Abstract
Poverty remains a multidimensional social issue in Palembang City, where the poor population reached 10.22% in 2023. The distribution of social assistance often faces challenges such as data inaccuracies, uneven distribution, and subjective decision-making processes. This study aims to apply the K-Means Clustering algorithm to map 18 sub-districts in Palembang City into priority zones for social assistance distribution based on seven socio-economic indicators. The dataset includes poor population, unemployment rate, average income, population density, public facility access score, number of social assistance recipient households, and the Human Development Index (HDI). Data preprocessing was performed using Min-Max Normalization, followed by the calculation of the Composite Vulnerability Index (CVI). Model evaluation using the Elbow Method, Silhouette Score (0.45–0.55), and Davies–Bouldin Index confirmed K=3 as the optimal number of clusters. The resulting clusters categorize the sub-districts into high priority (emergency, e.g., Kertapati, Gandus, and Seberang Ulu I), medium priority (alert, e.g., Plaju, Sukarami, and Sako), and low priority (independent, e.g., Ilir Timur I, Bukit Kecil, and Alang-Alang Lebar). In addition, a Decision Support System (DSS) simulator was implemented based on the clustering and CVI results to provide data-driven budget and quota allocation recommendations.
How to Cite
| Issue | Vol. 5 No. 1 (2027) |
| Section | Articles |
| DOI | 10.58477/cj.v5i1.520 |
| Publication Date | 2027-02-01 |
| Pages | 12-17 |
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.