Investigasi Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole

Akrom, Muhamad
Sumarjono, Cornellius Adryan Putra
Trisnapradika, Gustina Alfa

KOMPUTIKA - Jurnal Sistem Komputer
Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer

This research aims to investigate the corrosion inhibition performance of Benzimidazole compounds using a machine learning (ML) approach. The main challenge in developing ML is to obtain a model with high accuracy so that the prediction results are relevant and accurate to the actual properties of a material. In this research, we evaluate various linear and non-linear algorithms to obtain the best model. Based on the coefficient of determination (R2) and root mean square error (RMSE) metrics, it was found that the AdaBoost Regressor (ADA) model was the model with the best predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds. This approach offers a new perspective on the ability of ML models to predict effective corrosion inhibitors.