Optimizing Gaussian Mixture Model Using Principal Component Analysis for Welfare Clustering - Rafif Ilafi Wahyu Gunawan; Muhammad Muharrom Al Haromainy; Achmad Junaidi (bit-Tech, 2026)
Welfare inequality among regions remains a fundamental challenge in achieving balanced development across East Java Province. The complexity of social, economic, and development indicators often obscures the true patterns of regional welfare. To address this issue, this study proposes a more efficient analytical approach by integrating Principal Component Analysis (PCA) and the Gaussian Mixture Model (GMM) to cluster regions based on welfare levels. The dataset, obtained from the Central Bureau of Statistics (BPS) of East Java for the 2010–2024 period, includes diverse social and economic indicators. PCA was employed to reduce dimensionality and eliminate variable correlations, preserving the essential information within the data. The resulting principal components were then analyzed using GMM to uncover welfare clustering patterns. Based on the evaluation using the Bayesian Information Criterion (BIC) and silhouette score, the optimal configuration was achieved with two clusters, a tolerance of 1e-2, a maximum iteration of 200, and a silhouette score of 0.3403. The first cluster represented regions with higher welfare conditions, while the second indicated relatively lower welfare. These findings demonstrate that the PCA–GMM integration not only improves clustering accuracy but also enhances interpretability of welfare distribution across regions. Future studies may combine PCA with non-linear dimensionality reduction techniques such as Uniform Manifold Approximation and Projection (UMAP) to preserve local structures within complex datasets. Such integration is expected to reveal subtler and more dynamic welfare patterns, offering deeper insights into regional development disparities.