Transformative Insights into Corrosion Inhibition: A Machine Learning Journey from Prediction to Web-Based Application

Transformative Insights into Corrosion Inhibition: A Machine Learning Journey from Prediction to Web-Based Application

corrosion, inhibitor, machine learning, web, streamlit

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.

Dzaki Asari Surya Putra, Nicholaus Verdhy Putranto, Nibras Bahy Ardyansyah, Gustina Alfa Trisnapradika, Muhamad Akrom

http://dx.doi.org/10.21609/jiki.v18i1.1303

Faculty of Computer Science Universitas Indonesia

2024-06-04

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

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Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)