Diagnosis Penyakit Ginjal Kronis dengan Algoritma C4.5, K-Means dan BPSO

Abstract

Chronic kidney disease or Chronic Kidney Disease (CKD) is a disorder of the kidneys that results in the kidneys not being able to perform their functions properly due to decreased kidney performance. Classification is a data mining technique that can be used in diagnosing chronic kidney disease. In this study, the classification was carried out using the C4.5 algorithm. K-Means Clustering is used to discretize numeric type data. Binary Particle Swarm Optimization (BPSO) serves to select a subset of features that are redundant and less informative in the dataset or what is known as feature selection. The test was carried out using the 10-fold cross validation method on the Chronic Kidney Disease (CKD) dataset obtained from the UCI Machine Learning Repository. The test results in this study found that the application of feature selection with BPSO was able to increase the performance of the C4.5 classification with the values ??of accuracy, precision, recall and f-measure, respectively, namely 96%, 96.869%, 96.8% and 96.781% as well as computation time. which is obtained is 62.56 ms. While in BPSO parameter testing, the best parameter values ??obtained with the number of particles is 15, the number of iterations is 40, the value of c1 is 1 and c2 is 1.2 and the value of inertia weight (w) is 0.9.