Published: 2025-12-01
Optimization of Employee Burnout Prediction Using Explainable Boosting Machine, Long Short-Term Memory, and Extreme Gradient Boosting Methods in Human Resource Management at PT. XYZ
DOI: 10.35870/ijsecs.v5i3.5772
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Abstract
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Employee burnout threatens organizational sustainability through reduced productivity, compromised mental health, and elevated turnover rates. Early detection remains critical for maintaining workforce stability. We address burnout prediction optimization at PT. XYZ through three advanced machine learning models: Explainable Boosting Machine (EBM), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost). Our methodology incorporates structured data preprocessing, model construction, training protocols, and rigorous performance evaluation. We assessed models using MAE, RMSE, and R² for regression tasks, alongside Accuracy, Precision, Recall, F1-score, Confusion Matrix, Feature Importance, and ROC curves for classification. Cross-validation ensured robust evaluation, with burnout labels derived from established psychosocial factor assessments. Results reveal LSTM's superior performance at 0.99 accuracy, followed by EBM (0.96) and XGBoost (0.95). LSTM demonstrates exceptional capability in identifying subtle burnout patterns, while EBM delivers high interpretability regarding causal factors. These findings offer a data-driven framework for human resource management, enabling precise, proactive intervention through evidence-based decision-making.
Keywords
Employee Burnout ; LSTM ; Explainable Boosting Machine ; XGBoost ; Human Resource Management ; Psychosocial Factors
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 3 (2025)
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Section: Articles
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/ijsecs.v5i3.5772
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Syahrul Kahfi
Postgraduate, Master of Informatics Engineering, Universitas Pamulang, South Tangerang City, Banten Province, Indonesia
Sudarno Wiharjo
Postgraduate, Master of Informatics Engineering, Universitas Pamulang, South Tangerang City, Banten Province, Indonesia
Abu Khalid Rivai
Postgraduate, Master of Informatics Engineering, Universitas Pamulang, South Tangerang City, Banten Province, Indonesia
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