Analysis of the Resilient Method in Training and Accuracy in the Backpropagation Method

Abstract

Artificial Neural Network (ANN) is one of the clusters of computer science that leads to artificial intelligence, there are several methods in ANN, one of which is the backpropagation method. This method is used in the prediction process. In some cases the backpropagation method can help in problems solving, especially predictions. However, the backpropagation method has weaknesses. The results of the backpropagation method are very influenced by the determination of the parameters so that the convergence becomes very slow. So needed an optimization method to optimize the performance of the bakpropagation method. The resilient backpropgation method is one solution, this method can change the weight and bias of the network with a direct adaptation process of weighting based on local gradient information from learning iterations so that it can provide optimal results. The data used is the Higher Education Gross Enrollment Rate in Indonesia from 2015-2020 by province. The results were obtained from several data testing with architectural experiments 3-5-1, 3-20-1, 3-37-1, 3-19-1, 3-26-4 and 3-4-1 from backpropagation and resilient testing, shows that the data training process can be optimized significantly, but the accuracy is not evenly optimal