Article Per Year (5 Year)
p-Index From 2020 - 2025
1.288
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Voice Of Informatics Abdimasku : Jurnal Pengabdian Masyarakat Journal of Computing Theories and Applications Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
T. Sutojo
Program Studi Teknik Informatika, Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Multiscale Materials Informatics

 1 
Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning Herowati, Wise; Akrom, Muhamad; Hidayat, Novianto Nur; Sutojo, Totok
Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i1.10448

Abstract

Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.
Co-Authors Achmad Wahid Kurniawan Akrom, Muhamad Andreas Widiyantoro Astuti, Yani Parti Budi, Setyo De Rosal Ignatius Moses Setiadi Dutta, Pushan Kumar Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Fauzi Adi Rafrastara Hanny Haryanto Herowati, Wise Hidayat, Novianto Nur Ignatius Moses Setiadi, De Rosal Kusuma, Muh Galuh Surya Putra Minh T. Nguyen, Minh T. Moch Anjas Aprihartha Muljono Muljono Mulyanto, Edy Noor Ageng Setiyanto, Noor Ageng Prajanto Wahyu Adi, Prajanto Wahyu Rahmadianto, Rizky Setiawan, Marcell Adi Sudaryanto Sudaryanto Supriadi Rustad Suria Sandi Winarto Trisnapradika, Gustina Alfa Wisinggya, Kadhana Reya