SUMMARY
Liver disease is one of the diseases that is difficult to detect and becomes the largest contributor to deaths because it is considered a silent killer without symptoms. Liver disease can be detected based on abnormalities in the number of contents in the human body. The Indian Liver Patient Dataset (ILPD) dataset has many variables related to content in the body of liver patient data which are used as parameters in the classification of liver disease patients. Previous studies have shown that only two variables influence the ILPD dataset. The purpose of this study is to examine the use of the Principal Component Analysis (PCA) method to determine the optimal number of features in the context of classification of liver disease and examine the percentage distribution of data training and data testing which produces the best accuracy. The ILPD dataset was obtained from the UCI Machine Learning website with a total of 583 rows of data and 11 features. The percentage of training data and testing data used is 50%:50%, 60%:40%, 70%:30%, 73%:27%, 75%:25%, 80%:20%, 83%:17%, 85%:15% and 90%:10%. The use of different training and testing data percentages serves to find the best accuracy. The PCA result feature is used as input for the Decision Tree C4.5 classification algorithm. The experimental results show that using the training and testing data distribution percentage of 90%:10% and after the application of PCA produces the highest accuracy, namely 78.40% which is obtained for the number of PCA components n = 8.