Fuzzy Decision Tree to Predict Student Success in Their Studies

Heri Bambang Santoso

Abstract


The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.


Keywords


fuzzy decision tree, fuzzy inference system, fuzzy ID3, ID3

Full Text:

PDF

References


Han J, Kamber M. (2006). Data Mining Concepts and Techniques Second Edition. San Fransisco: Morgan Kaufmann Publisher.

Vasani VP, Gawali RD. (2014). Classification and performance evaluation using data mining algorithms. International Journal of Innovative Research in Science, Engineering and Technology. 3(2): 10453-10458.

Adhatrao K, Gaykar A, Dhawan A, Jha R, Honrao V. (2013). Predicting students’ performance using ID3 and C4.5 classification algorithms. International Journal of Data Mining & Knowledge Management Process. 3(5): 39-52.

Yadav SK, Pal IS. (2012). Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification. World of Computer Science and Information Technology Journal. 2(2): 51-56.

Yadav SK, Bhardwaj B, Pal S. (2012). Data Mining Applications: A Comparative Study for Predicting Student’s performance. International Journal of Innovative Technology & Creative Engineering. 1(12): 13-19.

Mustafidah H, Aryanto D. (2012). Fuzzy Inference Systems to Predict Student Learning Achievement Based on the National Exam, a Test of Academic Potential, and Learning Motivation. JUITA. 2(1): 1-7.

Idri A, Elyassami S. (2011). Applying Fuzzy ID3 decision tree for software effort estimation. International Journal of Computer Science Issues. 8(4): 131-138.

Li Y, Jiang D, Li F. (2012). The Application of generating fuzzy ID3 algorithm in performance evaluation. Procedia Engineering. 29: 229-234.

Martin A, Balaji S, Venkatesan VP. (2012). Effective prediction of bankruptcy based on the qualitative factors using FID3 algorithm. International Journal of Computer Application. 43(21): 28-32.

Yun J, Seo JW, Yoon T. (2014). Fuzzy Decision Tree. International Journal of Fuzzy Logic System. 4(3): 7-11.

Liang G. (2005). A Comparative Study of Three Decision Tree algorithms: ID3, Fuzzy ID3 and Probabilistic Fuzzy ID3. Informatics & Economics Erasmus University Rotterdam. Rotterdam, the Netherlands.

Umano M, Okamoto H, Hatono I, Tamura H, Kawachi F, Umedzu S, Kinoshita J. (1994). Fuzzy Decision Trees by Fuzzy ID3 algorithm and Its Application to Diagnosis Systems. Proceedings of the third IEEE Conference on Fuzzy Systems. Orlando. (3): 2113-2118.

Quinlan JR. (1986). Induction of decision trees. Machine Learning. 1(1): 81-106.




DOI: https://doi.org/10.46336/ijqrm.v1i3.59

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 International Journal of Quantitative Research and Modeling

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Published By: 

IJQRM: Jalan Riung Ampuh No. 3, Riung Bandung, Kota Bandung 40295, Jawa Barat, Indonesia

 

IJQRM Indexed By: 

width= width= width= width= width= width=

 


Lisensi Creative Commons Creation is distributed below Lisensi Creative Commons Atribusi 4.0 Internasional.


View My Stats