<![CDATA[Livestock farming serves as a vital economic pillar for the community in Bandar District, Simalungun Regency. However, the high intensity of livestock activities is accompanied by a significant risk of disease transmission, which has historically been managed through conventional recording methods that lack spatial integration. This research aims to analyze the spatial distribution patterns of livestock diseases by implementing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method integrated into a web-based Geographic Information System (GIS). Using a quantitative approach, the study processed 200 case records from December 2025 to January 2026. Spatial distances were calculated using the Haversine formula to ensure geographic accuracy. The results indicate that the optimal parameters for the DBSCAN algorithm are an epsilon ($\epsilon$) of 3.0 km and a minimum points (MinPts) of 2. These parameters successfully identified two primary clusters with zero noise, encompassing all 200 cases. Cluster 1 (98 cases) is concentrated in the west-central region, dominated by cattle and goats with diverse pathologies such as Scabies and BEF. Cluster 2 (102 cases) is located in the east-northern region and exhibits a more heterogeneous livestock profile, including rabies cases in dogs. High-density areas requiring priority intervention were identified in Pematang Kerasaan Rejo and Perdagangan II. The developed web-based GIS provides an interactive visualization platform that enhances early warning capabilities and supports data-driven decision-making for livestock disease surveillance and regional control.]]>
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