Increased accuracy in the classification method of backpropagation neural network using principal component analysis

Abstract

Water and air in life are needed in every human and living creature on earth, especially with the status of water quality and air quality status that must be known to humans. Water and air quality status has 120 records with 8 attributes consisting of 4 classes and 1096 records with 5 attributes consisting of 6 classes. Water and air quality classification can affect performance in data grouping. So from that the author tries to increase accuracy in classification by using the Neural Network Backpropagation algorithm with PCA. In this study, it is expected that the Backpropagation Neural Network algorithm using PCA is able to increase accuracy in the classification method.