DESIGN OF KIP KULIAH SELECTION SYSTEM AND RECIPIENT DETERMINATION USING SUPPORT VECTOR MACHINE (SVM)
KIP Kuliah is tuition assistance from the government for high school graduates or equivalent with good academic potential but has economic limitations. In recent years it has been seen that the Indonesian government has always tried to increase the quota for KIP Kuliah recipients. In this study, the Support Vector Machine (SVM) method was applied to create a system for selecting and determining KIP Kuliah recipients. To obtain the best model to be used in the system, the training and testing data are divided into three data distribution schemes, namely 60/40, 70/30, and 80/20. After the training and testing process was carried out using the SVM method with various parameter variations, then the best accuracy rate of 94.59% is obtained in the 80/20 data sharing scheme for the nonlinear SVM model with the RBF kernel. With this system, it is hoped that the KIP Kuliah selection process at the tertiary level can run effectively, efficiently and the results of the determination are more targeted.
K. R. d. T. Pusat Layanan Pembiayaan Pendidikan Kementerian Pendidikan, "kip-kuliah.kemdikbud.go.id," 2 Februari 2023. [Online]. Available: https://kip-kuliah.kemdikbud.go.id/uploads/Pedoman-Pendaftaran-KIP-Kuliah-2023-update-15Mei2023-_e79a89.pdf. [Accessed 10 Januari 2023].
D. K. D. B. Kemenristekdikti, "kemdikbud.go.id," 14 Juni 2019. [Online]. Available: https://lldikti13.kemdikbud.go.id/2019/06/14/petunjuk-teknis-pengelolaan-bidikmisi-2019/. [Accessed 12 April 2023].
N. N. Rahmawati, M. I. A. Fathoni and Ismanto, "Penentuan Penerima KIP Kuliah Mahasiswa S1 Unugiri Menggunakan Fuzzy C-Means Clustering," Transformasi : Jurnal Pendidikan Matematika dan Matematika, vol. 6, no. 2, pp. 121-130, 2022.
R. Susetyoko, W. Yuwono and E. Purwantini, "Model Klasifikasi Pada Seleksi Mahasiswa Baru Penerima KIP Kuliah Menggunakan Regresi Logistik Biner," Jurnal Informatika Polinema, vol. 8, no. 4, pp. 31-40, 2022.
H. Nopriandi, A. Aprizal and S. Chairani, "Sistem Pendukung Keputusan Seleksi Calon Penerima Beasiswa Kartu Indonesia Pintar Kuliah (Kip-K) Di Universitas Islam Kuantan Singingi," JURNAL TEKNOLOGI DAN OPEN SOURCE, vol. 6, no. 1, pp. 41-54, 2022.
P. A. Kurniawijaya and I. W. W. Karsana, "Implementasi Metode AHP Dalam Sistem Penunjang Keputusan Penerima KIP Kuliah," JUKI : Jurnal Komputer Dan Informatika, vol. 5, no. 1, p. 22–31, 2023.
B. P. Tomasouw and M. I. Irawan, "Multiclass Twin Bounded Support Vector Machine Untuk Pengenalan Ucapan," in Seminar Nasional Penelitian, Pendidikan dan Penerapan MIPA, Yoygakarta, 2012.
Z. A. Leleury and B. P. Tomasouw, "Diagnosa Penyakit Saluran Pernapasan Dengan Menggunakan Support Vector Machine (SVM)," BAREKENG: J. Math. & App., vol. 9, no. 2, pp. 109-119, 2015.
R. Damasela, B. P. Tomasouw and Z. A. Leleury, "Penerapan Metode Support Vector Machine (SVM) Untuk Mendeteksi Penyalahgunaan Narkoba," PARAMETER: Jurnal Matematika, Statistika dan Terapannya, vol. 1, no. 2, pp. -, 2021.
I. Hmeidi, B. Hawashin and E. El-Qawasmeh, "Performance of KNN and SVM classifiers on full word Arabic articles," Advanced Engineering Informatics, vol. 22, no. 1, pp. 106-111, 2008.
Y. Qiu, "Towards Prediction of Pancreatic Cancer Using SVM Study Model," JSM Clinical Oncology And Research, vol. 2, no. 4, pp. 1-6, 2014.
I. T. Utami, "Perbandingan Kinerja Klasifikasi Support Vector Machine (SVM) Dan Regresi Logistik Biner dalam Mengklasifikasikan Ketepatan Waktu Kelulusan Mahasiswa FMIPA UNTAD," Jurnal Ilmiah Matematika dan Terapan, vol. 15, no. 2, pp. 256-267, 2018.
R. Wahyudi and G. Kusumawardhana, "Analisis Sentimen pada review Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine," JURNAL INFORMATIKA, vol. 8, no. 2, pp. 200-207, 2021.
M. T. Anjasmoros, Istiadi and F. Marisa, "Analisis Sentimen Aplikasi Go-Jek Menggunakan Metode SVM Dan NBC (Studi Kasus: Komentar Pada Play Store)," in The 3rd Conference on Innovation and Application of Science and Technology (CIASTECH 2020), Malang, 2020.
S. Dong, "Multi class SVM algorithm with active learning for network traffic classification," Journal Expert Systems with Applications, vol. 176, no. -, pp. -, 2021.
M. Onel , C. A. Chris A. Kieslich and E. N. Pistikopoulo, "A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process," AIChE Journal, vol. 65, no. 3, p. 992–1005, 2018.
S. Abe, Support Vector Machines for Pattern Classification, 2 ed., London: Springer-Verlag, 2010.
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