Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa Sebagai Material Antikorosi

Abstract

Corrosion is an electrochemical process between the metal surface and a corrosive environment that can lead to significant losses in various industries, especially in the oil and gas sector. Experimental studies are conducted to evaluate the performance of corrosion inhibitors and available resources. In this research, a machine learning (ML) approach is employed to assess the effectiveness of expired drug compounds as corrosion inhibitors. The primary challenge in machine learning is obtaining a highly accurate model to ensure that predictions are relevant to the properties of the tested materials. Therefore, the polynomial function is tested in the gradient-boosting regressor (GBR) algorithm to enhance the accuracy of the developed ML model. The results indicate that the implementation of the polynomial function in the GBR algorithm can improve the accuracy of the prediction model based on R2 and RMSE metrics.

Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation

“…The exploration of corrosion inhibitors, encompassing both inorganic and organic constituents, is increasingly gaining prominence as it undergoes continuous investigation. The focus of corrosion inhibitor development is shifting towards the quest for organic inhibitors sourced from natural substances, primarily due to the costliness, hazardous nature, and adverse environmental impact associated with inorganic counterparts [25], [26], [27], [28]. Natural extracts have emerged as favored alternatives, heralded as environmentally friendly inhibitors or "green inhibitors," owing to their biodegradability, renewability, absence of toxic byproducts, cost-effectiveness, simplicity of synthesis, and notable anti-corrosion efficacy [29], [30].…”