<![CDATA[The process of evaluating student academic achievement results is needed as an effort to monitor student academic development. Grouping students according to the results of academic achievement is needed. One of the uses is as a mapping process for students who can be expected to graduate on time and students whose grades are still low so that assistance is needed. The purpose of this grouping can be used as a student achievement index mapping. The research conducted aims to provide an alternative grouping of academic achievement based on the data mining process using a clustering model. The research method uses the Knowledge Discovery in Database method. The data mining method with Knowledge Discovery in Database consists of selection, pre-processing, transformation, model and evaluation. The datasheet used is a collection of student achievement index data processed from the acquisition of student achievement index in semester 1 to 6. The clustering model is built using the K-Means algorithm. The results of the study resulted in the best grouping is grouping as much as 2, The process of getting the best grouping is done by testing the model with 6 groupings. The best grouping results are done with Davies Bouldin testing. The conclusion of the study with the results of the 2 groups can be given a category, cluster 0 with the name of the good category and cluster 1 with the category not good.]]>
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