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Pengelompokan Kemiskinan di Provinsi Sulawesi Selatan Tahun 2023 dengan Metode K-Means Clustering

ESTIMASI: Journal of Statistics and Its Application · Vol. 6 No. 2 (2025) · pp. 279–285

DOI
10.20956/ejsa.v6i2.45824
ISSN
2721-379X, 2721-3803
Published
2025-08-04
Publisher
Hasanuddin University
Language
en

Abstract

Poverty remains a significant social and economic issue in South Sulawesi Province. This study aims to classify districts/cities in South Sulawesi based on poverty levels using the K-Means Clustering method. The data used were obtained from the Central Bureau of Statistics (BPS) for 2023, including indicators such as the percentage of poor population, education level, and employment sector. The Silhouette Index method was applied to determine the optimal number of clusters. The results indicate that South Sulawesi is divided into two clusters, representing high and low poverty levels. The scatter plot further reveals that cluster 1 is more varied, while cluster 2 is more concentrated. These findings can serve as a foundation for formulating more targeted policies to reduce poverty in South Sulawesi.

Keywords

Collaboration network of the authors

40 authors · 111 co-authorship links · showing the 40 most connected

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  • Sultan Ageng Tirtayasa University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Raupong RaupongAffiliation unknown4
Nirwan IlyasAffiliation unknown4
Siswanto SiswantoAffiliation unknown4
Anisa AnisaSultan Ageng Tirtayasa University2
Sri Astuti ThamrinAffiliation unknown2
Fitri FitriAffiliation unknown2
Rini JamalAffiliation unknown2
Widya Nauli Amalia PuteriAffiliation unknown1
Zhazha Alifkhamulki RamdhaniAffiliation unknown1
Samsul ArifinAffiliation unknown1
Bunga ApriliaAffiliation unknown1
Dewi Rahma EnteAffiliation unknown1
Muhammad Jayzul UsrahAffiliation unknown1
AnisaAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Novilia JaoAffiliation unknown1
Ainun Utari BudistiharahAffiliation unknown1
Mamik UtamiAffiliation unknown1
Nasrah SirajangAffiliation unknown1
Nurkamalia NurkamaliaAffiliation unknown1
Nurul IkhsaniAffiliation unknown1
Hajratul Ashwad KAffiliation unknown1
Andi Kresna JayaAffiliation unknown1
Andi Ummi Melin Aicha AichaAffiliation unknown1
RaupongAffiliation unknown1
SuritmanAffiliation unknown1
Georgina Maria TinungkiAffiliation unknown1
Surya Cahyadi SyamAffiliation unknown1
Afifah Mutiah NurdinAffiliation unknown1
Muh. Indirwan ArfanAffiliation unknown1
Kevin Ekarinaldo PalunganAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Pengelompokan Kemiskinan di Provinsi Sulawesi Selatan Tahun 2023 dengan Metode K-Means Clustering</dc:title>
	<dc:creator xml:lang="en">Wulandari, A. Elisha</dc:creator>
	<dc:creator xml:lang="en">Baso, Andi M. Alfin</dc:creator>
	<dc:creator xml:lang="en">Fajri, Belia Nurul</dc:creator>
	<dc:creator xml:lang="en">Kalondeng, Anisa</dc:creator>
	<dc:creator xml:lang="en">Islamiyati, Anna</dc:creator>
	<dc:creator xml:lang="en">Pannu, Abdullah</dc:creator>
	<dc:creator xml:lang="en"> Fadil, Muhammad</dc:creator>
	<dc:creator xml:lang="en">Vallarino, Alfian Akbar</dc:creator>
	<dc:creator xml:lang="en">Rahman, Anugrah Nur Isnaeni</dc:creator>
	<dc:subject xml:lang="en">Poverty</dc:subject>
	<dc:subject xml:lang="en">K-Means Clustering</dc:subject>
	<dc:subject xml:lang="en">Clustering</dc:subject>
	<dc:subject xml:lang="en">South Sulawesi</dc:subject>
	<dc:subject xml:lang="en">Silhouette Index</dc:subject>
	<dc:description xml:lang="en">Poverty remains a significant social and economic issue in South Sulawesi Province. This study aims to classify districts/cities in South Sulawesi based on poverty levels using the K-Means Clustering method. The data used were obtained from the Central Bureau of Statistics (BPS) for 2023, including indicators such as the percentage of poor population, education level, and employment sector. The Silhouette Index method was applied to determine the optimal number of clusters. The results indicate that South Sulawesi is divided into two clusters, representing high and low poverty levels. The scatter plot further reveals that cluster 1 is more varied, while cluster 2 is more concentrated. These findings can serve as a foundation for formulating more targeted policies to reduce poverty in South Sulawesi.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2025-08-04</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/45824</dc:identifier>
	<dc:identifier>10.20956/ejsa.v6i2.45824</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 6, No. 2, Juli, 2025 : Estimasi; 279-285</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/45824/13232</dc:relation>
</oai_dc:dc>