Clustering ECE and NFE Accredited Statuses with Unsupervised Possibilistic Fuzzy C-Means

Agung Prihantoro; Kartianom Kartianom; Begimbetova Guldana Atymtaevna

doi:10.47200/jnajpm.v10i2.3025

Authors

Agung Prihantoro Universitas Cokroaminoto Yogyakarta Indonesia
Kartianom Kartianom Institut Agama Islam Negeri Bone Indonesia
Begimbetova Guldana Atymtaevna Abai Kazakh National Pedagogical University Kazakhstan

DOI:

https://doi.org/10.47200/jnajpm.v10i2.3025

Keywords:

UPFC, accredited status, early childhood education, non-formal education

Abstract

The research aims to have clusters of accredited statuses of early childhood education (ECE) and non-formal education (NFE) institutions in Yogyakarta Special Province in Indonesia, which are created by unsupervised possibilistic fuzzy c-means (UPFC) and to organize the institutions into the clusters created. The Board of National Accreditation for ECE and NFE determined four accredited statuses of A, B, C, and TT. The research employs a method of machine learning, especially UPFC. The dataset is a data of accreditation 2022 from the Board of National Accreditation for ECE and NFE of Yogyakarta Special Province. The data consists of 760 institutions composed of 749 (98.55%) ECE institutions and 11 (1.45%) NFE institutions. The analysis of UPFC created two clusters of accredited statuses of the institutions, thar are Accredited A that consists 437 (57.5%) institutions and Accredited B consisting of 323 (42.5%) institutions. The names of the clusters have political impact.

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