Evaluation of Human Development Index Clustering Results Using Fuzzy C-Means and Possibilistic C-Means

Lativa Yulia Taviani; Eva Yulia Puspaningrum; Achmad Junaidi

doi:10.56480/jln.v5i2.1571

Authors

Lativa Yulia Taviani Universitas Pembangunan Nasional Veteran Jawa Timur
Eva Yulia Puspaningrum Universitas Pembangunan Nasional Veteran, Jawa Timur, Surabaya, Indonesia
Achmad Junaidi Universitas Pembangunan Nasional Veteran, Jawa Timur, Surabaya, Indonesia

DOI:

https://doi.org/10.56480/jln.v5i2.1571

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Keywords:

Human Development Index, Fuzzy C-Means, Possibilistic C-Means, Clustering Evaluation, Bayesian Optimization

Abstract

This study aims to evaluate the clustering results of the Human Development Index (HDI) in East Java using two fuzzy-based algorithms: Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM). The dataset includes key indicators Life Expectancy (LE), Mean Years of Schooling (MYS), Expected Years of Schooling (EYS), and Adjusted Per Capita Expenditure (APCE) sourced from the Central Bureau of Statistics. After preprocessing the data and applying Bayesian Optimization to determine optimal parameters, both clustering methods were executed and evaluated using internal metrics: Partition Coefficient (PC), Partition Entropy (PE), and Modified Partition Coefficient (MPC). The results show that both FCM and PCM successfully formed three meaningful clusters representing different levels of human development. However, PCM achieved higher clustering quality, as indicated by superior PC, PE, and MPC values. These findings highlight the effectiveness of PCM in handling complex, overlapping socio-economic data and offer insights for more accurate regional segmentation. The methodology is also applicable to broader socio-economic clustering tasks beyond HDI.

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