<![CDATA[Abstract-Corrosion is a serious problem in various industries that leads to increased production costs, maintenance, and decreased equipment efficiency. The use of organic compounds as corrosion inhibitors has become an increasingly desirable solution due to their effectiveness and environmental friendliness. This study compares the performance of two machine learning algorithms, Multilinear Regression (MLR) and AdaBoost Regression (ABR), in predicting the corrosion inhibition efficiency (CIE) of pyridazine-derived compounds. The dataset used consists of molecular properties as independent variables and CIE values as targets. To measure the performance of the model, a k-fold cross-validation process was used, where the dataset was divided into equal subsets. Each iteration uses one subset as validation data, while the other subset as training data. Results show that the AdaBoost Regression model achieves higher accuracy (99%) than Multilinear Regression (98%) in predicting CIE. Important feature analysis showed that Total Energy (TE) and Dipole Moment (ยต) were the most influential variables in the ABR model, highlighting their important role in inhibitor effectiveness. Model evaluation was performed with R2 and RMSE metrics, where nonlinear models such as ABR were shown to be superior in predicting corrosion inhibition efficiency. These findings support the use of nonlinear methods to improve the effectiveness of protecting industrial equipment from corrosion.]]>
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