Kurniasari, Dian and Kurniawati, Virda and Nuryaman, Aang and Usman, Mustofa and Nisa, Rizki Khoirun (2024) Implementation of Fuzzy C-Means and Fuzzy Possibilistic C-Means Algorithms on Poverty Data in Indonesia. BAREKENG: Journal of Mathematics and Its Applications, 18 (3). pp. 1919-1930. ISSN 2615-3017

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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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika
Depositing User:	DIAN KURNIASARI
Date Deposited:	02 Oct 2024 04:30
Last Modified:	02 Oct 2024 04:30
URI:	http://repository.lppm.unila.ac.id/id/eprint/54180

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