Selection of Specialization Class Using Support Vector Machine (SVM) Method in Sekolah Menengah Atas Negeri 1 Ambon

Stevanny Tamaela, Yopi Andry Lesnussa, Venn Yan Ishak Ilwaru

Abstract


The curriculum is a plan to form the abilities and character of children based on a standard. One of its form is the division of specialization classes at the high school level. The 2013 curriculum emphasizes that all students in Indonesia can practice their abilities based on their interests and talents, so students no longer choose majors but choose abilities (interests) in them specialize. This research uses the Support Vector Machine (SVM) method in specialization Decision Making System (DMS) at SMA Negeri 1 Ambon. By using the motivating acceptance factors and student selection as input data, this SVM method that processed with MATLAB Software produces a Classification of Interest Class with an accuracy rate more than 95%.

Keywords


Support Vector Machine

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References


M. Lestari, “Implementation of Citizenship Character Formation by the Study of Civic Education on Senior High School in The District of Bantul,” E-CIVICS, 2016.

Suyatmini, “Implementasi Kurikulum 2013 pada Pelaksanaan Pembelajaran Akuntansi di Sekolah Menengah Kejuruan,” J. Pendidik. Ilmu Sos., 2017.

Subandi, “Pengembangan Kurikulum 2013,” J. Pendidik. dan Pembelajaran Dasar, 2014.

B. L. Julien, L. Lexis, J. Schuijers, T. Samiric, and S. McDonald, “Using capstones to develop research skills and graduate capabilities: A case study from physiology,” J. Univ. Teach. Learn. Pract., 2012.

Direktorat Pembinaan SMA, Modul Pelatihan Implementasi Kurikulum 2013 SMA Tahun 2018. 2018.

R. O. Akbar and C. C. Cuyatno, “Pengaruh Motivasi Bimbingan Belajar Matematika Terhadap Prestasi Belajar Matematika Siswa Pada Pokok Bahasan Program Linier (Di Kelas Xii Science SMA Negeri 5 Cirebon),” Eduma Math. Educ. Learn. Teach., 2016.

A. S. Nugroho, A. B. Witarto, and D. Handoko, “Application of Support Vector Machine in Bioinformatics,” in Proceeding of Indonesian Scientific Meeting in Central Japan, 2003.

Y. Lin, H. Tseng, and C. Fuh, “Using Support Vector Machine,” Image Process., 2003.

Y. Wang, J. Wong, and A. Miner, “Anomaly intrusion detection using one class SVM,” in Proceedings fron the Fifth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC, 2004.

M. A. Oskoei and H. Hu, “Support vector machine-based classification scheme for myoelectric control applied to upper limb,” IEEE Trans. Biomed. Eng., 2008.

S. Vijayakumar and S. Wu, “Sequential Support Vector Classifiers and Regression,” in Proceedings of International Conference on Soft Computing (SOCO ‘99), 1999.

H. Amalia, A. F. Lestari, and A. Puspita, “Penerapan Metode Svm Berbasis Pso Untuk Penentuan Kebangkrutan Perusahaan,” None, 2017.




DOI: https://doi.org/10.18860/ca.v6i4.8882

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