<![CDATA[This study aims to cluster students of the Mathematics Study Program at Mulawarman University based on their academic achievements using the K-Means method. By grouping students into several clusters based on similarities in academic scores, it is hoped that this approach can assist in more objectively screening high-achieving students. The data used consists of course grades from the 2023 cohort over two semesters, employing an exploratory quantitative approach and data mining methods. The analysis process is conducted using Google Colab and the Elbow Method to determine the optimal number of clusters, resulting in three clusters: students with high, medium, and low scores. The findings indicate that the K-Means method is effective in clustering academic achievement data and assists the Mathematics Study Program in screening high-achieving students. The visualization of the clustering results shows the distribution of students in three main groups with centroids representing the average of these groups, thereby enabling a more systematic and measurable process for identifying and developing student potential.]]>
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