Prediction of Student Graduation Time Using the Best Algorithm

Verry Riyanto, Abdul Hamid, Ridwansyah Ridwansyah

Abstract


Data mining has a very important role in the world of education can help education institutions in predicting and making decisions related to student academic status. We use the NN, SVM and DT algorithms to predict the graduation time of academic students at one of the private universities in Indonesia. The results of this study indicate that the three models produce the accuracy of more than 80%, and the SVM model has an accuracy of 85.18% higher than the other two models. The results arising from this study provide important reference material for planning the future success of students and faculty in early warning to students in the future.

References


J. K. Choi, C. Il Yoo, K. A. Kim, Y. Won, and J. J. Kim, “Study on datamining techinique for foot disease prediction,” in International Conference on IT Convergence and Security, ICITCS 2014, 2014.

S. Moro and R. M. S. Laureano, “Using Data Mining for Bank Direct Marketing: An application of the CRISP-DM methodology,” Eur. Simul. Model. Conf., pp. 117–121, 2011.

S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,” Decis. Support Syst., vol. 62, pp. 22–31, 2014.

Ridwansyah and E. Purwaningsih, “Particle Swarm Optimization Untuk Meningkatkan Akurasi Prediksi Pemasaran Bank,” J. PILAR Nusa Mandiri, vol. 14, no. 1, pp. 83–88, 2018.

M. Sugimoto, M. Takada, and M. Toi, “Comparison of robustness against missing values of alternative decision tree and multiple logistic regression for predicting clinical data in primary breast cancer,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, pp. 3054–3057.

J. L. Tang, Q. R. Cai, and Y. J. Liu, “Prediction of material mechanical properties with Support Vector Machine,” in International Conference on Machine Vision and Human-Machine Interface, 2010, vol. 1, pp. 592–595.

Freitas F. A. and L. L. J., “Maslow’s hierarchy of needs and student academic success,” Teach. Learn. Nurs., vol. 6, no. 1, pp. 9–13, 2011.

M. V. Ashok and A. Apoorva, “Data mining approach for predicting student and institution’s placement percentage,” in 2016 International Conference on Computation System and Information Technology for Sustainable Solutions, CSITSS 2016, 2016, pp. 336–340.

M. T. Devasia, M. V. T. P, and M. V. Hegde, “Prediction of Students Performance using Educational Data Mining,” Int. J. Cogn. Ther., vol. 1, no. 3, pp. 266–279, 2008.


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