Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium


  • Desmarita Leni Universitas Muhammadiyah Sumatera Barat



Model, Machine Learning, Aluminum, Tensile Strength, Algorithm


This study designs and compares optimal machine learning models to predict the mechanical properties of aluminum, including Yield Strength (YS) and Tensile Strength (TS), based on the percentage composition of aluminum's chemical elements. The machine learning modeling in this study has nine input variables consisting of aluminum chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and two output or target variables consisting of YS and TS. Additionally, Heatmap correlation is used to observe the correlation between chemical elements and the mechanical properties of aluminum. Three machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), are compared in this study. The comparison of these algorithms shows that Random Forest (RF) outperforms the other algorithms in predicting YS with MAE of 11.44, RMSE of 14.282, and R value of 0.93. On the other hand, ANN performs better in predicting TS with MAE of 19.593, RMSE of 22.005, and R value of 0.947.


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How to Cite

Leni, D. (2023). Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium. Jurnal Engine: Energi, Manufaktur, Dan Material, 7(1), 35–44.




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