Analisis Perbandingan Metode Hirearchical, K-Means, dan K-Medoids Clustering dalam Pengelompokan Kasus Penyakit Menular di Bengkulu Tengah - Tasti, Desi T.; Gumay, Fridz M.; Aysha, Ulfianida; Agwil, Winalia; Pratami, Wingke Y. (Diophantine Journal of Mathematics and Its Applications, 2025)
In Indonesia, infectious diseases are still a persistent problem. Experts have accumulated knowledge regarding the emergence of the disease. In the last ten years, Indonesia is still experiencing the problem of triple burden diseases. Where Indonesia is still hit by infectious diseases, non-communicable diseases (NCDs) and diseases that should have been resolved, apart from that, infectious diseases are also still a big problem that must be faced. Researchers are interested in conducting research on infectious diseases in Central Bengkulu Regency. When analyzing infectious diseases, grouping can be done. Cluster analysis is an approach to looking for similarities in data and placing similar data into groups. There are two grouping methods in cluster analysis, hierarchical methods and non-hierarchical methods. One of the cluster analyzes using hierarchical methods is the average linkage method, while non-hierarchical ones are K-Means and K-Medoids. The variables used in this research are TBC and DHF in 2022. The highest rates of TB and DHF occurred in Pondok Kelapa sub-district, namely 29 and 23 cases. Based on the results of the analysis, it consists of 2 clusters, with cluster 1 consisting of 9 sub-districts, while cluster 2 consists of 2 sub-districts. Based on the results of evaluating the best method using the Calinski-Harabasz Index, it was found that the K-medoids method was the best method with a value of 0.