Penerapan Particle Swarm Optimization Untuk Meningkatkan Kinerja Algoritma K-Nearest Neighbor Dalam Klasifikasi Penyakit Diabetes

Abstract

The study aims to apply the KNN algorithm to classify diabetes by combining the PSO algorithm as a selection so as to obtain the best accuracy from the KNN algorithm in classifying, so that it can be applied to diagnose diabetes. This research consists of several stages of research including dataset collection, data pre-processing, data sharing, finding the optimal k-value to the classification process and performance or accuracy testing. From this research, the accuracy of the KNN algorithm before feature selection using PSO is 75% at k-optimal 19 and after feature selection using PSO is obtained an increase in accuracy to 77.213% at the same k value with features that affect the pima dataset, namely Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, and Age. Thus the use of KNN with PSO feature selection can be used to identify diabetes because it has a much better level of accuracy.