<![CDATA[Identifying high-achieving students is a critical step in evaluating learning outcomes to enhance the quality of education. This study aims to analyze the clustering of student grade data using the K-Means Clustering method to identify groups of high-achieving students. The K-Means method is utilized due to its effectiveness in grouping data based on value similarity. The data used in this study consist of students' academic scores across various subjects. The research stages include data collection, preprocessing, applying the K-Means algorithm, and validating the clustering results. The results show that the K-Means method successfully grouped students into several categories, such as high-achieving, moderate-achieving, and low-achieving students. The clustering analysis indicates that high-achieving students exhibit consistent performance across all subjects, whereas low-achieving students tend to show significant variations in their scores. This method also provides data visualization that helps schools make informed decisions to improve student performance. Thus, the implementation of the K-Means method in clustering student grade data can serve as an effective and efficient approach to support evaluation processes and data-driven decision-making.]]>
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