Penerapan Artificial Neural Network Terhadap Identifikasi Wajah Menggunakan Metode Backpropagation

Abstract

Backpropagation is one of the supervised training methods that causes an error in the output produced. Backpropagation neural networks will be carried out in 3 stages, namely feedforward from input training patterns, backpropagation from errors related to adjustment of weights. Updating the weight is done when the training results obtained have not been converged. The value of the goal error (MSE) is 0.0070579 which is achieved at epochs to 99994 from the provisions of 100000 iterations. Based on the plot regression, the training data resulted in a correlation coefficient value of up to 0.55321. The correlation coefficient value is concluded that the greater the R value produced, the better the level of accuracy in face identification carried out in this study