<![CDATA[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.]]>
