<![CDATA[Student attendance at school is a key factor in determining the quality of education and the effectiveness of the learning process. Therefore, student attendance data can be one of the indicators for schools in managing and improving the quality of education. The problem is that the analysis process has not been carried out to group potential student activeness based on similar characteristics and the school still has difficulty in processing large data so that the quality of education is not optimal. This research involves student attendance data from SMP Negeri 3 Rancaekek for one academic year as the main dataset. The research method includes the stages of data collection, pre-processing, and analysis. The collected student attendance data was processed to remove outliers and create a dataset suitable for Clustering analysis. The K-Means Clustering method is used to group students into groups based on their attendance patterns. K-Means means an iterative clustering solution procedure that performs partitioning to classify or group a large number of objects. K-Means as a popular data mining method, is a solution procedure that is often used to identify natural groups in a case. This method focuses on grouping data that has similarities, so that the results can be analysed in more depth. The research results show that.]]>
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