Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN)

  • Desmarita Leni Teknik Mesin, Fakultas Teknik, Universitas Muhammadiyah Sumatera Barat
  • Helga Yermadona Teknik Sipil, Fakultas Teknik, Universitas Muhammadiyah Sumatera Barat
  • Ade Usra Berli Teknik Sipil, Fakultas Teknik, Universitas Muhammadiyah Sumatera Barat
  • Ruzita Sumiati Teknik Mesin, Politeknik Negeri Padang
  • Haris Haris Teknik Mesin, Politeknik Negeri Padang
Keywords: Algoritma, Artificial Neural Network (ANN), Aluminium

Abstract

This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.

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Published
2023-06-26
How to Cite
Leni, D., Yermadona, H., Usra Berli , A., Sumiati, R., & Haris, H. (2023). Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN). Jurnal Surya Teknika, 10(1), 625-632. https://doi.org/10.37859/jst.v10i1.4843
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