<![CDATA[The new student admission system that uses the K-Means algorithm data grouping is the simplest clustering pattern compared to other algorithms. This algorithm is one of the data mining. K-Means groups them into several clusters that have similarities and separates each cluster based on the differences between each cluster.The research of the K-Means Clustering algorithm aims to minimize the functions set during the Clustering process.The implementation of the K-Means Clustering algorithm into the clustering information system provides the results of an effective data grouping classification and the process of each literacy rotation of the Centroid distance, the determination of the Cluster point is formed, student data as a reference object saves more time on clustering the superior class. The application of this web-based clustering information system results in more flexible information that can be accessed at any time by users who are given access rights to utilize the data. The application of the K-Means Clustering Algorithm to get the results of the Superior Class clarification requires an information system implementation to form 3 clusters for each class, namely M1, M2 and M3. M1 as a high score with a criterion value of 85 to 100, M2 as a medium value with a criterion value of 75 to 80 and M3 as a low value with a criterion value of 10 to 70.]]>
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