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In Indonesia, Dengue Fever (DF) is a contagious disease that is a significant issue in public health. The Kediri Regency in East Java, as reported by the Ministry of Health in 2019, had the highest number of DF cases. If not addressed promptly, DF can lead to outbreaks, creating a health emergency. The lack of a thorough investigation into the diversity of risk within a spatial and temporal region exacerbates this issue. Therefore, spatial-temporal analysis is crucial in developing a warning system to prevent and control DF. This paper proposes a method that combines the Euclidean Distance calculation with the Hierarchical Clustering method. We collected data from the Kediri Regency health department and conducted pre-processing and classification processes, considering the number of DF victims, death rate, population, rainfall, and public facilities. The hierarchical clustering algorithm was used to categorize the 344 village analyses into low, medium, and high vulnerability categories. This method allows for a comparison of yearly single, average, complete, and centroid linkage in DF vulnerability levels. We also employed spatial-temporal visualization based on cellular applications to create a clear picture of areas vulnerable to DF. The experimental results in clustering showed a satisfactory level of matching, with variant values calculated using the hierarchical clustering method. The variants for single linkages were 0.113; for average linkages, they were 0.120; for complete linkages, they were 0.178; and for centroid linkages, they were 0.106. The grouping validation results indicated that the centroid linkage method produced the best variant level. We suggest further enhancing the methods with better process steps using other pre-processing methods to improve the validation quality.
Hamida et al. (Mon,) studied this question.