Classification is a technique used to build a classification model from a sample of training data. One of the most popular classification techniques is The K-Nearest Neighbor (KNN). The KNN algorithm has important parameter that affect the performance of the KNN Algorithm. The parameter is the value of the K and distance matrix. The distance between two points is determined by the calculation of the distance matrix before classification process by the KNN. The purpose of this study was to analyze and compare performance of the KNN using the distance function. The distance functions are Braycurtis Distance, Canberra Distance and Euclidean Distance based on an accuracy perspective. This study uses the Iris Dataset from the UCI Machine Learning Repository. The evaluation method used id 10-Fold Cross-Validation. The result showed that the Braycurtis distance method had better performance that Canberra Distance and Euclidean Distance methods at K=6, K=7, K=8 ad K=10 with accuracy values of 96 %.

K-Nearest Neighbor (KNN) has important parameters that affect the performance of the KNN. The parameter is the k value and distance matrix. In KNN, the distance between two points is determined by the calculation of the distance matrix. In this paper we will analyze and compare the performance KNN using the distance function. The distance are Braycurtis, Canberra and Euclidean Distance. This study uses Confusion Matrix for evaluation of accuracy, sensitivity and specificity. The results showed that the Braycurtis distance had better performance than Canberra Distance and Euclidean Distance with accuracy values of 96%, sensitivity of 96.8% and specificity of 98.2%.