TY - GEN
T1 - Generator Capacity Predictor System Modeling Using Decision Tree Regressor at PT Saka Indonesia Pangkah Limited
AU - Utama, Sangsaka Wira
AU - Asy'Ari, Muhammad Khamim
AU - Karlina, Diyajeng Luluk
AU - Ashidiqqi, Muhammad Roy
AU - Raafi'U, Brian
N1 - Publisher Copyright:
© 2023 American Institute of Physics Inc.. All rights reserved.
PY - 2023/5/19
Y1 - 2023/5/19
N2 - 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..
AB - 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..
UR - http://www.scopus.com/inward/record.url?scp=85161421577&partnerID=8YFLogxK
U2 - 10.1063/5.0136775
DO - 10.1063/5.0136775
M3 - Conference contribution
AN - SCOPUS:85161421577
T3 - AIP Conference Proceedings
BT - Proceedings of the International Conference on Advanced Technology and Multidiscipline, ICATAM 2021
A2 - Widiyanti, Prihartini
A2 - Jiwanti, Prastika Krisma
A2 - Prihandana, Gunawan Setia
A2 - Ningrum, Ratih Ardiati
A2 - Prastio, Rizki Putra
A2 - Setiadi, Herlambang
A2 - Rizki, Intan Nurul
PB - American Institute of Physics Inc.
T2 - 1st International Conference on Advanced Technology and Multidiscipline: Advanced Technology and Multidisciplinary Prospective Towards Bright Future, ICATAM 2021
Y2 - 13 October 2021 through 14 October 2021
ER -