K-means clustering for classifying the quality management of secondary education in Indonesia
Rahmat Kamal, Institut Agama Islam Negeri Pekalongan, Indonesia
Abstract
Mapping educational management is critical for improving national education quality. This study aims to classify Indonesian provinces in terms of secondary education management within quality standard parameters. Framed in a quantitative approach with the K-means cluster analysis, this study applied eleven parameters to classify the provinces into three main clusters. These parameters were schools, new entrants, students, repeaters, drop-outs, graduates, teachers, classrooms, laboratories, libraries, and school health services. Secondary data related to secondary education in Indonesia published by the Ministry of Education and Culture were analyzed. With the R software, the clusters were formed based on the shared characteristics of the provinces. This study found that Cluster 1 had twenty-two provinces, Cluster 2 two provinces, and Cluster 3 ten provinces. Cluster 2 was found to be the best cluster, while West Java and East Java shared similar characteristics, hence in the same cluster. Further, 5.88% of the provinces were eligible to be pilot models for the standard quality management of education in Indonesia. There is, therefore, a pressing need for the improvement of education infrastructure to support a better-quality education.
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Abdinazar, N. (2020). Managing the quality of training of pedagogical personnel’s on the basis of TQM - (Total Quality Management). International Journal of Psychosocial Rehabilitation, 24(5), 3759–3770. https://doi.org/10.37200/IJPR/V24I5/PR202085
Adachi, K. (2016). Matrix-based introduction to multivariate data analysis. Springer.
Ananda, R. (2019). Analisis mutu pendidikan sekolah menengah atas program ilmu alam di Jawa Tengah dengan algoritme K-means terorganisir. Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 2(1), 65–72. https://doi.org/10.20895/inista.v2i1.97
Anttila, J., & Jussila, K. (2017). Understanding quality – conceptualization of the fundamental concepts of quality. International Journal of Quality and Service Sciences, 9(3–4), 251–268. https://doi.org/10.1108/IJQSS-03-2017-0020
Aoyama, I., Barnard-Brak, L., & Talbert, T. L. (2011). Cyberbullying among high school students: Cluster analysis of sex and age differences and the level of parental monitoring. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 1(1), 25–35. https://doi.org/10.4018/ijcbpl.2011010103
Bendermacher, G. W. G., oude Egbrink, M. G. A., Wolfhagen, I. H. A. P., & Dolmans, D. H. J. M. (2017). Unravelling quality culture in higher education: a realist review. Higher Education, 73(1), 39–60. https://doi.org/10.1007/s10734-015-9979-2
Bojesen, A. B., & Rayce, S. B. (2020). Effectiveness of a school-based road safety educational program for lower secondary school students in Denmark: A cluster-randomized controlled trial. Accident Analysis and Prevention, 147, 105773. https://doi.org/10.1016/j.aap.2020.105773
Butarbutar, N., Windarto, A. P., Hartama, D., & Solikhun, S. (2017). Komparasi kinerja algoritma fuzzy c-means dan k-means dalam pengelompokan data siswa berdasarkan prestasi nilai akademik siswa. Jurasik (Jurnal Riset Sistem Informasi Dan Teknik Informatika), 1(1), 46. https://doi.org/10.30645/jurasik.v1i1.8
Chabrol, H., Melioli, T., Van Leeuwen, N., Rodgers, R., & Goutaudier, N. (2015). The Dark Tetrad: Identifying personality profiles in high-school students. Personality and Individual Differences, 83, 97–101. https://doi.org/10.1016/j.paid.2015.03.051
Darcan, O. N., & Badur, B. Y. (2012). Student profiling on academic performance using cluster analysis. Journal of E-Learning & Higher Education, e1-8. https://doi.org/10.5171/2012.622480
Desmet, A., Aelterman, N., Bastiaensens, S., Van Cleemput, K., Poels, K., Vandebosch, H., Cardon, G., & De Bourdeaudhuij, I. (2015). Secondary school educators’ perceptions and practices in handling cyberbullying among adolescents: A cluster analysis. Computers and Education, 88, 192–201. https://doi.org/10.1016/j.compedu.2015.05.006
Dumuid, D., Olds, T., Martín-Fernández, J.-A., Lewis, L. K., Cassidy, L., & Maher, C. (2017). Academic performance and lifestyle behaviors in Australian School Children: A cluster analysis. Health Education & Behavior, 44(6), 918–927. https://doi.org/10.1177/1090198117699508
Erdoğmuş, N., & Esen, M. (2016). Classifying universities in Turkey by hierarchical cluster analysis. Education & Science/Egitim ve Bilim, 41(184). https://doi.org/10.15390/EB.2016.6232
Everitt, B., & Hothorn, T. (2011). An introduction to applied multivariate analysis with R. Springer Science & Business Media.
