<![CDATA[The selection was an attempt College to get qualified prospective students. Test data for new students able to describe the quality of academic and connect to graduate on time. Recognizing the academic quality of students is required in the implementation of the lecture to obtain optimal results. Real conditions today, timely graduation has not achieved optimally, need to be improved to reach the limits of reasonableness. Data that has no need to do a classification based on academic quality, in order to obtain predictions timely graduation. Therefore, proposed an effort to resolve the problem by applying the K-Nearest Neighbor algorithm to re-clustering the test result data for new students. The procedure is to determine the amount of data clusters, determining the center point of the cluster, calculate the distance of the object with the centroid, classifying objects. If the new data group calculation results together with the results of calculation of new data group then finished its calculations. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results.]]>
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