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Vol.14, No.2, May 2025. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Accuracy of Fuzzy Clustering Based on School Accreditation Data: Reflection and Evaluation for Improving Education Quality
Eko Wahyunanto Prihono, Haryanto Haryanto, Sudji Munadi
© 2025 Haryanto Haryanto, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 14, Issue 2, Pages 1715-1724, ISSN 2217-8309, DOI: 10.18421/TEM142-69, May 2025.
Received: 23 August 2024. Revised: 06 March 2025.
Abstract:
Fuzzy clustering has been widely utilised to handle various types of data. This method is particularly effective for grouping large datasets due to its ability to manage complex structures. This study aimed to classify and correctly utilise school accreditation data. The data was obtained from 230 schools that had scores below the national average standard in education in 2021. The results of the study indicated that the fuzzy clustering analysis achieved satisfactory accuracy, demonstrating its effectiveness in correctly data grouping. Furthermore, fuzzy clustering showed highly significant performance in categorising datasets. This study can be beneficial to accreditation institutions and policymakers for making informed decisions to improve the quality of education by effectively utilising datasets. Although analysing large datasets can be time-consuming, the features of the fuzzy clustering algorithm greatly assist in making high-quality decisions.
Keywords – Accreditation of schools, accuracy, cluster analysis, fuzzy clustering |
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