Hanushek, E. A., Schwerdt, G., Woessmann, L., & Zhang, L. (2017). General education, vocational education, and labor-market outcomes over the lifecycle. Journal of Human Resources, 52(1), 48–87. https://doi.org/10.3368/jhr.52.1.0415-7074R
Härdle, W. K., & Simar, L. (2013). Applied multivariate statistical analysis. In Applied Multivariate Statistical Analysis. https://doi.org/10.1007/978-3-642-17229-8
Hartanto, Y. I., Rusgiyono, A., & Wuryandari, T. (2017). Penerapan analisis klaster metode ward terhadap kabupaten/kota di Jawa Tengah berdasarkan pengguna alat kontrasepsi. Jurnal Gaussian, 6(4), 528–537. https://doi.org/10.14710/j.gauss.v6i4.30387
Kabók, J., Radišić, S., & Kuzmanović, B. (2017). Cluster analysis of higher-education competitiveness in selected European countries. Economic Research-Ekonomska Istrazivanja , 30(1), 845–857. https://doi.org/10.1080/1331677X.2017.1305783
Mahmudah, U., Suhartono, & Fatimah, S. (2017). A robust approach to analyzing the factors influencing quality education in Indonesia. New Educational Review, 49(3), 77–90. https://doi.org/10.15804/tner.2017.49.3.06
Mahmudah, U., Suhartono, S., & Rohayana, A. D. (2018). A robust data envelopment analysis for evaluating technical efficiency of indonesian high schools. Jurnal Pendidikan IPA Indonesia, 7(1), 114–121. https://doi.org/10.15294/jpii.v7i1.9883
Martinez, P. R., Molnar, J., Trejos, E., Meyer, D., Meyer, S. T., & Tollner, W. (2004). Cluster membership as a competitive advantage in aquacultural development: Case study of tilapia producers in Olancho, Honduras. Aquaculture Economics & Management, 8(5–6), 281–294. https://doi.org/10.1080/13657300409380370
Ministry of Education and Culture, I. (2020). Senior high school in statistics. In Ministry of Education and culture (Vol. 53, Issue 9).
Ng, B. L. L., Liu, W. C., & Wang, J. C. K. (2016). Student motivation and learning in mathematics and science: A cluster analysis. International Journal of Science and Mathematics Education, 14(7), 1359–1376. https://doi.org/10.1007/s10763-015-9654-1
Nugraha, G. S., & Hairani, H. (2018). Aplikasi pemetaan kualitas pendidikan di Indonesia menggunakan metode k-means. Jurnal MATRIK, 17(2), 13–23. https://doi.org/10.30812/matrik.v17i2.84
Nurzahputra, A., Muslim, M. A., & Khusniati, M. (2017). Penerapan Algoritma K-Means Untuk Clustering Penilaian Dosen Berdasarkan Indeks Kepuasan Mahasiswa. Techno. Com, 16(1), 17–24. https://doi.org/10.33633/tc.v16i1.1284
Oktavianty, E., Junaidi, & Handayani, L. (2019). Pengelompokkan kabupaten/kota di Sulawesi berdasarkan indikator pendidikan menggunakan analisis klaster average linkage dan median linkage. Natural Science: Journal of Science and Technology, 8(3), 191–197. https://doi.org/10.22487/25411969.2019.v8.i3.14960
Omigie, C. A., Ikenwe, I. J., & Idhalama, O. U. (2019). The role of knowledge management for education in Nigeria. International Multidisciplinary Research Journal, 20–23. https://doi.org/10.25081/imrj.2019.v9.5496
Papi, M., & Teimouri, Y. (2014). Language learner motivational types: A cluster analysis study. Language Learning, 64(3), 493–525. https://doi.org/10.1111/lang.12065
Perrotta, C., & Williamson, B. (2018). The social life of Learning Analytics: cluster analysis and the ‘performance’of algorithmic education. Learning, Media and Technology, 43(1), 3–16. https://doi.org/10.1080/17439884.2016.1182927
Prayoga, A., & Zain, I. (2016). Analisis faktor dan pengelompokan kecamatan berdasarkan indikator mutu pendidikan jenjang pendidikan dasar di Kabupaten Sidoarjo. Jurnal Sains Dan Seni ITS, 4(2). https://doi.org/10.12962/j23373520.v4i2.10920
