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

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