Model Analisis Crime Hotspot Dengan Algoritma Dbscan Berbasis Metodologi Crisp-Dm Untuk Optimalisasi Penempatan Patroli (Studi Kasus: Satreskrim Polres Metro Depok)
Keywords:
Crime Hotspot, CRISP-DM, DBSCAN, Klasterisasi Spasial, Prioritas PatroliAbstract
This study aims to develop a crime hotspot analysis model using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm within the Cross-Industry Standard Process for Data Mining (CRI SP-DM) methodology to support the formulation of relative patrol area and time priorities at the Criminal Investigation Unit of Depok Metropolitan Police. The dataset contained 662 crime incidents from June 2024 to June 2025. After period verification, 655 incidents were used for descriptive and temporal analyses, while 160 records with G1–G3 location quality were used as the main spatial dataset. The research stages included business understanding, data understanding, data preparation, modeling, evaluation, and deployment. DBSCAN modeling with an epsilon of 1,500 meters and min_samples of 6 produced six main clusters, comprising 121 hotspot records and 39 noise records, with a Silhouette Coefficient of 0.4539. Cluster C1, the largest cluster with 74 incidents, was further divided using K-Means into operational subzones C1-A and C1-B without changing the main DBSCAN results. Sensitivity analysis produced an Adjusted Rand Index of 0.8186 and 90% status consistency. Grid-based temporal validation achieved 100% recall, 11.76% precision, and 77.27% accuracy, indicating that the model retained all positive areas in the testing period while still producing several false positives. The outputs include a crime hotspot map, seven operational zones, and information on relative area, time, and dominant crime-type priorities. The model is considered feasible to retain with limitations and should be used as decision-support information for patrol evaluation rather than as an automatic operational decision tool.
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