<![CDATA[The rapid advancement of data science has made data processing a critical requirement across various fields, including education. Educational institutions are increasingly required to leverage available resources and information systems to enhance competitiveness and support strategic decision-making. Student achievement is generally assessed through both theoretical and practical subjects; however, determining achievement groups (very good, good, sufficient) often lacks efficiency, limiting early identification by homeroom teachers. This study applies the K-Means Clustering method to classify students' achievement levels at SD N Sanggang 03. The objective is to develop a system that assists teachers in grouping students based on performance categories—very good, good, and sufficient—thereby supporting data-driven decision-making in academic evaluation. The system was implemented using PHP and MySQL, with evaluation employing the Modified Partition Coefficient to measure clustering quality.]]>
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