<![CDATA[K-Means is a non-hierarchical data clustering method that can group data into several clusters based on data similarity, so that data with the same characteristics are grouped in one cluster and data with different characteristics are grouped in another cluster. The K-Means method can be used to process various data, including for clustering in the field of education. The use of the K-Means algorithm has been widely carried out but not many activities have been handled and are only often used for selection or acceptance activities and the use of attributes that must be reproduced to get optimal results. In this study, we will review various papers that perform clustering using the K-Means method for research in the field of education. Based on our research, papers related to the use of the K-Means algorithm for clustering in education have proved feasible and useful for future research. So it can be concluded that the K-Means method has been tested to be used for clustering in the field of education and the K-Means method can be useful in many aspects of education, both having an impact on educators, students and other educational aspects.]]>
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