Clustering based on poverty indicator data using K-Means cluster with Density-Based Spatial Clustering of Application with Noise

Sapriadi Rasyid; Siswanto Siswanto; Sitti Sahriman

doi:https://doi.org/10.59139/stattrans-2025-018

Clustering based on poverty indicator data using K-Means cluster with Density-Based Spatial Clustering of Application with Noise

Sapriadi Rasyid Department of Statistics, Hasanuddin University, Indonesia ORCID:https://orcid.org/0000-0006-2972-7125 , Siswanto Siswanto Corresponding author. Department of Statistics, Hasanuddin University, Indonesia ORCID:https://orcid.org/0000-0003-1934-5343 , Sitti Sahriman Department of Statistics, Hasanuddin University, Indonesia ORCID:https://orcid.org/0000-0002-9614-7132 Statistics in Transition new series, vol. 26, 2025, 2, pages: 113-128 Published online: 13 June 2025 https://doi.org/10.59139/stattrans-2025-018 Citation: Rasyid S., Siswanto S., Sahriman S., 2025. Clustering based on poverty indicator data using K-Means cluster with Density-Based Spatial Clustering of Application with Noise. Statistics in Transition new series, 26(2), pp. 113-128; https://doi.org/10.59139/stattrans-2025-018

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ABSTRACT

The Indonesian government has implemented poverty alleviation programs, including assistance programs for the poor. Despite these efforts, the number of impoverished individuals in South Sulawesi continues to rise. To address this issue, a statistical method is necessary to cluster the poor based on error indicators for each region, serving as a reference for providing assistance. The appropriate statistical method is cluster analysis by minimizing object differences within one cluster and maximizing object differences between clusters. This study employs two methods, namely K-Means and Density-Based Spatial Clustering of Application with Noise (DBSCAN), to compare their effectiveness based on the Silhouette Coefficient. The data used for the analysis included eight poverty indicators for the South Sulawesi province in 2022. The K-Means method yielded two optimal clusters, with cluster 1 comprised of 23 regencies and cities, and cluster 2 only of Makassar City. The results of further analysis on cluster 1 consisted of eight new clusters and produced a Silhouette Coefficient of 0.507. In contrast, the DBSCAN method yielded one cluster, that encompassed 23 regencies and cities, with Makassar City identified as noise. The results of the further analysis on the clusters consisted of one cluster with three noises and produced a Silhouette Coefficient of 0.318. The study concludes that K-Means provides a higher Silhouette Coefficient and a more accurate representation of poverty clusters in South Sulawesi, which renders it a more effective tool for targeted poverty alleviation efforts.

KEYWORDS

Cluster, DBSCAN, poverty, K-Means, Silhouette Coefficient

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