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All Journal Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Teknologi Informasi MURA Jurnal Teknologi Informasi Mura
M. Agus Syamsul Arifin
Universitas Jenderal Soedirman
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Other

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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
MODEL MACHINE LEARNING TREE BASED UNTUK DETEKSI SERANGAN PADA SISTEM CHARGING ELECTRIC VEHICLE Ucky Pradestha Novettralita; Azis Amirulbahar; Emha Diambang Ramadhany; M. Agus Syamsul Arifin
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2755

Abstract

Cyberattack detection in Electric Vehicle Charging Infrastructure (EVCI) is increasingly critical as the global transition toward electric mobility accelerates to reduce carbon emissions. This study provides a comprehensive evaluation of machine learning models for cyberattack detection using the CICSEV2024 dataset. The performance of tree-based algorithms, including Decision Trees (DT), Random Forest (RF), and Gradient Boosting (GB), is compared to identify effective yet interpretable models. Experimental results demonstrate that these models achieve exceptional performance, with DT, RF, and GB reaching 100% accuracy and precision. Furthermore, 10-fold cross-validation on an imbalanced dataset (Benign class) confirms the models’ consistency, maintaining a score of 1.00 across all iterations. The proposed models also achieve a perfect Area Under the Curve (AUC) score of 1.00, indicating their robustness and reliability in detecting cyberattacks. The findings highlight that simple and interpretable tree-based models can achieve state-of-the-art performance in EVCI cybersecurity detection, offering practical implications for enhancing the security of electric vehicle charging infrastructures in real-world deployments.
MODEL MACHINE LEARNING TREE BASED UNTUK DETEKSI SERANGAN PADA SISTEM CHARGING ELECTRIC VEHICLE Ucky Pradestha Novettralita; Azis Amirulbahar; Emha Diambang Ramadhany; M. Agus Syamsul Arifin
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2755

Abstract

Cyberattack detection in Electric Vehicle Charging Infrastructure (EVCI) is increasingly critical as the global transition toward electric mobility accelerates to reduce carbon emissions. This study provides a comprehensive evaluation of machine learning models for cyberattack detection using the CICSEV2024 dataset. The performance of tree-based algorithms, including Decision Trees (DT), Random Forest (RF), and Gradient Boosting (GB), is compared to identify effective yet interpretable models. Experimental results demonstrate that these models achieve exceptional performance, with DT, RF, and GB reaching 100% accuracy and precision. Furthermore, 10-fold cross-validation on an imbalanced dataset (Benign class) confirms the models’ consistency, maintaining a score of 1.00 across all iterations. The proposed models also achieve a perfect Area Under the Curve (AUC) score of 1.00, indicating their robustness and reliability in detecting cyberattacks. The findings highlight that simple and interpretable tree-based models can achieve state-of-the-art performance in EVCI cybersecurity detection, offering practical implications for enhancing the security of electric vehicle charging infrastructures in real-world deployments.
Co-Authors Azis Amirulbahar Dinda Wahyu Anggraeni Emha Diambang Ramadhany Moeng Sakmar Ucky Pradestha Novettralita Ucky Pradestha Novettralita