors influencing turnover, namely job satisfaction, average monthly working hours, and length of service. This model can help organizations develop proactive HR management strategies.

References

Deep Learning oleh Ian Goodfellow, Yoshua Bengio, dan Aaron Courville. Cambridge: MIT Press; 2016.

Human Resource Analytics: Minbaeva D. Human resource analytics: A systematization of basic concepts. Human Resource Management Review. 2018;29(1):3–16.

Dwivedi YK, Hughes L, et al. Artificial Intelligence for employee attrition prediction: A review. International Journal of Information Management. 2021;58:102318.

Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems. 2017;30:4765–4774.

Chandar S, Sumathi P. Predicting employee attrition using machine learning algorithms. International Journal of Engineering and Technology. 2019;7(2):610–615.

Khera R, Kumar V. Deep learning approaches for predicting employee turnover. Expert Systems with Applications. 2022;188:116018.

Park J, Feng Y, Jeong SP. Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques. Scientific Reports. 2024;14:1221.

Quinteros DM. Predictive modelling of employee attrition using deep learning. Acadlore Transactions on AI and Machine Learning. 2023;2(4):212–225.

Duan L, Paknejad J, Kim H. Employee attrition prediction with convolutional neural network and synthetic minority over-sampling technique. Journal of Business Analytics. 2024;8(1):24–35.

Lim CS, Malik EF, Khaw KW, et al. Hybrid GA–DeepAutoencoder–KNN Model for Employee Turnover Prediction. Statistics, Optimization & Information Computing. 2024;12(1):75–90.

Shiri FM, Yamaguchi S, Ahmadon MAB. A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition. Applied Sciences. 2025;15(6):2984.

Liu H, Ge Y. Employee Turnover Prediction: A Cross-component Attention Transformer with Consideration of Competitor Influence and Contagious Effect. arXiv preprint. 2025.

Ma X, Liu W, Zhao C, Tukhvatulina LR. Can Large Language Model Predict Employee Attrition? arXiv preprint. 2024.

Shafie MR, Khosravi H, Farhadpour S, et al. A cluster-based human resources analytics for predicting employee turnover using optimized Artificial Neural Networks and data augmentation. Decision Analytics Journal. 2024;11:100461.

Alqahtani H, Almagrabi H, Alharbi A. Employee attrition prediction using machine learning models: A review paper. International Journal of Artificial Intelligence & Applications. 2024;15(2):23–49.

A decade of research on machine learning techniques for predicting employee turnover: A systematic literature review. Expert Systems with Applications. 2024;238:121794.

Predictive model of employee attrition based on stacking ensemble learning. Expert Systems with Applications. 2023;215:119364.

Siregar IM, Othman ZA, Abu Bakar A. Deep Learning Based Recommendation System for Employee Retention Using Bipartite Link Prediction. Jurnal INTECH Teknik Industri Universitas Serang Raya. 2025;11(1).

Siregar AAF, Utami E, Sari TN. A Hybrid Case-Based Reasoning Framework Using KNN, Word2Vec, and Cosine Similarity for Employee Attrition Analysis. JUITA: Jurnal Informatika. 2026;14(1).

Derrazi A, Sharami JPR. Integrating SAINT with Tree-Based Models: A Case Study in Employee Attrition Prediction. arXiv preprint. 2026.