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Aminuddin, Amir
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Prediksi Downtime Mesin Computer Numerical Control dengan Pendekatan Ensemble Learning Aminuddin, Amir; Shariff, Adam; Mahmad Khairai, Kamarulzaman
Jurnal Studi Multidisiplin Qomaruna Vol 3 No 1 (2025): December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Universitas Qomaruddin, Gresik, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62048/qjms.v3i1.137

Abstract

Computer Numerical Control (CNC) machining is a subtractive manufacturing technique that removes layers of material from a blank or workpiece to create a specific product. With increasing global competition, minimizing downtime during production is essential to maximize machine availability and productivity. This study investigates the application of machine learning models, specifically Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to forecast CNC machine downtime from multiple failure sources. The study uses data collected from 16 CNC machines at Company A in Malaysia over an extended period. The data contain key variables for each downtime event, such as machine ID, failure type, start date/time, end date/time, and downtime duration in minutes. Failure types are categorized into several groups, including mechanical, electrical, and tool malfunctions.  After hyperparameter tuning, the XGBoost model outperformed the RF model, achieving a Mean Squared Error (MSE) of 0.4017, Root MSE (RMSE) of 0.634, and Mean Absolute Error (MAE) of 0.470 on the test set, while the RF model yielded higher errors, with an MSE of 1.2654, RMSE of 1.125, and MAE of 0.943. These results demonstrate the superiority of the XGBoost model over RF in predicting future CNC downtime, as indicated by its lower prediction errors. Future work should focus on refining the model with larger, more diverse datasets and exploring its integration into AI-based decision support systems to enhance machine availability and operational efficiency.
Sistem Rekomendasi Lokasi Optimal dan Potensi Penghematan Energi Pemasangan PLTS Atap Berbasis AI di Pulau Jawa Aminuddin, Amir; Supanto, Supanto; Saputra, Hadi; Herawati, Neng Ayu; Purwarianti, Ayu; Utama, Nugraha Priya
Jurnal Infomedia: Teknik Informatika, Multimedia, dan Jaringan Vol 10, No 2 (2025): Jurnal Infomedia
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jim.v10i2.7219

Abstract

Transisi menuju energi terbarukan di Indonesia menuntut pendekatan berbasis data dalam menentukan lokasi optimal pemasangan Pembangkit Listrik Tenaga Surya (PLTS) atap dan dalam memperkirakan dampak ekonomisnya. Penelitian ini mengembangkan sistem rekomendasi berbasis Artificial Intelligence (AI) yang mengintegrasikan data penyinaran matahari dari BMKG dan data konsumsi listrik dari PLN untuk mendukung perencanaan PLTS atap di Pulau Jawa. Pendekatan dilakukan melalui tiga metode pembelajaran mesin utama: klasifikasi untuk mengevaluasi kelayakan pelanggan, klasterisasi wilayah menggunakan algoritma clustering, dan regresi untuk memprediksi potensi penghematan energi. Lima algoritma klasifikasi dibandingkan, dengan LightGBM menunjukkan performa tertinggi (akurasi 87%). Segmentasi wilayah optimal diperoleh melalui KMeans (silhouette score 0,5566). Estimasi penghematan energi paling akurat dihasilkan oleh XGBoost Regressor dengan koefisien determinasi (R²) sebesar 0,9999. Hasil ini menunjukkan bahwa pendekatan integratif berbasis AI dapat menyediakan informasi prediktif yang akurat dan aplikatif bagi penyusunan strategi promosi dan investasi PLTS atap, sekaligus memberikan estimasi manfaat kuantitatif bagi pelanggan. Penelitian ini memberikan kontribusi signifikan dalam pengembangan sistem pendukung keputusan untuk energi terbarukan berbasis analitik spasial dan perilaku konsumsi.
Co-Authors Ayu Purwarianti Hadi Saputra Mahmad Khairai, Kamarulzaman Neng Ayu Herawati Shariff, Adam Supanto Supanto Utama, Nugraha Priya