Optimization of Random Forest Prediction for Industrial Energy Consumption Using Genetic Algorithms

Sartini Sartini (Nusa Mandiri University)
Luthfia Rohimah (Bina Sarana Informatika University)
Yana Iqbal Maulana (Bina Sarana Informatika University)
Supriatin Supriatin (Bina Sarana Informatika University)
Dewi Yuliandari (Bina Sarana Informatika University)

PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic
Vol 11 No 1 (2023): March 2023

Abstract Saving electrical energy consumption in industries is crucial; hence, the prediction of industrial energy consumption needs to be performed. The random forest method can be applied to steel industry data to predict energy consumption. The purpose of this prediction is to increase energy savings in industries and optimize the performance of the random forest method. The results of the random forest show that the algorithm can predict energy consumption in industries effectively; however, it needs further optimization to achieve better predictions. Therefore, the genetic algorithm method will be used to optimize the previous method. The optimization results indicate that it is successfully conducted in terms of accuracy and kappa level. This optimization is beneficial to society, especially industrial companies.