EFFECTIVENESS OF K-MEANS CLUSTERING TO DISTRIBUTE TRAINING DATA AND TESTING DATA ON K-NEAREST NEIGHBOR CLASSIFICATION

One of the constraints in classification is how to divide the dataset into two parts, training and testing which can represent every data distribution. The most commonly used technique is K-Fold Cross Validation which divides data into several parts and alternately into training data and testing data. In addition, the commonly used technique is to divide data into percentage form (70% and 30%), also become an option in data mining research. K-Means is a grouping algorithm which able to maximizes the effectiveness of distributing data in classification. The experiments performed using K-Means Clustering against K-Nearest Neighbor (K-NN) which was validated by Confusion Matrix have the highest accuracy of 93.4%, it is higher than the K-Fold Cross Validation data distribution technique for each experiment using data Education Management Information System (EMIS) as well as random data. The concept of distributing data in groups can be a representative to each member and increase the accuracy of classification algorithm, although the experiment only applied 70% of training data and 30% of testing data in each group.

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