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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

- 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

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

- 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.

Analysis of Multilayer Perceptron, Long Short-Term Memory, and Temporal Convolutional Network Modeling Algorithms for Rainfall Prediction at Soekarno–Hatta Airport Eko Widyantoro; Eko Widyantoro HS; Sajarwo Anggai; Sudarno Wiharjo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7332

Tropical atmospheric variability poses challenges for daily precipitation prediction at Soekarno–Hatta International Airport, with implications for aviation safety. This study aimed to compare the performance of three deep learning architectures—Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Temporal Convolutional Network (TCN)—for daily precipitation prediction using meteorological observations from the BMKG Soekarno–Hatta Station for the period 2019–2025. A quantitative experimental approach was employed using a time-series split scheme to preserve the temporal structure of the data and reduce the risk of data leakage. Air temperature, relative humidity, atmospheric pressure, and wind speed were used as input variables, while precipitation was used as the target variable. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results showed that TCN achieved the best predictive performance, with an RMSE of 10.32 mm, MAE of 7.15 mm, MAPE of 18.27%, and R² of 0.87, followed by LSTM and MLP. Permutation-based sensitivity analysis identified relative humidity and air temperature as the two most influential variables for model prediction. Overall, TCN demonstrated stronger performance in capturing temporal patterns in daily precipitation than LSTM and MLP and showed potential for future application in operational precipitation forecasting and extreme-weather early warning systems at airport environments.

Face Image Authenticity Detection for ASN Attendance Using CNN MobileNetV2: A Case Study of Tangerang City Government Suryadi Suryadi; Sajarwo Anggai; Sudarno Wiharjo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7459

Face recognition-based attendance systems can improve the convenience of attendance processes, but they remain vulnerable to presentation attacks, in which facial images displayed on another device are used to simulate a user's physical presence. At the Tangerang City Government, the Tangerang AYO application is used as the main attendance channel for civil servants (ASN) across government agencies (OPD), with attendance data linked to the calculation of Additional Employee Income (TPP). This study develops a face liveness detection model to distinguish between real and fake facial images using a CNN MobileNetV2 architecture with a transfer learning and two-phase fine-tuning approach. The dataset was constructed from actual ASN attendance data and consisted of real and fake images representing facial displays through a secondary device. The model was evaluated using a confusion matrix, accuracy, precision, recall, specificity, F1-score, ROC curve, and AUC. On the testing data (n=301), the model achieved an accuracy of 97.34%, precision of 96.13%, recall of 98.68%, specificity of 96.00%, F1-score of 97.39%, and AUC of 0.9934 at a threshold of 0.5. Grad-CAM analysis showed that the model produced different activation patterns between real and fake images, including contextual visual information in real images and screen-surface characteristics in fake images. A FastAPI-based REST API prototype achieved an average response time of 306.48 ms under warm conditions without a dedicated GPU, indicating the potential feasibility of the model as an additional verification layer for an ASN attendance system.

Analysis of Early Detection of Automatic Weather Station Sensor Failures: A Comparative Study of Supervised Deep Neural Networks and Unsupervised Autoencoders Hasbullah Zuhri Hasibuan; Sudarno Wiharjo; Yan Mitha Djaksana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7593

Automatic Weather Stations (AWS) continuously collect and record meteorological data in real time and support weather information services. The reliability of AWS observations depends on sensor performance, as sensor failures, equipment degradation, and communication disturbances may produce anomalous data and reduce data quality. This study aims to develop an early-detection approach for AWS sensor anomalies using a Deep Neural Network (DNN) and an Autoencoder, compare their performance, and identify the sensors most frequently associated with anomalies. The dataset consists of 385,237 observations collected from the AWS Ancol station in North Jakarta from January to September 2025, covering nine meteorological and oceanographic parameters. The study involved data preprocessing, Min-Max normalization, model training, and evaluation using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC. The DNN achieved 99.6% accuracy, 0.922 precision, 0.996 recall, 0.958 F1-score, and 1.000 AUC. The Autoencoder achieved 95.8% accuracy, 0.578 precision, 0.023 recall, 0.043 F1-score, and 0.584 AUC. The AUC of 1.000 obtained by the DNN should be interpreted within the characteristics of the labeled dataset used in this study and should not be considered evidence of perfect generalization. Sensor analysis identified wind direction as the parameter most frequently associated with anomalies, contributing 46.67% of the detected anomalies in the DNN and 66.02% in the Autoencoder. These findings indicate that the supervised DNN performed better than the Autoencoder for anomaly detection on the labeled AWS dataset used in this study.

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