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

  • Dian Kurniasari Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia https://orcid.org/0000-0001-8488-8112
  • Virda Kurniawati Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia
  • Aang Nuryaman Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia http://orcid.org/0000-0003-4239-5322
  • Mustofa Usman Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia http://orcid.org/0000-0003-2649-0899
  • Rizki Khoirun Nisa Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia
DOI: https://doi.org/10.30598/barekengvol18iss3pp1919-1930
Keywords: Cluster Analysis, Fuzzy C-Means, Fuzzy Possibilistic C-Means, Partition Entropy

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.

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Published
2024-07-31
How to Cite
[1]
D. Kurniasari, V. Kurniawati, A. Nuryaman, M. Usman, and R. Nisa, “IMPLEMENTATION OF FUZZY C-MEANS AND FUZZY POSSIBILISTIC C-MEANS ALGORITHMS ON POVERTY DATA IN INDONESIA”, BAREKENG: J. Math. & App., vol. 18, no. 3, pp. 1919-1930, Jul. 2024.
Issue
Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2024 Dian Kurniasari, Virda Kurniawati, Aang Nuryaman, Mustofa Usman, Rizki Khoirun Nisa

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