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Implementasi Algoritma Centroid Linkage dan K-Medoids dalam Mengelompokkan Kabupaten/Kota di Sulawesi Selatan Berdasarkan Indikator Pendidikan

ESTIMASI: Journal of Statistics and Its Application · Vol. 5 No. 1 (2024) · pp. 61–74

DOI
10.20956/ejsa.v5i1.13605
ISSN
2721-379X, 2721-3803
Published
2024-01-29
Publisher
Hasanuddin University
Language
en

Abstract

Cluster analysis is a multivariate analysis technique that aims to cluster the observational data or variables into clusters in such a way that each cluster is homogeneous according to the factors used for clustering. This study used the Centroid linkage algorithm that was useful for forming groups based on the distance between centroids and the K-Medoids algorithm that was based on the use of the most centered object (medoid) to group districts/cities and obtained comparison results based on the education indicator data in South Sulawesi. The implementation of the Centroid Linkage Algorithm and K-Medoids on the education indicator data in South Sulawesi in 2018, showed that the grouping of districts/cities in South Sulawesi produced 2 clusters with cluster 1 of 21 districts/cities, and cluster 2 of 3. To determine the best method, it was seen from the value of the Standard Deviation ratio in the cluster 〖(S〗_W) and Standard Deviation between Clusters 〖(S〗_B) showed the same standard deviation ratio (S) in the Centroid Linkage algorithm and K-Medoids that was equal to 104,967.

Keywords

Collaboration network of the authors

33 authors · 71 co-authorship links

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  • Sultan Ageng Tirtayasa University
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Erna Tri HerdianiAffiliation unknown6
RaupongAffiliation unknown2
Iis Cendrah KasihAffiliation unknown2
Andi Kresna JayaAffiliation unknown2
Anisa KalondengAffiliation unknown2
Sitti SahrimanAffiliation unknown2
Ika FitrianaAffiliation unknown1
Asti Inayati MagfirahAffiliation unknown1
Mega Selvia TjahayaAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Reski AmalahAffiliation unknown1
Ratmila MammiAffiliation unknown1
Aqilah Salsabila RahmanAffiliation unknown1
Nurkamalia NurkamaliaAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Nurhidaya LAffiliation unknown1
Hendriete Tiur Marowi WaibusiAffiliation unknown1
Anisa AnisaSultan Ageng Tirtayasa University1
Victor LimanAffiliation unknown1
Sonya SonyaAffiliation unknown1
Surya Cahyadi SyamAffiliation unknown1
Nuralyatussa’ adaAffiliation unknown1
Nurul HikmahInstitut Agama Islam Negeri Kendari1
Raupong RaupongAffiliation unknown1
Parida Ayu NingsiAffiliation unknown1
Muh. HadriantoAffiliation unknown1
Rita Ruth Chendra ChendraAffiliation unknown1
Abdul Fathin FawwazAffiliation unknown1
Andini Riantika Putri Amirullah AmirullahAffiliation unknown1
Hadi Siswadi Hamran HamzahAffiliation 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">Implementasi Algoritma Centroid Linkage dan K-Medoids dalam Mengelompokkan Kabupaten/Kota di Sulawesi Selatan Berdasarkan Indikator Pendidikan</dc:title>
	<dc:creator xml:lang="en">Raja, Nur Alfianingsih</dc:creator>
	<dc:creator xml:lang="en">Tinungki, Georgina Maria</dc:creator>
	<dc:creator xml:lang="en">Sirajang, Nasrah</dc:creator>
	<dc:subject xml:lang="en">Centroid Linkage</dc:subject>
	<dc:subject xml:lang="en">Cluster Analysis</dc:subject>
	<dc:subject xml:lang="en">K-Medoids</dc:subject>
	<dc:subject xml:lang="en">Standard Deviation</dc:subject>
	<dc:description xml:lang="en">Cluster analysis is a multivariate analysis technique that aims to cluster the observational data or variables into clusters in such a way that each cluster is homogeneous according to the factors used for clustering. This study used the Centroid linkage algorithm that was useful for forming groups based on the distance between centroids and the K-Medoids algorithm that was based on the use of the most centered object (medoid) to group districts/cities and obtained comparison results based on the education indicator data in South Sulawesi. The implementation of the Centroid Linkage Algorithm and K-Medoids on the education indicator data in South Sulawesi in 2018, showed that the grouping of districts/cities in South Sulawesi produced 2 clusters with cluster 1 of 21 districts/cities, and cluster 2 of 3. To determine the best method, it was seen from the value of the Standard Deviation ratio in the cluster 〖(S〗_W) and Standard Deviation between Clusters 〖(S〗_B) showed the same standard deviation ratio (S) in the Centroid Linkage algorithm and K-Medoids that was equal to 104,967.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2024-01-29</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/13605</dc:identifier>
	<dc:identifier>10.20956/ejsa.v5i1.13605</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 5, No. 1, Januari, 2024 : Estimasi; 61-74</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/13605/11184</dc:relation>
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