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All Journal Prosiding Seminar Nasional Teknik Mesin Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal of Islamic Economics Lariba
Syarif, Muhyiddin
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Religion Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)

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Prediksi Nilai Unburned Carbon Batubara yang Dihasilkan PLTU Menggunakan Algoritma Linear Regression, Random Forest, dan LightGBM Regression Syarif, Muhyiddin; Afiyati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3313

Abstract

This study focuses on predicting unburned carbon levels in coal-fired power plants to enhance operational efficiency. Accurate prediction of unburned carbon is crucial as it directly affects fuel combustion efficiency and environmental sustainability. The research compares three machine learning algorithms: Linear Regression, Random Forest, and LightGBM Regression, using performance metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results show that LightGBM Regression performs the best, with MAE of 0.31, MAPE of 1.29, and RMSE of 0.38, outperforming the other two models. This model can be further optimized to improve prediction accuracy, contributing to more efficient and environmentally friendly power plant operations. The application of machine learning in this study supports data-driven decision-making in the energy sector.
Prediksi Nilai Unburned Carbon Batubara yang Dihasilkan PLTU Menggunakan Algoritma Linear Regression, Random Forest, dan LightGBM Regression Syarif, Muhyiddin; Afiyati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3313

Abstract

This study focuses on predicting unburned carbon levels in coal-fired power plants to enhance operational efficiency. Accurate prediction of unburned carbon is crucial as it directly affects fuel combustion efficiency and environmental sustainability. The research compares three machine learning algorithms: Linear Regression, Random Forest, and LightGBM Regression, using performance metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results show that LightGBM Regression performs the best, with MAE of 0.31, MAPE of 1.29, and RMSE of 0.38, outperforming the other two models. This model can be further optimized to improve prediction accuracy, contributing to more efficient and environmentally friendly power plant operations. The application of machine learning in this study supports data-driven decision-making in the energy sector.
Co-Authors Abdillah, Amin Afiyati Herman, Sebastian Septian, Vionanda Sudarmawan, R. Grenny