<![CDATA[Agroecological heterogeneity poses challenges for agricultural planning in Bangkalan Regency, Indonesia. This study aimed to delineate agroecological zones by integrating soil fertility, climate, and topographic variables using K-Means clustering and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A total of 11,000 geospatial observations obtained from Google Earth Engine were aggregated into 277 village-level units. The dataset included soil nutrients (nitrogen, phosphorus, and potassium), the Soil Quality Index, temperature, rainfall, humidity, elevation, and slope. Data preparation, modeling, and evaluation were performed as the primary methodological steps. Min-Max Scaling was applied to normalize the data. The optimal K-Means configuration (K = 3) achieved a Silhouette Score of 0.2668, an Inertia value of 294.5529, and a Calinski-Harabasz Index (CHI) of 75.8821. The resulting clusters were classified as High-Potential (52 villages), Moderate-Potential (142 villages), and Environmental-Constraint (83 villages) zones. HDBSCAN was used to validate clustering patterns and detect environmental anomalies. The optimal HDBSCAN configuration identified two density-based clusters and five noise villages. These villages showed exceptionally high nitrogen, phosphorus, and Soil Quality Index values, indicating localized agroecological hotspots. The integration of K-Means and HDBSCAN offers a comprehensive framework for agricultural planning, resource allocation, and sustainable land management.]]>
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