Clustering and Mixture Model Analysis of Human Development Index in Papua: A Study Based on Educational Data (2010–2023)

Alvian Sroyer, Henderina Morin, Felix Reba, Jonathan Wororomi, Agustinus Languwuyo

Abstract


The purpose of this research is to analyze the distribution of the Human Development Index (HDI) in Papua based on the average length of schooling during the period 2010–2023 using the Gaussian Mixture Model (GMM) approach. Data from 27 districts are grouped into three clusters based on the distribution characteristics of each region. Weibull, Nakagami, and Generalized Extreme Value (GEV) distributions were selected to represent Cluster 1, Cluster 2, and Cluster 3, with parameter estimation using Maximum Likelihood Estimation (MLE). The results of the analysis show that Cluster 1 includes areas with low HDI such as Mamberamo Raya and Yahukimo, Cluster 2 reflects moderate HDI in areas such as Nduga and Tolikara, while Cluster 3 describes high HDI in districts such as Jayapura and Mimika. The mixture model that combines these three distributions provides an accurate representation of the HDI distribution pattern in Papua. Policy implications from these results include the development of cluster-based education programs to improve access to education in areas with low HDI, reduce educational disparities in areas with moderate HDI, and maintain sustainable development in areas with high HDI. This approach can be a reference for similar analyses in other regions with high development heterogeneity characteristics


Keywords


Gaussian Mixture Model;Goodness of Fit; HDI Papua; Mixture Model; Probability Distribution;

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DOI: https://doi.org/10.18860/cauchy.v10i2.32988

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