ANALISIS VARIATION K-FOLD CROSS VALIDATION ON CLASSIFICATION DATA METHOD K-NEAREST NEIGHBOR

Abstract

To produce a data classification that has data accuracy or similarity in proximity of a measurement result to the actual numbers or data, testing can be done based on accuracy with test data parameters and training data determined by Cross Validation. Therefore data accuracy is very influential on the final result of data classification because when data accuracy is inaccurate it will affect the percentage of test data grouping and training data. Whereas in the K-Nearest Neighbor method there is no division of training data and test data. For this reason, researchers analyzed the determination of training data and test data using the Cross validation algorithm and K-Nearest Neighbor in data classification. The results of the study are based on the results of the evaluation of the Cross Validation algorithm on the effect of the number of K in the K-nearest Neighbor classification of data. The author tests using variations in the value of K K-Nearest Neighbor 3,4,5,6,7,8,9. While the training and test data distribution using Cross validation uses variations in the number of K-Fold 1,2,3,4,5,6,7,8,9,10