Rahayuningsih, R. S., Fajaruddin, S., & Manggalasari, L. C. (2018). The implementation of total quality management in vocational high schools. Psychology, Evaluation, and Technology in Educational Research, 1(1), 31–40. https://doi.org/10.33292/petier.v1i1.20
Sadeghi Moghadam, M. R., Safari, H., & Yousefi, N. (2018). Clustering quality management models and methods: systematic literature review and text-mining analysis approach. Total Quality Management and Business Excellence. https://doi.org/10.1080/14783363.2018.1540927
Stukalina, Y. (2010). Using quality management procedures in education: Managing the learner‐centered educational environment. Technological and Economic Development of Economy, 16(1), 75–93. https://doi.org/10.3846/tede.2010.05
Subanar, H. (2011). Analisis kluster untuk pemetaan mutu pendidikan di Aceh. [Yogyakarta]: Universitas Gadjah Mada.
Thrun, M. C. (2018). Projection-based clustering through self-organization and swarm intelligence. In Projection-Based Clustering through Self-Organization and Swarm Intelligence. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-20540-9
Tkaczynski, A. (2017). Segmentation using two-step cluster analysis. In Segmentation in social marketing (pp. 109–125). Springer. https://doi.org/https://doi.org/10.1007/978-981-10-1835-0_8
Töremen, F., Karakuş, M., & Yasan, T. (2009). Total quality management practices in Turkish primary schools. Quality Assurance in Education, 17(1), 30–44. https://doi.org/10.1108/09684880910929917
Vörös, A., & Snijders, T. A. B. (2017). Cluster analysis of multiplex networks: Defining composite network measures. Social Networks, 49, 93–112. https://doi.org/10.1016/j.socnet.2017.01.002
Widiyaningtyas, T., Prabowo, M. I. W., & Pratama, M. A. M. (2017). Implementation of K-means clustering method to distribution of high school teachers. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 1–6.
Wijayanto, F. (2016). Clustering analysis on Indonesian education quality performance using input–output model. Advanced Science Letters, 22(10), 2799–2803. https://doi.org/10.1166/asl.2016.7127
Windarto, A. P. (2017). Penerapan datamining pada ekspor buah-buahan menurut negara tujuan menggunakan k-means clustering method. Techno.Com, 16(4), 348–357. https://doi.org/10.33633/tc.v16i4.1447
Wong, A. (1999). Total quality management in the construction industry in Hong Kong: A supply chain management perspective. Total Quality Management, 10(2), 199–208. https://doi.org/10.1080/0954412997956
Xu, D., & Trimble, M. (2016). What about certificates? Evidence on the labor market returns to nondegree community college awards in two states. Educational Evaluation and Policy Analysis, 38(2), 272–292. https://doi.org/10.3102/0162373715617827
Yamauchi, F. (2011). School quality, clustering and government subsidy in post-apartheid South Africa. Economics of Education Review, 30(1), 146–156. https://doi.org/10.1016/j.econedurev.2010.08.002
Yuguda Kotirde, I., Bin, J., & Yunos, M. (2015). ScienceDirect-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Faculty of Technical and Vocational Education The Processes of Supervisions in Secondary Schools Educational System in Nigeria. Procedia - Social and Behavioral Sciences, 204, 259–264. https://doi.org/10.1016/j.sbspro.2015.08.149
DOI: https://doi.org/10.21831/cp.v40i3.40150
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