Abstract
Classification is the process of differentiating a set of models into several data classes. There are many methods that can be used for the classification process, one of which is the Artificial Neural Network method. Neural networks are a computational method that mimics biological syafar networks. Artificial condition networks can be used to model complex relationships between input and output to recognize patterns in data [1]. In this study, testing was conducted to determine the effect of uncorrelated or low-correlation features in the data classification process and the effect of changing the number of units in the hidden layer on the classification results. The data used in this study were liver disease dataobtained from the Kaggle Dataset.Where in comparing the results of using feature selection, it is divided into 4 predetermined scenarios through the search for significance values ??with the SPSS correlation test.In the results of the implementation of the Multilayer Perceptron which aims to determine the effect of feature selection on the classification results, the results are that feature selection does not really affect the computation time obtained, and correlated data has more influence on the accuracy obtained when compared to uncorrelated data. In the results of the implementation of the Multilayer Perceptron which aims to determine the effect of changing the number of hidden layer units on the classification results, the results show that changes in the number of units in the hidden layer in Artificial Neural Networks have increased significantly in accuracy in several scenarios, but the computation time increases if the number of units in the hidden layer increases.
Keywords: Classification, Artificial Neural Network, Liver Disease, Accuracy, Time.
Flower Pollination Algorithm and Multilayer Perceptron Artificial Neural Network for Heart Disease Feature Selection and Classification
