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All Journal Building of Informatics, Technology and Science Journal of Electrical Engineering and Computer (JEECOM)
Muhammad Faisal
Universitas Islam Negeri Maulana Malik Ibrahim, Malang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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Predicting Sleep Quality Using Hybrid Stacking Ensemble Of Random Forest And Catboost With Shap Interpretability Baitullah Hakim; Muhammad Faisal; Mochamad Imamudin
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17445

Abstract

Sleep quality is an important indicator of physical and mental well-being, yet its prediction from lifestyle variables remains challenging because behavioral and physiological factors interact nonlinearly. This study develops a hybrid stacking ensemble that combines Random Forest and CatBoost as base learners and Logistic Regression as the meta-classifier for three-class sleep-quality prediction (Poor, Moderate, Good). The Sleep Health and Lifestyle Dataset contains 374 records, of which only 109 are unique, and is preprocessed through blood-pressure decomposition, derived-feature construction, categorical encoding, and numerical standardization. Models are evaluated with Accuracy, Precision, Recall, F1-Score, and multiclass ROC-AUC under Stratified 10-Fold Cross Validation, both in its standard form and in a duplicate-aware form in which identical records are kept in the same fold. Under the duplicate-aware protocol, Random Forest achieved 97.86% Accuracy, 91.06% macro F1-Score, and 0.9970 ROC-AUC; CatBoost achieved 97.33%, 90.70%, and 0.9930; and Hybrid Stacking achieved 97.06%, 90.51%, and 0.9784. Recall for the minority Poor class (n = 12) was 66.7% for all three models, and stacking did not improve on its base learners. SHAP analysis identifies Stress Level and Sleep Duration as the two dominant features. The results are specific to this small, largely duplicated dataset and do not support clinical use.
Comparison of Global Horizontal Irradiance Prediction Using LSTM and M-LSTM Across Seasonal Conditions Rudi Kasianto; Totok Chamidy; Muhammad Faisal; M. Ainul Yaqin; M. Imamuddin
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11094

Abstract

Short-term prediction of Global Horizontal Irradiance (GHI) is challenging because solar radiation exhibits dynamic temporal patterns and rapidly responds to changing atmospheric conditions. This study compares univariate Long Short-Term Memory (LSTM) and multivariate LSTM models using four and six predictor variables (M-LSTM-4 and M-LSTM-6) for 30-minute-ahead GHI forecasting using Automatic Solar Radiation Station data from Malang, East Java. One-minute observations were aggregated into 30-minute intervals and evaluated using sliding windows of 12, 24, and 36 timesteps. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²) for full-year, wet-season, and dry-season datasets. For the full-year dataset, M-LSTM-6 with a 12-timestep window achieved the best performance, with an MAE of 37.700 W/m², an RMSE of 79.340 W/m², and an R² of 0.9283. In contrast, M-LSTM-4 provided the best performance for both seasonal datasets using a 36-timestep window, achieving an MAE of 36.551 W/m² and an R² of 0.9378 during the wet season, and an MAE of 39.181 W/m² and an R² of 0.9454 during the dry season. M-LSTM-6 showed substantial performance degradation with longer historical windows, particularly during the dry season. This result suggests that combining a larger number of predictors with longer historical sequences increases the amount of input information processed by the model, which may be less effective when the additional historical information does not provide proportional predictive benefit under rapidly varying irradiance conditions. The results demonstrate that the effectiveness of multivariate information depends on predictor relevance, historical window length, and seasonal conditions. The main contribution of this study is the identification of different optimal combinations of predictor complexity and historical window length between full-year and seasonal GHI forecasting. Overall, the findings indicate that input-variable complexity and window length should be jointly optimized for local short-term GHI forecasting.
Co-Authors Baitullah Hakim M. Ainul Yaqin M. Imamuddin Mochamad Imamudin Rudi Kasianto Totok Chamidy