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All Journal Jurnal Teknologi Informatika dan Komputer International Journal Software Engineering and Computer Science (IJSECS)
Sudarno Wiharjo
Universitas Pamulang
Computer Science & IT Electrical & Electronics Engineering

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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 Syahrul Kahfi; Sudarno Wiharjo; Abu Khalid Rivai
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5772

Abstract

- 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.
Sentiment Analysis of the Indonesian Megathrust Earthquake and Tsunami Issue Using BERT and Roberta Methods Hendra Rahman; Taswanda Taryo; Sudarno Wiharjo
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3563

Abstract

The megathrust earthquake in Indonesia is a major potential natural disaster capable of triggering high-magnitude earthquakes and tsunamis, thereby influencing public perception. This study aims to analyze public opinion and identify the main topics related to the megathrust earthquake issue using Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa) models. The dataset consists of 16,592 comments collected from the X social media platform during the period 2012–2025, which were classified into three sentiment categories, positive, negative, and neutral. The research methodology included exploratory data analysis, text preprocessing, model training, and evaluation using four experimental scenarios. The results indicate that the best performance was achieved using an 80:10:10 train–validation test split with ten training epochs. The BERT model outperformed RoBERTa, achieving an accuracy of 92,4350%, precision of 92,4291%, recall of 92,4350%, and F1-score of 92,4292%. These findings demonstrate that BERT is more effective in capturing the linguistic context of the Indonesian language. Furthermore, this study contributes to the advancement of artificial intelligence-based sentiment analysis for monitoring public opinion on disaster-related issues and provides a valuable foundation for developing more effective risk communication strategies, disaster mitigation education, and evidence-based policymaking that is more responsive to public perception.
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 Syahrul Kahfi; Sudarno Wiharjo; Abu Khalid Rivai
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5772

Abstract

- 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.
Co-Authors Abu Khalid Rivai Hendra Rahman Syahrul Kahfi Taswanda Taryo