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All Journal Jurnal Ilmu Komputer dan Informasi
Nibras Bahy Ardyansyah
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Computer Science & IT Library & Information Science

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Transformative Insights into Corrosion Inhibition: A Machine Learning Journey from Prediction to Web-Based Application Dzaki Asari Surya Putra; Nicholaus Verdhy Putranto; Nibras Bahy Ardyansyah; Gustina Alfa Trisnapradika; Muhamad Akrom
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i1.1303

Abstract

This study focuses on the exploration and evaluation of machine learning (ML) models to analyze expired pharmaceutical data for their potential use as corrosion inhibitors. Additionally, the entire modeling process is integrated into a user-friendly platform through a Streamlit service-assisted corrosion inhibitor website, facilitating broader accessibility and practical application. The models are trained offline to ensure accurate performance, eliminating the need for users to retrain the models themselves. This approach simplifies the user experience by offering a ready-to-use prediction service directly on the website platform. Among the various ML models implemented, XGB demonstrated the highest performance with an R2-score of 0.99999999. Given that many chemists are not familiar with informatics coding, the researchers developed a Streamlit-based website that includes tools to customize the models. The end product of this work is a corrosion inhibitor experimentation tool that eliminates the need for users to code, making advanced ML techniques accessible to a broader audience within the chemistry community.
Co-Authors Dzaki Asari Surya Putra Gustina Alfa Trisnapradika Muhamad Akrom Nicholaus Verdhy Putranto