<![CDATA[The dynamics of disease patterns in densely populated areas like East Java cannot be fully addressed with traditional data reporting alone. A more advanced data analysis technique is necessary. This study aims to develop a spatial Business Intelligence (BI) dashboard by integrating the K-Means algorithm with GeoJSON mapping with the goal of accurately identifying trends in the disease burden. Official epidemiological data for 38 districts and cities in East Java covering hypertension, diabetes, dengue fever, and tuberculosis (2022–2024) were used. We processed official epidemiological data first, then validated the model using three major metrics: the Calinski-Harabasz Index, the Silhouette Score, and the Davies-Bouldin Index Index. The study findings show that a 6-cluster arrangement is the most efficient model, with a Silhouette Score of 0.61 and a Davies-Bouldin Index of 0.43. These values demonstrate extremely high group cohesiveness. On the other hand, spatial analysis identified contrasting epidemiological patterns: the disease burden is disproportionately concentrated in metropolitan and industrial districts, whereas rural areas have a low burden. In fact, the findings of this study show that combining BI and geographical analysis can increase the efficiency of health resource allocation by identifying observable epidemiological trends. The developed architecture is also scalable and stable for use in regional smart health surveillance systems.]]>
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