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Submitted
October 21, 2020
Accepted
January 29, 2021
Published
February 2, 2021
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Abstract

data mining techniques in education sector have begun to evolve, along with the development of technology and the amount of data that can be stored in an education database storage system. One of them is a database of Bidikmisi scholarships in Indonesia. The Bidikmisi data used in this study will be classified using classification data mining technique. The technique that used in this study is random forest in combination with boosting algorithm and bagging algorithms. These algorithms also combine with SMOTE algorithm to handling the imbalance class in dataset. Based on the performance criteria G-mean and AUC, the algorithm combines with SMOTE tended to be better. The classification accuracy of each method being more than 90%

Keywords

Bidikmisi Bagging Boosting Classification Educational data mining

Article Details

Author Biographies

Sinta Septi Pangastuti, Statistic Department, Universitas Padjajaran

Statistics Department

Kartika Fithriasari, Statistic Department, Institut Teknologi Sepuluh Nopember

Statistics Department

Nur Iriawan, Statistic Department, Institut Teknologi Sepuluh Nopember

Statistics Department

Wahyuni Suryaningtyas, Mathematics Education, Muhammadiyah University of Surabaya

Mathematics Education
How to Cite
Pangastuti, S. S., Fithriasari, K., Iriawan, N., & Suryaningtyas, W. (2021). Data Mining Approach for Educational Decision Support. EKSAKTA: Journal of Sciences and Data Analysis, 2(1), 33–44. https://doi.org/10.20885/EKSAKTA.vol2.iss1.art5
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References

  1. Baker, Data mining for Education. In McGaw, B., Peterson, P., Baker, E., International Encyclopedia of Education 3nd Edition, vol.7, Elsevier, United Kingdom, 2010, pp. 112-118
  2. S.S. Pangastuti, K., Fithriasari, N. Iriawan, W. Suryaningtyas, Classification of Boosting in Embalanced Data. Malaysian Journal of Science, Vol. 8, Special Issue (2) (2019) 36-45.
  3. Directorate General of Learning and Student Affairs, K.R.T.d.P.T. (2018). Bidikmisi Guide 2018, Kemenristekdikti, Jakarta, 2018.
  4. W. Suryaningtyas, N. Iriawan, K. Fithriasari, B.S.S. Ulama, L. Susanto, A.A. Pravitasari, On the Bernoulli Micture model for Bidikmisi Scholarship Classfication with Bayesian MCMC. Journal of Physics: Conference Series, 1090 (2018) 1-8.
  5. R. E. Schapire, Y. Frued, Boosting: Foundations and Algorithms, MIT Press, London, 2018.
  6. L. Breiman, Bagging Predictors. Machine Learning, 1996, 123-140
  7. N.V. Chawla, K.W. Bowyer, L.O Hall, W.P. Kegelmeyer, SMOTE: Synthetic Minority Over-sampling technique. Journal of Artificial intelligence research, 16 (2002), 321-357.
  8. S. Wang, X. Yao, Diversity Analysis on Imbalances Data Sets by Using Ensemble Models. IEEE Symp. Comput. Intell. Data Mining (2009) 324-331.
  9. J. Han, M. Kamber, J. Pei, Data Mining Concepts and Technique 2nd Edition. Kaufman Publisher, USA, 2006.
  10. M. Kubat, S. Matwin, Addressing the curse of Imbalanced Training Sets: One-Sided Selection. Fourteenth International Conference on Machine Learning.Conference Proceedings, (1997) 179-186.