Clustering of Primary and Secondary School in Indonesia Using The Fuzzy C-Means Method Based on School Self-Evaluation With Imputation Data
DOI:
https://doi.org/10.53819/81018102t2024Abstract
The National Education Standard is one of the government's efforts to achieve equitable quality education. National Education Standards include eight outcomes, namely Graduation Competency Standards, Content Standards, Process Standards, Assessment Standards, Educators and Educators Standards, Facilities and Infrastructure Standards, Management Standards, and Financing Standards. This research was conducted to classify elementary and junior high schools in Indonesia based on SNP using the Fuzzy C-Means method. Prior to the cluster analysis, the missing value imputation was carried out using regression. The variables that have the lowest median and average value are the standard variables of educators and education personnel, while those with the highest value are the process standards. Based on the results of grouping using C-Means, the optimum number of clusters is four clusters with the most members being cluster 1 (the best cluster).
Keywords: Education; Clustering; Fuzzy C-Means; imputation missing value
References
Alkareem, H. S., & Hossain, S. S. (2014). Determinants of education quality: what makes student’s perception different. 52-67. https://doi.org/10.1080/23265507.2016.1155167
Bezdek, J., Ehrlich, R., & Full, W. (1984). Fuzzy C-Means Clustering Algorithm. Compters & Geosciences Vol 10, no. 2-3, 191-203. https://doi.org/10.1016/0098-3004 (84)90020-7
Chapman, C., & Sammons, P. (2013). School Self-Evaluation for School Improvement: What Works and Why?
Enders, C. (2010). Applied Missing Data Analysis. New York: The Guilford Press.
Ginns, P., Prosser, M., & Barrie, S. (2007). Students’ perceptions of teaching quality in higher education: The perspective of currently enrolled students. Studies in Higher Education, 32(5), 603–615. https://doi.org/10.1080/03075070701573773
Gujarati, D. (2006). Ekonometrika Dasar Diterjemahkan oleh Sumarto Zain. Jakarta: Erlangga.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. In Vectors. https://doi.org/10.1016/j.ijpharm.2011.02.019
Hofman, R. H., Dijkstra, N. J., & Hofman, W.H.A., (2009, March 10). School self-evaluation and student achievement. Retrieved from tandfonline.com: https://www.tandfonline.com/doi/full/10.1080/09243450802664115
Kaagan, S., & Smith, M. S. (1985). Indicators of Educational Quality. Educational Leadership, 21-24.
Ministry of Education and Culture. (2016). Ministry of Education and Culture Regulation no 28 regarding the Quality Assurance System. Jakarta.
National Inspectorate of Education. (2006). Proportional supervision and school improvement from an international perspective. A study into the side effects of utilising school selfevaluations for inspection purposes in Europe, Utrecht, The Netherlands: Inspectie van het onderwijs. Retrieved from tandfonline.com : https://www.tandfonline.com/doi/full/10.1080/09243450802664115
Nawari. (2010). Analisis Regresi dengan MS Excel 2007 dan SPSS. Jakarta: PT. Elex Media Komputindo.
Pyle, D. (1999). Data Preparation for Data Mining. USA: Academic Press.
Republic of Indonesia Government. (2013). Government Regulation of the Republic of Indonesia No 32 of 2013 concerning Amendments to Government Regulation No 19 of 2005 concerning National Education Standards. Jakarta.
Santoso, S. (2010). Statistik Parametrik, Konsep dan Aplikasi dengan SPSS. Jakarta: PT. Gramedia.
Schneider, J., Jacobsen, R., White, R., & Gehlbach, H. (2017). Building a better measure of school quality. In Phi Delta Kappan (Vol. 98, Issue 7). https://doi.org/10.1177/0031721717702631
UNESCO. (2019). SDG 4 Data Digest: How to Produce and Use the Global and Thematic Education Indicators. Canada: Institute for Statistics.
