Work

Evaluation of the Performance of K-means and Bisecting K-means in Clustering Indonesian Regions using Poverty Data

Year: 2026

Type: article

Abstract: Clustering regions in Indonesia based on poverty data is essential for developing targeted policies. A key challenge is determining the optimal number of clusters to accurately reflect regional disparities. This study compares the Bisecting K-means and K-means algorithms, evaluating them with Sum Squared Error (SSE), David-Bouldin Index (DBI), and Silhouette Coefficient (SC). Evaluation results identified K-means with six clusters (k= 6) as the most optimal model. It achieved a low SSE (2129.37), a relatively low DBI (0.82), and a moderate SC (0.3625). This model successfully maps regions from the most prosperous to the poorest, providing a clear basis for poverty alleviation strategies. For future work, a deeper analysis of cluster members using heatmaps or boxplots is recommended. Visualizing the results on a map would also help stakeholders easily understand the spatial distribution of poverty levels. Furthermore, comparing clustering results year-on-year would be valuable for tracking regional progress.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/e3sconf/202668702008/pdf

Authors: Dhea Cindera Bantala, Paulina Heruningsih Prima Rosa

Institution: Sanata Dharma University

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: No poverty, Reduced inequalities

Open Access status: green

OpenAlex ID: W7124449547

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