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Abstrak

Korosi merupakan tantangan signifikan bagi daya tahan material, yang seringkali menyebabkan kerugian ekonomi yang besar. Penelitian ini memanfaatkan teknik Machine Learning (ML) untuk memprediksi efektivitas senyawa obat sebagai inhibitor korosi. Kami menggunakan lima algoritma ML yang menonjol: Regresi Linear, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, dan XGBoost. Model-model ini dilatih dan dievaluasi menggunakan dataset yang terdiri dari 14 fitur molekuler dengan efisiensi inhibisi korosi (IE%) sebagai variabel target. Hasil pelatihan model awal mengidentifikasi Random Forest dan XGBoost sebagai yang berkinerja terbaik berdasarkan metrik seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan R-squared (R²). Penyetelan hiperparameter lebih lanjut menggunakan GridSearchCV menunjukkan bahwa XGBoost, setelah penyetelan, secara signifikan mengungguli model lainnya, mencapai kesalahan terendah dan nilai R² tertinggi, menunjukkan akurasi prediktif yang superior untuk aplikasi ini. Temuan ini menegaskan potensi ML, khususnya XGBoost, dalam meningkatkan pemodelan prediktif inhibitor korosi, sehingga memberikan wawasan berharga bagi bidang ilmu korosi.

Kata Kunci

Machine Learning Inhibitor Korosi Senyawa Obat Model Prediksi Hyperparameter Tuning

Rincian Artikel

Biografi Penulis

Muhamad Akrom, Universitas Dian Nuswantoro

Research Center for Materials Informatics

Cara Mengutip
[1]
M. R. Rosyid, L. Mawaddah, dan M. Akrom, “Investigasi Model Machine Learning Regresi Pada Senyawa Obat Sebagai Inhibitor Korosi”, Jurnal Algoritma, vol. 21, no. 1, hlm. 332–342, Jul 2024.
Unduh Sitasi
Endnote/Zotero/Mendeley (RIS)

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