What type of study is this?

This is a Quantitative Study study.

September 16, 2025

Segmentation of West Java Province Based on Socio-Economic Indicators Using K-Means and Agglomerative Clustering Methods

Key Points

Agglomerative Clustering produces more interpretable segmentations than K-Means, highlighting regional socio-economic similarities.
The use of the Elbow Method and Silhouette Score effectively assessed the quality of the clusters formed using different methods.
K-Means demonstrated better computational efficiency, processing faster than Agglomerative Clustering in segmenting regions.
Insights from this study can help policymakers prioritize development effectively based on varying socio-economic conditions across West Java.

Abstract

Regional segmentation based on socio-economic indicators is a crucial approach in data-driven development planning. Through accurate segmentation, governments can design more targeted policies aligned with the specific characteristics of each region. This study aims to compare two clustering methods, namely K-Means and Agglomerative Clustering, in grouping regions within West Java Province based on socio-economic indicators such as poverty rate, open unemployment rate, and Human Development Index (HDI). The analysis was conducted using the Python programming language on the Google Colab platform. Cluster performance was evaluated using the Elbow Method to determine the optimal number of clusters and the Silhouette Score to assess cluster quality. The results indicate that Agglomerative Clustering produces more consistent and interpretable segmentations, particularly in reflecting the socio-economic similarities between regions. However, in terms of computational efficiency, the K-Means method performs better due to its faster processing time. These findings offer valuable insights for regional policymakers in setting development priorities more effectively, grounded in the actual socio-economic conditions of each area

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