Open AccessIncreased accuracy in the classification method of backpropagation neural network using principal component analysis
Author(s) -
Kristin Lorensi Sitompul,
Muhammad Zarlis,
Poltak Sihombing
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/725/1/012124
Subject(s) - backpropagation , artificial neural network , principal component analysis , artificial intelligence , computer science , pattern recognition (psychology) , data mining , quality (philosophy) , machine learning , philosophy , epistemology
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.