Predicting and Analyzing the Length of Study-Time Case Study: Computer Science Students

Authors

  • Teny Handhayani Universitas Tarumanagara
  • Lely Hiryanto Universitas Tarumanagara

DOI:

https://doi.org/10.21512/comtech.v8i2.3756

Keywords:

Support Vector Machine, Mutual Information, length of study-time

Abstract

The length of study-time is one of the important issues in higher education. The goal of this research was to predict and analyze the length of studytime in the early stage of Computer Science students in X University. The research proposed Mutual Information (MI) as feature selection method and Support Vector Machine (SVM) as a classification method. There were two different sections of the experiments. The first experiment used two class targets that were grouped in ‘on time group’ and ‘late group’. The experiment result shows that the proposed method produces accuracy around 85%. The second experiment used three class targets, ‘on time group’, ‘late group’, and ‘very late group’. The experiment result of the proposed method produces accuracy around 80%. Mutual Information (MI) does not only successfully raise the accuracy but also uncovers the relationship between subjects and the class targets.

Dimensions

Plum Analytics

Author Biographies

Teny Handhayani, Universitas Tarumanagara

Department of Computer Science

Lely Hiryanto, Universitas Tarumanagara

Department of Computer Science

References

Alzubaidi, A., Cosma, G., Brown, D., & Pockley, A. G. (2016). Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information. In 2016 International Conference on Interactive Technologies and Games

(ITAG) (pp. 70-76). IEEE.

Bo, G., Rui, Z., Guang, X., Chuangming, S., & Li, Y. (2015).

Predicting students performance in educational data mining. In 2015 International Symposium on Educational Technology (ISET) (pp. 125-128). IEEE.

Cristianini, N., & Taylor, J. (2000). An introduction to Support Vector Machines and other kernel-based learning methods. New York: Cambridge University Press.

Deepak, E., Pooja, G. S., Jyothi, R. N., Kumar, S. V., & Kishore, K. V. (2016). SVM kernel based predictive analytics on faculty performance evaluation. In 2016 International Conference on Inventive Computation Technologies (ICICT) (pp. 1-4). IEEE.

Dirjen Belmawa. (2016). Direktorat jenderal pembelajaran dan kemahasiswaaan Kemristekdikti. Retrieved February 22nd, 2017, from http://belmawa.ristekdikti.go.id/2016/03/04/kemristekdikti-sosialisasikanpermen-nomor-44-tahun-2015-tentang-sn-dikti/

Gad, W., & Rady, S. (2015). Email filtering based on supervised learning and mutual information feature selection. In 2015 Tenth International Conference on Computer Engineering & Systems (ICCES) (pp. 147-152). IEEE.

Harwati, Alfiani, A. P., & Wulandari, F. A. (2014). Mapping student’s performance based on data mining approach. In The 2014 International Conference on Agro-industry (ICoA): Competitive and Sustainable Agroindustry for Human Welfare (pp. 173-177). ELSEVIER.

Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415-425.

Li, Y., Ma, X., & Yang, M. (2015). Improved feature selection

based on normalized mutual information. In 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) (pp. 518-522). IEEE.

Liu, W. X., & Cheng, C. H. (2016). A hybrid method based on MLFS approach to analyze students’ academic achievement. In 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (pp. 1625-1630). IEEE.

Liu, Y., & Zheng, Y. F. (2005). One-against-all multi-class SVM classification using reliability measures. In International Joint Conference on Neural Networks (pp. 849-854). Montreal: IEEE.

Liu, Y., Wang, Rui, & Zheng, Y. S. (2007). An improvement

of one-against-one method for multi-class Support Vector Machine. In Sixth International Conference on Machine Learning and Cybernetics (pp. 2915-2920). Hongkong: IEEE.

Mouri, K., Okubo, F., Shimada, A., & Ogata, H. (2016). Bayesian network for predicting students’ final grade using e-book logs in university education. In 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT) (pp. 85-89). IEEE.

Ogunde, A. O., & Ajibade, D. A. (2014). A data mining system for predicting university students’ graduation grades using ID3 decision tree algorithm. Journal of Computer Science and Information Technology, 2(1), 21-46.

Piad, K. C., Dumlao, M., Ballera, M. A., & Ambat, S. C. (2016). Predicting IT employability using data mining techniques. In 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) (pp. 26-30). IEEE.

Scikit-Learn. (2016). Scikit-Learn. Retrieved December 10th, 2016 from http://scikit-learn.org/stable/

Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A review on predicting student’s performance using data mining techniques. In The Third Information Systems International Conference (pp. 414–422). Procedia Computer Science.

Smith, R. (2015). A mutual information approach to calculating nonlinearity. Stat, 4(1), 291-303.

Suykens, J. A., Gestel, T. V., Brabanter, J. D., Moor, B. D., & Vandewalle, J. (2002). Least Square Support Vector Machines. London: World Scientific

Taruna, S., & Pandey, M. (2014). An empirical analysis of

classification techniques for predicting academic performance. In 2014 IEEE International Advance Computing Conference (IACC) (pp. 523-528). IEEE.

Ukpong, D. E., & George, I. N. (2013). Length of studytime

behaviour and academic achievement in social studies education students in the University of Uyo. International Education Studies, 6(3), 172.

Zhang, X., Zhao, X. M., He, K., Lu, L., Cao, Y., Liu, J., ... &

Chen, L. (2012). Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics, 28(1), 98-104.

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2017-06-30

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