A Comparison of Accuracy between Decision Tree and k-NN Algorithm

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. The purpose of this research 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. Evaluation result is a confusion matrix for measuring accuracy in precision, recall, F-measure, and success rate. Based on the comparative analysis, the decision tree algorithm gains the accuracy better by variation of 2.28%-2.5% compared to k-NN algorithm in the implementation for 5 research data sets. Keywords-Classification; Decision Tree; k-NN; 10-fold Cross Validation; Confusion Matrix; Accuracy.