Data mining has many functionalities. One of the main functions of data mining is the classification that is used to predict the class and generate information based on historical data. In the classification, there is a lot of algorithms that can be used to process the input into the desired output, thus it is very important to observe the performance of each algorithm. This paper is to analyze and compare the performance i.e. accuracy of decision tree (C4.5) and k-Nearest Neighbor (k-NN) algorithms. The evaluation method used is 10-fold cross validation.