<![CDATA[Bank Sampah Perumahan Sapta Pesona RW 08 Bekasi City has waste weighing data that is still limited to administrative records and has not been optimally used to identify waste characteristic patterns. This study aims to Cluster waste data based on quantity/tonnage and unit price using the K-Means algorithm. The research method used Cross Industry Standard Process for Data Mining (CRISP-DM). The dataset consisted of 810 waste weighing records from January 2024 to December 2025 with item type, quantity/tonnage, and unit price attributes. The number of Clusters was determined using the Elbow Method based on the Sum of Squared Error (SSE), while the Clustering results were evaluated using the Silhouette Coefficient. The results show that the data were grouped into three Clusters with a Silhouette Coefficient value of 0.7844, indicating a strong Cluster structure. Cluster 0 represents waste with low to moderate tonnage and unit price, Cluster 1 represents waste with low tonnage and high unit price, while Cluster 2 represents waste with high tonnage and low to moderate unit price. These results help managers understand waste data characteristics more systematically]]>
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