Characteristics of Provinces in Indonesia Based on JKN Indicator Outcomes by Gaussian Mixture Model with Expectation-Maximization Algorithm and Biplot

Abstract

Indonesia, an archipelago with a population of 257.77 million in 2022, faces significant challenges in enhancing the quality of life to improve human resource productivity. This study aims to identify provincial characteristics in Indonesia based on the outcomes of the Jaminan Kesehatan Nasional (JKN) program from 2019 to 2021. Using a Gaussian Mixture Model (GMM) with the Expectation Maximization (EM) algorithm, we cluster 34 provinces based on 14 health indicators. The data were obtained from the BPJS website and included variables such as access to health services, program effectiveness, and service quality. Our methodology allows for clustering provinces with similar health outcomes and analyzing the unique indicators for each cluster using biplot analysis.The results indicate significant variation in cluster membership across the years. In 2019, three clusters were identified, with cluster sizes of 16, 12, and 6 provinces. In 2020, the optimum model also had three clusters, but with different member distributions: 24, 7, and 3 provinces. By 2021, four clusters emerged with sizes of 9, 16, 3, and 6 provinces. These findings highlight the dynamic nature of health outcomes across Indonesia's provinces and suggest the need for tailored policy interventions to improve the JKN program's effectiveness.The study's limitations include the reliance on available BPJS data and the assumption that the selected health indicators comprehensively represent the JKN program's impact. This research's novelty lies in its use of advanced clustering techniques to provide a nuanced understanding of regional health disparities in Indonesia, which can inform more targeted and effective health policies.