Machine learning model can be used to predict gas turbine generator capacity at PT Saka Energi Pangkah Limited as the foundation of anomaly detector system. Previous research shown that the using of ANN model resulting adequate performance to predict gas turbine generator. However, the using of ANN in the plant has several drawbacks for example high cost computation, low accuracy and precision. In this research, machine learning approach were selected to improve performance of the predictor model. Decision tree regressor is one of many machine learning models which can also can be used to predict gas turbine generator capacity. The test results show that the best model using decision tree regressor is obtained by providing a data ratio for training and testing of 70:30 (type-3) with an MAE value of 0.821, MSE of 1.329, R2 of 0.998, EVS of 0.998 and RMSE of 1.115..
|Title of host publication
|Proceedings of the International Conference on Advanced Technology and Multidiscipline, ICATAM 2021
|Subtitle of host publication
|"Advanced Technology and Multidisciplinary Prospective Towards Bright Future" Faculty of Advanced Technology and Multidiscipline
|Prihartini Widiyanti, Prastika Krisma Jiwanti, Gunawan Setia Prihandana, Ratih Ardiati Ningrum, Rizki Putra Prastio, Herlambang Setiadi, Intan Nurul Rizki
|American Institute of Physics Inc.
|Published - 19 May 2023
|1st International Conference on Advanced Technology and Multidiscipline: Advanced Technology and Multidisciplinary Prospective Towards Bright Future, ICATAM 2021 - Virtual, Online
Duration: 13 Oct 2021 → 14 Oct 2021
|AIP Conference Proceedings
|1st International Conference on Advanced Technology and Multidiscipline: Advanced Technology and Multidisciplinary Prospective Towards Bright Future, ICATAM 2021
|13/10/21 → 14/10/21