<![CDATA[Today’s education sector is required to remain competitive by maximizing all available resources. High student success rates and low failure rates reflect the quality of education. However, determining student achievement levels—categorized as low, sufficient, or high—often becomes a challenge. To address this issue, data mining can be applied as a method for analyzing data and identifying patterns within large datasets. One important technique in data mining is clustering, which groups data into clusters based on similarity. Data within the same cluster have high similarity, while data between clusters have low similarity. A commonly used clustering method is the K-Means algorithm. K-Means is a non-hierarchical clustering technique that partitions data into one or more clusters based on shared characteristics, grouping similar objects together and separating those with different characteristics. In analyzing student achievement, the attributes used include student names and subject grades. The grouping process applies Euclidean Distance to measure similarity between data points. By implementing clustering with the K-Means algorithm, student achievement levels can be classified into low, sufficient, and high categories, thereby supporting more effective and targeted teaching and learning processes.]]>
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