Educational Data Mining (EDM) Prediction of Student Study Period with Naïve Bayes Classifier and C4.5 Algorithm Comparison

  • Galih STMIK Jabar, Indonesia
Keywords: Educational Data Mining (EDM), Naive Bayes Classifier, Decision Tree C4.5, Classification


Until now, many colleges are running to improve the quality of education to create a competitive environment. The wealth of data contained in the college can be put to good use according to the needs and processed into useful information to find out the relationship between the attributes of the data contained in it for analysis and the expected result in the form study achievements are related to study time, i.e. in adequate or late in the probable study period can be classified. Data mining, which refers to the analysis of data in the field of educational institutions, is also known as educational data mining (EDM). In the study conducted using two models of Naive Bayes Classifier i.e. Algorithms and C 4.5. The value of best accuracy in the Naive Bayes Classifier (NBC) algorithm model was 86.83% with a ratio of 80% training data, while in the model algorithm C 4.5 was 88.10% with a ratio of 90% training data. The application of EDM is expected to be maximized and developed so that it can contribute to the world of education and advance, especially in the field of data mining.


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M. M. A. Tair and A. M. El-halees., 2012. “Mining Educational Data t o Improve Students ’ Performance : A Case Study,” vol. 2, no. 2, pp. 140–146.

Larose, D. T., 2006. ”Data Mining Methods and Models”. Hoboken, New Jersey, United State of America: John Wiley & Sons, Inc.

Romero, C., 2010. Educational Data Mining : A Review of the State of the Art. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, 40(6), 601–618.

Abeer Badr, Ibrahim Sayed., 2014. Data Mining: A predictionfor student’s Performance Using Clasification Method. World Journal of Computer Application and Technology 2(2): 43-47.

Tismy Devasia, Vinushree T P , Vinayak Hegde., 2016. Prediction of Students Performance using Educational Data Mining. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) pp.1-5

Shakeel Khawar & Butt Anwer Naveed., 2016. Educational Data Mining to Reduce Student Dropout Rate by Using Classification. Conference: Conference: 253rd OMICS International Conference on Big Data Analysis & Data Mining, At Lexington, Kentucky, USA, Volume: 8

Makhtar Mokhairi, Nawang Hasnah, Shamsuddin S N W., 2017. Analysis on Students Performance Using Naive Bayes Classifier. Journal of Theoritical and Applied Information Technology (JATIT).

Ihsan A Abu Amra dan Ashraf Y.A. Maghari.,2017. Students Performance Prediction Using KNN and Naïve Bayesian.

Olson, D., Shi, Y.,2007. Introduction to Business Data Mining, McGraw-Hill, New York.

Yu, L. et al.,2007. Application and Comparison of Classification Techniques in Controlling Credit Risk. In P.M. Pardalos, ed. Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. Singapore: World Scientific. Ch. 2.

Yu, H., Huang, X., Hu, X. & Cai, H., A., 2010. Comparative Study on Data Mining Algorithms for Individual Credit Risk Evaluation. In 2010 International Conference on Management of e-Commerce and e-Government. China.

Gorunescu, F.,2011. Data Mining Concept Model and Techniques. Berlin: Springer. ISBN 978-3-642-19720-8.

Kusrini and E. T. Luthfi., 2009. Algoritma Data Mining. Yogyakarta: Andi Offset.

Larose, Daniel. T., 2005. “Discovering Knowledge in Data: An Introduction to Data Mining”. John Willey & Sons. Inc.

BANPT., 2007. Buku I Naskah Akademik Akreditasi Instistusi Perguruan Tinggi.

How to Cite
Galih. (2022). Educational Data Mining (EDM) Prediction of Student Study Period with Naïve Bayes Classifier and C4.5 Algorithm Comparison. Journal of Systems Engineering and Information Technology (JOSEIT), 1(2), 58-61.