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All Journal Jusikom : Jurnal Sistem Komputer Musirawas Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi
Moeng Sakmar
Universitas Jenderal Soedirman
Computer Science & IT Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Evektivitas Xgboost Lightgbm dan Catboost pada Dataset Imbalanced Predictive Maintenance Moeng Sakmar; Nurul Tiara Kadir; Puteri Awaliatush Shofo; Agus Darmawan
Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Vol. 3 No. 1 (2026): SINTA: JANUARI
Publisher : Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/sinta.v3i1.145

Abstract

In the era of Industry 4.0, unexpected machine failures have become a critical challenge, triggering unplanned downtime and significant financial losses for the manufacturing sector. A fundamental obstacle in the development of Machine Learning-based Predictive Maintenance systems is data imbalance, where damage incidents occur much less frequently than normal conditions, causing models to become biased and fail to recognize vital anomalies. This study aims to evaluate the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in optimizing failure detection performance on the AI4I 2020 dataset. It uses a comparative approach with three Gradient Boosting algorithms: XGBoost, LightGBM, and CatBoost. This study highlights the Accuracy Paradox phenomenon in scenarios without resampling, where high spurious accuracy masks the model's inability to detect failures or low Recall. The findings of this study show that the integration of SMOTE successfully reconstructs the model's decision boundaries, thereby significantly increasing sensitivity to minority classes. Based on an in-depth analysis using the Confusion Matrix, the XGBoost algorithm combined with SMOTE was identified as the most optimal model, as it effectively balanced critical trade-offs by achieving a high Recall to ensure asset safety, while minimizing false alarms (False Positives) that impact technician work efficiency, compared to its competitors. This study concludes that addressing data imbalance is a deterministic step in building a predictive maintenance system that is not only technically precise but also reliable and safe for implementation in real industrial ecosystems.
MODEL ENSEMBLE TREE-BASED UNTUK PREDIKSI KELEMBABAN TANAH BERBASIS DAQ ARDUINO Ucky Pradestha Novettralita; Dinda Wahyu Anggraeni; Moeng Sakmar; M. Agus Syamsul Arifin
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3126

Abstract

Soil moisture is a critical parameter in agricultural and hydrological systems that requires accurate monitoring to support smart irrigation management. This study aims to develop a soil moisture forecasting model based on stacking ensemble machine learning using time series data from an Arduino-based Data Acquisition (DAQ) system. The dataset includes environmental variables such as atmospheric temperature, soil temperature, humidity, soil_moisture, and dew point with hourly temporal resolution over the 2017–2018 period. The research stages include data preprocessing (missing value handling, interpolation, outlier handling using the IQR method, and resampling), feature engineering (temporal feature extraction, lag features, and rolling windows), and modeling using six tree-based methods: Random Forest, Gradient Boosting, Extra Trees, LightGBM, XGBoost, and CatBoost. The three best-performing models (CatBoost, LightGBM, and Gradient Boosting) were combined using Ridge Regression as a meta-learner. Evaluation was conducted through time-series cross-validation with MSE, RMSE, MAE, R², and MAPE metrics. Test results show the Ridge Ensemble achieved MSE of 52.21, RMSE of 7.23, MAE of 5.16, R² of 0.9652, and MAPE of 9.07%. The stacking ensemble approach outperformed individual models and shows strong potential for real-time deployment in IoT systems to support precision agriculture.
Co-Authors Agus Darmawan Dinda Wahyu Anggraeni M. Agus Syamsul Arifin Nurul Tiara Kadir Puteri Awaliatush Shofo Ucky Pradestha Novettralita