<![CDATA[Evidence management at the Bengkulu District Attorney's Office was previously handled manually, which complicated storage and retrieval as caseloads increased. This study implements the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to group 150 evidence records based on evidence type, quantity, date received, and storage location. A quantitative-experimental design was used, covering data pre-processing, encoding, normalization, and clustering with Python on Google Colab, using epsilon = 0.8 and minimum points = 3. DBSCAN produced four clusters and identified eight records as noise, achieving a Silhouette Score of 0.4757, indicating moderate clustering quality. The clusters were respectively dominated by electronics, vehicles, sharp weapons, and documents, each linked to a separate storage warehouse. These findings show that DBSCAN can support more structured and efficient evidence storage and retrieval governance within the prosecutor's office.]]>
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