Segmentation of Inclusive Economic Growth Profiles Across Provinces in Indonesia Using a Clustering Approach - Deki Fujiansyah (PT. Altaf Publishing Corp, 2025)
This study aims to address the issue of economic inequality across provinces in Indonesia despite national economic growth. Inclusive economic growth, which ensures that economic progress benefits all layers of society particularly those in lower socio-economic groups remains unevenly distributed. Using Gross Regional Domestic Product (GRDP) per employed person as a key indicator of inclusive growth, this research investigates the contributing factors and patterns of disparity among 34 Indonesian provinces from 2015 to 2021. A quantitative approach using K-Means clustering was applied to segment provinces based on determinants such as education, health, investment, formal sector involvement, and infrastructure. The study employed secondary data sourced from Statistics Indonesia (BPS) and the Directorate General of Fiscal Balance (DJPK), processed with machine learning techniques such as standardization, PCA dimensionality reduction, and cluster evaluation using Silhouette Score. The optimal number of clusters was determined using the Elbow Method, which identified three distinct groups of provinces. These clusters were analyzed to highlight the unique characteristics and disparities among them, supporting the development of more targeted, evidence-based policy recommendations. The findings not only deepen the understanding of interprovincial economic disparities but also emphasize the potential of data science in shaping inclusive and sustainable development strategies.