Meningkatkan Kinerja Backpropagation Neural Network Menggunakan Algoritma Adaptif

Abstract

The application of Artificial Neural Networks in various fields of human life is getting wider, especially in the industrial sector. One of the artificial neural network structures that are quite often used is the Feedforward Neural Network with its well-known learning algorithm, namely Backpropagation. However, as reported by several researchers, Backpropagation has several weaknesses such as it takes a long time to converge in the training process, it is quite sensitive to initial weight conditions and is relatively often trapped in a local minima which can thwart the training process. In this study, the Adaptive algorithm is proposed as an alternative to the Backpropagation learning algorithm. The proposed algorithm provides hope in overcoming the weaknesses faced by Bakpropagation. As reported in the test results, compared to Backpropagation, the Adaptive algorithm is much stronger in dealing with variations in the initial weight conditions. From 100 tests in this study for each Backpropagation and Adaptive algorithm, with random variations for the initial weight value, the success rate of the Adaptive algorithm training process reaches 100% compared to Backpropagation which is at the level of 77%. In terms of speed, the Adaptive algorithm has successfully carried out the training process with an average number of iterations of 37 times compared to Backpropagation which requires an average of 162 iterations.