Sistem Peringatan Dini Keterlambatan Masa Studi Mahasiswa Menggunakan Metode Support Vector Machine
Article Sidebar
-
masa studi, peringatan dini, prediksi, support vector machine, universitas lambung mangkurat
Abstract
Students graduating late from college are a common problem in universities. The study of students at universities is generally designed to be completed in 3.5 to 4 years. If a student has not graduated past that time, he is considered late in completing his education. Lambung Mangkurat University, as the oldest university in Kalimantan, also experienced these problems. Therefore, an early warning system was build to predict students' possibility of being late in completing their studies. This study uses a sample of students from the Faculty of Engineering, the University of Lambung Mangkurat, to predict students who will be late graduating from Lambung Mangkurat University since semester 5. This system was to develop using a model built using the Support Vector Machine (SVM) method. Model training conducted using 755 data from Lambung Mangkurat University Faculty of Engineering students from 2010 to 2014. Then, the performance of the model tested using 234 student data from 2015 and 2016. The parameters used were the number of credits, gender, GPA on semester 1 to 4, and study programs. The test results show that the model has good performance to predict students who will be late in completing their studies with 88.2% accuracy.
References
[2] V. Tampakas, I. E. Livieris, E. Pintelas, N. Karacapilidis, and P. Pintelas, “Prediction of students’ graduation time using a two-level classification algorithm,†in International Conference on Technology and Innovation in Learning, Teaching and Education, 2018, pp. 553–565.
[3] M. S. Nurafifah, S. Abdul-Rahman, S. Mutalib, N. H. A. Hamid, and A. M. Ab Malik, “Review on predicting students’ graduation time using machine learning algorithms,†Int. J. Mod. Educ. Comput. Sci., vol. 11, no. 7, p. 1, 2019.
[4] Z. Wang et al., “Design and Implementation of Early Warning System Based on Educational Big Data,†in 2018 5th International Conference on Systems and Informatics, ICSAI 2018, 2019, pp. 549–553.
[5] A. R. Rozzaqi, “Naive Bayes dan Filtering Feature Selection Information Gain untuk Prediksi Ketepatan Kelulusan Mahasiswa,†J. Inform. UPGRIS, vol. 1, no. 1 Juni, 2015.
[6] D. Kartini, R. A. Nugroho, M. R. Faisal, and U. L. Mangkurat, “Klasifikasi Kelulusan Mahasiswa Menggunakan Algoritma Learning Vector Quantization,†vol. 3, no. 2, pp. 93–98, 2017.
[7] A. Fadli, M. I. Zulfa, and Y. Ramadhani, “Perbandingan Unjuk Kerja Algoritme Klasifikasi Data Mining dalam Sistem Peringatan Dini Ketepatan Waktu Studi Mahasiswa,†J. Teknol. dan Sist. Komput., vol. 6, no. 4, pp. 158–163, 2018.
[8] T. Ojha, G. L. Heileman, M. Martinez-Ramon, and A. Slim, “Prediction of graduation delay based on student performance,†in 2017 International Joint Conference on Neural Networks (IJCNN), 2017, pp. 3454–3460.
[9] B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,†in Proceedings of the fifth annual workshop on Computational learning theory - COLT ’92, 1992, pp. 144–152.
[10] C. Cortes and V. Vapnik, “Support-vector networks,†Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
[11] H. Faris, M. A. Hassonah, A.-Z. Ala’M, S. Mirjalili, and I. Aljarah, “A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture,†Neural Comput. Appl., vol. 30, no. 8, pp. 2355–2369, 2018.
[12] S. Suthaharan, “Support vector machine,†in Machine learning models and algorithms for big data classification, Springer, 2016, pp. 207–235.
[13] I. Ahmad, M. Basheri, M. J. Iqbal, and A. Rahim, “Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection,†IEEE access, vol. 6, pp. 33789–33795, 2018.
[14] A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support Vector Machine,†Power Syst., vol. 28, pp. 161–226, 2007.
[15] S. Vijayakumar and S. Wu, “Sequential support vector classifiers and regression,†Margin, vol. 1, no. h2, p. h2, 1999.
[16] J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
[17] I. Kelman and M. H. Glantz, “Early Warning Systems Defined BT - Reducing Disaster: Early Warning Systems For Climate Change,†A. Singh and Z. Zommers, Eds. Dordrecht: Springer Netherlands, 2014, pp. 89–108.
Authors
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.