APPLICATION OF DECISION TREE AND NAIVE BAYES ON STUDENT PERFORMANCE DATASET

  • Hilda amalia (1*) Universitas Bina Sarana Informatika
  • Ari Puspita (2) Universitas Bina Sarana Informatika
  • Ade Fitria Lestari (3) Universitas Bina Sarana Informatika
  • Frieyadie Frieyadie (4) Universitas Nusa Mandiri https://orcid.org/0000-0002-8282-0672

    • (*) Corresponding Author
DOI: https://doi.org/10.33480/pilar.v18i1.2714

Keywords: student performance, decision tree, naïve bayes

Abstract

Student performance is the ability of students to deal with the entire academic series taken during school. Student performance produces two labels, namely successful and unsuccessful students. Successful students can graduate with excellent, excellent, and suitable performance labels. At the same time, students who have a label on average are students who get poor performance. Measurement of student performance is needed for every educational institution to take strategic steps to improve student performance. This study aimed to obtain a data mining method that worked well on student performance datasets. In this study, student performance datasets were processed, which had 11 indicators with one result label. Student performance datasets are processed using data mining methods, namely decision tree and nave Bayes, while the tool used for dataset processing is WEKA. The research results from processing student performance datasets obtained that the accuracy value for the decision tree method was 94.3132%, and the accuracy produced by the naive Bayes method was 84.8052%.

Downloads

Download data is not yet available.

References

Amalia, H. (2014). Prediction of Student Graduation Using Algoritma C4.5. International Seminar on Scientific Issues and Trens (ISSIT), A31-A36.

Amalia, H., Puspita, A., & Lestari, A. F. (2021). Research Report.

Amalia, H., Yunita, Puspita, A., & Lestari, A. F. (2020). Student Performance Analysis Using Algoritma C4.5 Optimize Selection. Pilar Nusa Mandiri, 10-18.

Asraf, A., Anwer, S., & Khan, M. (2018). A Comparative study of Predicting Student’s Performance of Data Mining techniques. American Scientist Research Journal for Engineering, Technology, and Sciences (ASRJETS), 44(1), 122-136.

Brew, E. A., Nketiah, B., & Korentang, R. (2021). A Literature Review of Academic Performance, an Insight into Factors and Their Influences on Academic Outcomes of Students at Senior High School. OALIB, 1-14.

Fajar, S., Hussain, M., Sarwar, H., Afzal, M., & Gilani, S. A. (2019). Factors Affecting Academic Performance of Undergraduate Nursing Students. International Journal of Social of Undergraduate Nursing Students, 7-16.

Hussain, S., Dahan, N. A., Ba-Alwi, F. M., & Ribata, N. (2018). Educational Data Mining and Analysis of Student’s Academic Performance Using WEKA. Indonesia Journal of Electrical Engineering and Computer Science, 447-459.

Kavipriya, P. (2016). A Review on Predicting Students’ Academic Performance Earlier, Using Data Mining Techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 101-105.

Khasanah, A. U., & Harwati. (2017). A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques. Material Science and Engineering (MOIME), 1-7.

Lynn, N. D., & Emanuel, A. W. (2021). Using Data Mining Techniques to Predict Students’ Performance. a Review. Materials Science and Engineering , 1-9.

Lynn, N., & Emanuel, A. W. (2020). Using Data Mining Techniques to Predict Students’ Performance. a Review. Confeerrence Series: Materials Science and Engineering, 1-9.

Masangu, L., Jadhav, A., & Ajoodha, R. (2021). Predicting Student Academic Performance Using Data Mining Techniques. Advances in Science, Technology and Engineering System Journal (ASTES) , 153-163.

Olufemi, O. T., Adederin, A. A., & Oyediran, W. O. (2018). Factors Affecting Students’ Academic Performance in Collages of Education in Southwest Nigeria. British Journal of Education, 43-56.

Patil, P. (2015). A STUDY OF STUDENT’S ACADEMIC PERFORMANCE USING DATA MINING TECHNIQUES. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS, 59-63.

Sumitha, R., & Vinothkumar, E. S. (2016). Prediction of Students Outcome Using Data Mining Techniques. International Journal of Scientific Engineering and Applied Science (IJSEAS), 132-139.

Walia, N., & Kumar, M. (2020). Students’ Academic Performance Prediction in Academic Using Data Mining. International Conference on Intelligent Communication and Computational Research (ICICCR-2020), 1-5.

Walia, N., Kumar, M., Nayar, N., & Metha, G. (2020). Student’s Academic Performance Prediction in Academic using Data Mining Techniques. Inernational Conference in Intelligent Prediction in Academic Using Data Mining Techniques, 1-5.

Yaacob, W. F., Nasir, S. A., Yaacob, W. W., & Sobri, N. M. (2019). Supervised data mining approach for predicting student performance. Indonesian Journal of Electrical Engineering and Computer Science, 1584-1592.

Published
2022-03-09
How to Cite
amalia, H., Puspita, A., Lestari, A., & Frieyadie, F. (2022). APPLICATION OF DECISION TREE AND NAIVE BAYES ON STUDENT PERFORMANCE DATASET. Jurnal Pilar Nusa Mandiri, 18(1), 53-58. https://doi.org/10.33480/pilar.v18i1.2714
Article Metrics

Abstract viewed = 546 times
PDF downloaded = 368 times
Issue
Vol 18 No 1 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for March 2022
Section
Articles

Copyright (c) 2022 Hilda amalia, Ari Puspita, Ade Fitria Lestari, Frieyadie Frieyadie

Creative Commons License

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

An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:

  1. Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
  2. Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
  3. Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
    Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.

Most read articles by the same author(s)