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IMPLEMENTATION OF FUZZY C-MEANS AND FUZZY POSSIBILISTIC C-MEANS ALGORITHMS ON POVERTY DATA IN INDONESIA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 3 (2024) · pp. 1919–1930

DOI
10.30598/barekengvol18iss3pp1919-1930
ISSN
2615-3017, 1978-7227
Published
2024-07-31
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Cluster analysis involves the methodical categorization of data based on the degree of similarity within each group to group data with similar characteristics. This study focuses on classifying poverty data across Indonesian provinces. The methodologies employed include the Fuzzy C-Means (FCM) and Fuzzy Probabilistic C-Means (FPCM) algorithms. The FCM algorithm is a clustering approach where membership values determine the presence of each data point in a cluster. On the other hand, the FPCM algorithm builds upon FCM and Possibilistic C (PCM) algorithms by incorporating probabilistic considerations. This research compares the FCM and FPCM algorithms using local poverty data from Indonesia, specifically examining the Partition Entropy (PE) index value. It aims to identify the optimal number of clusters for provincial-level poverty data in Indonesia. The findings indicate that the FPCM algorithm outperforms the FCM algorithm in categorizing poverty in Indonesia, as evidenced by the PE validity index. Furthermore, the study identifies that the ideal number of clusters for the data is 2.

Collaboration network of the authors

19 authors · 39 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Lampung University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Warsono WarsonoLampung University2
Favorisen Rosyking LumbanrajaLampung University2
Nur AlifiahLampung University1
Amanto AmantoLampung University1
Warsono WarsonoLampung University1
Warsono WarsonoAffiliation unknown1
Zaenal MukhlisinAffiliation unknown1
Wamiliana WamilianaAffiliation unknown1
La ZakariaAffiliation unknown1
Fatimatuzzahra FatimatuzzahraAffiliation unknown1
Agus SutrisnoAffiliation unknown1
Fachri FaisalAffiliation unknown1
Baki SwitaAffiliation unknown1
Mulia AstutiAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">IMPLEMENTATION OF FUZZY C-MEANS AND FUZZY POSSIBILISTIC C-MEANS ALGORITHMS ON POVERTY DATA IN INDONESIA</dc:title>
	<dc:creator>Kurniasari, Dian</dc:creator>
	<dc:creator>Kurniawati, Virda</dc:creator>
	<dc:creator>Nuryaman, Aang</dc:creator>
	<dc:creator>Usman, Mustofa</dc:creator>
	<dc:creator>Nisa, Rizki Khoirun</dc:creator>
	<dc:description xml:lang="en-US">Cluster analysis involves the methodical categorization of data based on the degree of similarity within each group to group data with similar characteristics. This study focuses on classifying poverty data across Indonesian provinces. The methodologies employed include the Fuzzy C-Means (FCM) and Fuzzy Probabilistic C-Means (FPCM) algorithms. The FCM algorithm is a clustering approach where membership values determine the presence of each data point in a cluster. On the other hand, the FPCM algorithm builds upon FCM and Possibilistic C (PCM) algorithms by incorporating probabilistic considerations. This research compares the FCM and FPCM algorithms using local poverty data from Indonesia, specifically examining the Partition Entropy (PE) index value. It aims to identify the optimal number of clusters for provincial-level poverty data in Indonesia. The findings indicate that the FPCM algorithm outperforms the FCM algorithm in categorizing poverty in Indonesia, as evidenced by the PE validity index. Furthermore, the study identifies that the ideal number of clusters for the data is 2.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-07-31</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12585</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss3pp1919-1930</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application; 1919-1930</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss3year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12585/8542</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Dian Kurniasari, Virda Kurniawati, Aang Nuryaman, Mustofa Usman, Rizki Khoirun Nisa</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>