The increasing burden of infectious diseases such as Tuberculosis (TB) and non-communicable diseases such as Diabetes Mellitus (DM) places significant pressure on the National Health Insurance (JKN) financing system. This study aims to analyze the healthcare financing patterns of TB and DM using BPJS Kesehatan secondary data from 2022 through a clustering analysis approach. This quantitative study employs BPJS Kesehatan Sample Data, covering 34 provinces in Indonesia. Data processing was performed using Stata 14.2 for case proportion calculations and RStudio for cluster analysis with the K-Means algorithm. The optimal number of clusters was determined using the Gap Statistic and Within-Cluster Sum of Squares (WCSS) methods. The results indicate three regional clusters: a moderate-burden cluster (Sumatra), a high-burden cluster (Java), and a low-burden cluster (other provinces).
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