Comparison Of FCM And FKNN Methods For Clustering Provinces In Indonesia Based On People's Welfare Indicators

Maghfiroh firoh; Tony Yulianto; Faisol Faisol

doi:10.20956/j.v22i1.44314

Authors

Maghfiroh firoh Study Program of Mathematics, Faculty Of Mathematics and Natural Sciences, Madura Islamic University
Tony Yulianto Study Program of Mathematics, Faculty Of Mathematics and Natural Sciences, Madura Islamic University https://orcid.org/0000-0002-9732-6036
Faisol Faisol Study Program of Mathematics, Faculty Of Mathematics and Natural Sciences, Madura Islamic University https://orcid.org/0000-0003-2900-1448

DOI:

https://doi.org/10.20956/j.v22i1.44314

Keywords:

Fuzzy C-Means, Fuzzy K-Nearest Neighbor, Kesejahteraan Rakyat, clustering

Abstract

Improving people's welfare is one of the main indicators of a country's successful development. Public welfare covers various aspects, such as population, social, economic, and labor, which continue to evolve along with the growth and changes in human life needs. In Indonesia, various challenges such as rapid population growth, uneven population distribution, unemployment, poverty, and low Human Development Index (HDI) require strategic solutions to ensure equitable welfare. This study compares the Fuzzy C-Means (FCM) and Fuzzy K-Nearest Neighbor (FKNN) methods to cluster provinces in Indonesia based on public welfare indicators using variables of population density, population growth rate, percentage of poverty rate, Human Development Index (HDI), and Open Unemployment Rate (TPT). The clustering results are expected to support the government in formulating development policies that are more targeted and effective. The results showed that the best method was the Fuzzy C-Means method as many as 2 clusters with the highest Silhouette coefficient value of which states that the cluster structure formed in this clustering is very good. Cluster 1 is categorized as prosperous and cluster 2 is categorized as less prosperous.

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