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Clustering ECE and NFE Accredited Statuses with Unsupervised Possibilistic Fuzzy C-Means Prihantoro, Agung; Kartianom, Kartianom; Atymtaevna, Begimbetova Guldana
Nuansa Akademik: Jurnal Pembangunan Masyarakat Vol. 10 No. 2 (2025): In Progress
Publisher : Lembaga Dakwah dan Pembangunan Masyarakat Universitas Cokroaminoto Yogyakarta (LDPM UCY)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47200/jnajpm.v10i2.3025

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.
Factors Influencing the Success of Students’ Learning through Online Learning/Distance Education : A Bibliometric Analysis of Scopus Database Santoso, Agus; Retnawati, Heri; Rosyada, Munaya Nikma; Apino, Ezi; Rafi, Ibnu; Kartianom, Kartianom; Dauletkulova, Aigul
Jurnal Kependidikan : Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran Vol. 10 No. 1 (2024): March
Publisher : LPPM Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i1.10856

Abstract

This study aims to examine the co-occurrence of several topics linked to factors that influence student learning success and explore the motor themes related to factors that influence student learning success. This study used the literature review method with data analysis using R Studio and VOSViewer software. Data analyzed were exported from the Scopus website from 2013 to 2023 of 383 documents. The research data analysis techniques used network mapping analysis of VOSviewer results (network visualization) and content analysis within the keywords and studies. The findings of this study presented the keywords (most frequent words, trending topics), co-occurrence network (thematic map and thematic evolution), and discussion of topics related to factors that influence student learning success. The basic themes identified were higher education and Covid-19. Meanwhile, gender and self-efficacy are motor themes that could be researched further as those can influence student learning performance in online learning. The findings of the bibliometric analysis were intended to reveal unique insights into the factors that determine student learning performance in distant learning and contribute to previously unexplored issues.