School Clustering Using Fuzzy C Means Method
DOI:
10.33395/sinkron.v4i1.10168Keywords:
Admision of new students; Cluster; Fuzzy C Means; School; SocializationAbstract
Subang State Polytechnic is one of tertiary institution which was established in 2014. As a new tertiary institution this institution certainly competes with other tertiary institutions in obtaining prospective students. Currently, Subang State Polytechnic determines some schools to be visited for socialization activities for New Student Admissions based on the large number of students in the schools at Subang district. However, it does not prove that it has influenced the students to enroll in Subang State Polytechnic. This research highlights the issues involved in each school including the number of graduates, graduates who continue to colleges, graduates who continue to Subang State Polytechnic, average score of school’s national examination, average report card grades, number of counseling guidance teachers, number of college socialization, and the distance from the school to Subang State Polytechnic. As a result, there are 40 schools that are selected to determine as the potential schools do a socialization. The grouping schools use the Fuzzy C Means method and determe the number of groups or clusters use Modified Partition Coeffcient (MPC). The results of the MPC calculation revealed that there are 3 clusters, each cluster has a cluster center and members. In cluster I, there are 9 schools, cluster II has 16 schools, and cluster III consists of 15 schools. The results of the clustering assisted the New Student Admissions committees in determining the potential schools to do a socialization activities and it was a major step towards the success of the New Student Admissions at Subang State Polytechnic.
Downloads
References
Ayu, & Wulaning, P. (2016). Perancangan Sistem Pendukung Keputusan Pemasaran STIKOM Bali. Sistem Dan Informatika, 10(2).
Blankenau, W. (2014). Admission standards, student effort, and the creation of skilled jobs. Economic Modelling, 43, 209–216.
Gomes, E. P., Blanco, C. J. C., & Pessoa, F. C. L. (2019). Identification of homogeneous precipitation regions via Fuzzy c-means in the hydrographic region of Tocantins–Araguaia of Brazilian Amazonia. Applied Water Science, 9(1), 1–12. http://doi.org/10.1007/s13201-018-0884-6
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques 3rd Edition (3rd ed.).
Han, & Kamber, M. (2011). Data mining: Concepts and Techniques. 3rd Edition. San Francisco: Morgan Kaufmann Publisher, 1.
Hardiani, T. (2018). Segmentasi Nasabah Simpanan Menggunakan Fuzzy C Means Dan Fuzzy RFM (Recency , Frequency , Monetary) Pada BMT XYZ. Jurnal Ilmiah NERO, 3(3), 185–192.
Heinesen, E. (2018). Admission to higher education programmes and student educational outcomes and earnings – evidence from Denmark. Economics of Education Review, 10, 3–9. http://doi.org/10.1016/j.econedurev.2018.01.002
Javadi, S., Rameez, M., Dahl, M., & Pettersson, M. I. (2018). Vehicle Classification Based on Multiple Fuzzy C-Means Clustering Using Dimensions and Speed Features. Procedia Computer Science, 126, 1344–1350. http://doi.org/10.1016/j.procs.2018.08.085
Kon, M., & Kuwano, H. (2013). On sequences of fuzzy sets and fuzzy set-valued mappings. Fixed Point Theory and Applications, 327, 1–19. http://doi.org/10.1186/1687-1812-2013-327
Kurniawan, E. (2016). Metode TOPSIS untuk Menentukan Penerimaan Mahasiswa Baru Pendidikan Dokter di Universitas Muhammadiyah purwokerto. Bachelor Thesis, (Universitas Muhammadiyah Purwokerto).
Memon, K., & Lee, D.-H. (2018). Generalised kernel weighted fuzzy C-means clustering algorithm with local information. Fuzzy Sets and Systems, 340, 91–108.
Muhardi, & Nisar. (2015). Penentuan Penerimaan Beasiswa dengan Algoritma Fuzzy C Means. Universitas Megow Pak Tulang Bawang, Tim Darmajaya, 1(2).
Nariya, M., & Kim, J. H. (2017). Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets. Pharmaceutical Sciences, 106(11), 3270–3279.
PMB Polsub, P. (2016). Laporan PMB POLSUB Tahun 2016-2017. Politeknik Negeri Subang.
Rai, S. P., Sharma, N., & Lohani, A. K. (2019). Novel approach for issues identification in transboundary water management using fuzzy c ‑ means clustering. Applied Water Science, 9(1), 1–11. http://doi.org/10.1007/s13201-018-0889-1
Ratnawati, A. Y., Kom, S., Susena, M. M. E., Kom, S., Kom, M., & Terdahulu, P. (2017). KESEJAHTERAAN PEDAGANG BATIK DI KOTA SURAKARTA. SAINSTECH, 4(2), 58–66.
Rohayani, H. (2013). Analisis Sistem Pendukung Keputusan Dalam Memilih Program Studi Menggunakan Metode Logika Fuzzy. Jurnal Sistem Informasi (JSI), 5(1), 530–539.
Saputra, D. B., & Riksakomara, E. (2018). Implementasi Fuzzy C-Means dan Model RFM untuk Segmentasi Pelanggan. Jurnal Teknik ITS, 7(1), 1–6.
Stetco, A., Zeng, X., & Keane, J. (2015). Expert Systems with Applications Fuzzy C-means ++ : Fuzzy C-means with effective seeding initialization. EXPERT SYSTEMS WITH APPLICATIONS, 42(21), 7541–7548. http://doi.org/10.1016/j.eswa.2015.05.014
Suleman, A. (2015). A new perspective of modified partition coefficient ✩. Pattern Recognition Letters, 56, 1–6. http://doi.org/10.1016/j.patrec.2015.01.008
X, W., Kumar, & Q, R. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.
Xue, M., Zhou, L., Kojima, N., Sarmento, L., Machimura, T., & Tokai, A. (2018). Application of fuzzy c-means clustering to PRTR chemicals uncovering their release and toxicity characteristics. Science of the Total Environment, 622–623, 861–868. http://doi.org/10.1016/j.scitotenv.2017.12.032
Zhang, L., & Luo, M. (2018). Diverse fuzzy c-means for image clustering. Pattern Recognition Letters, 1.
Zhang, Y., Wang, W., Zhang, X., & Li, Y. (2008). A cluster validity index for fuzzy clustering. Information Sciences, 178(4), 1205–1218. http://doi.org/10.1016/j.ins.2007.10.004
Downloads
|
How to Cite
Issue
Section
Muhammad Khoiruddin harahap
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
