Prediction of Student Graduation Time Using the Best Algorithm

research
  • 29 Jul
  • 2019

Prediction of Student Graduation Time Using the Best Algorithm

Data mining has a very important role in the world of education that
can help educational institutions in predicting and making decisions
related to students' academic status. In predicting student success in
the future, a model that can predict well is needed. Decission Tree
Model (DT) is a decision model that can predict clearly but has the
disadvantage of not being able to accommodate large data, neural
network (NN) is a very popular model because it uses non-linear data
and can hold large data while support vector machine (SVM) can
generalize from nonlinear to linear. Of the three methods, each has
weaknesses and strengths, we use the NN, SVM, and DT algorithms
to predict the graduation time of academic students in one of the
private universities in Indonesia. The results of this study indicate
that the three models produce accuracy of more than 80%, the DT
model has an accuracy of 84.96%, NN has an accuracy of 84.68%
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.

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REFERENSI


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.