Perbandingan Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole

Cornellius Adryan Putra Sumarjono; Muhamad Akrom; Gustina Alfa Trisnapradika

doi:10.33633/tc.v22i4.9201

Authors

Cornellius Adryan Putra Sumarjono Universitas Dian Nuswantoro
Muhamad Akrom Universitas Dian Nuswantoro
Gustina Alfa Trisnapradika Universitas Dian Nuswantoro

DOI:

https://doi.org/10.33633/tc.v22i4.9201

Keywords:

Machine learning, korosi, cross-validation, benzimidazole, adaboost regressor

Abstract

Penelitian ini merupakan studi eksperimen untuk melakukan penyelidikan inhibitor korosi oleh senyawa Benzimidazole dengan melakukan pendekatan machine learning (ML). Karena korosi menyebabkan banyak kerugian yang timbul karena kehilangan material konstruksi, keselamatan kerja dan pencemaran lingkungan akibat produk korosi dalam bentuk senyawa yang mencemarkan lingkungan. Melakukan pendekatan ML adalah untuk mendapatkan model akurasi yang terbaik sehingga dapat digunakan untuk memprediksi dengan relevan dan akurat terhadap suatu material. Dalam penelitian ini, kami mengevaluasi algoritma ML dengan metode linear dan nonlinear dengan menggunakan metode k-fold cross-validation untuk membantu dalam mengukur performa model ML. Mengacu pada metrik coefficient of determination (R2) dan root mean square error (RMSE), kami menyimpulkan bahwa model AdaBoost regressor (ADA) merupakan model dengan performa prediksi terbaik dari eksperimen yang kami lakukan dari literatur untuk dataset senyawa benzimidazole. Keberhasilan model penelitian ini menawarkan perspektif baru tentang kemampuan model ML untuk memprediksi penghambat korosi yang efektif.

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Perbandingan Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole

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