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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Adimas : Jurnal Pengabdian Kepada Masyarakat International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Journal of Computing Theories and Applications Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Herowati, Wise
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds Fadil, Muhamad; Akrom, Muhamad; Herowati, Wise
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8894

Abstract

Corrosion is a significant issue in both industrial and academic sectors, with widespread negative impacts on various aspects, including economics and safety. To address this problem, the use of corrosion inhibitors has proven effective. This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. The XGBR optimized with hyperparameter tuning using Grid Search achieved the highest R² value of 0.970 and showed the lowest RMSE, MSE, MAD, and MAPE values of 0.368, 0.135, 0.119, and 0.273, respectively, indicating high predictive accuracy. These results are expected to contribute to the development of more effective methods for identifying corrosion inhibitor candidates.
Co-Authors Achmad Wahid Kurniawan Akrom, Muhamad Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Dutta, Pushan Kumar Hanny Haryanto Harisa, Ardiawan Bagus Harun Al Azies Hidayat, Novianto Nur Ignatius Moses Setiadi, De Rosal Indra Gamayanto Islam, Hussain Md Mehedul Kusuma, Muh Galuh Surya Putra Minh T. Nguyen, Minh T. Moch Anjas Aprihartha Muhamad Fadil Muljono Muljono Prajanto Wahyu Adi, Prajanto Wahyu Safitri, Aprilyani Nur Sasono Wibowo Sendi Novianto Supriadi Rustad T. Sutojo Trisnapradika, Gustina Alfa Zami, Farrikh Al