K-Nearest Neighbor with K-Fold Cross Validation and Analytic Hierarchy Process on Data Classification

Abstract

This study analyzes the performance of the k-Nearest Neighbor method with the k-Fold Cross Validation algorithm as an evaluation model and the Analytic Hierarchy Process method as feature selection for the data classification process in order to obtain the best level of accuracy and machine learning model. The best test results are in fold-3, which is getting an accuracy rate of 95%. Evaluation of the k-Nearest Neighbor model with k-Fold Cross Validation can get a good machine learning model and the Analytic Hierarchy Process as a feature selection also gets optimal results and can reduce the performance of the k-Nearest Neighbor method because it only uses features that have been selected based on the level of importance for decision making.