IMPLEMENTASI DATA MINING MENGGUNAKAN METODE NAIVE BAYES DENGAN FEATURE SELECTION UNTUK PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU

  • Royan Habibie Sukarna Institut Teknologi Tangerang Selatan
  • Yulian Ansori Universitas Budi Luhur
DOI: https://doi.org/10.47080/saintek.v6i1.1467
Keywords: Data Mining, Graduation Prediction, Naïve Bayes

Abstract

The Education Efficiency Rate (AEE) is one of the parameters of the quality of the education program. The quality is measured based on 7 main standards, one of which is students and graduates. Meanwhile, to predict students' graduation rates accurately based on manually owned data set characteristics is very difficult. Data Mining by Naïve Bayes method was chosen to find patterns in analyzing and predicting timely graduation of students. As for the test will be done by comparing the initial dataset and dataset characteristics using the algorithm attribute selector Gain Ratio Attribute with the help of tools WEKA. The results showed that there was a difference to the accuracy of the results, and the larger ROC or AUC curves on the dataset characteristics using the selector attribute by using the Gain Ratio Attribute, although not very significant. And the result of this research yield 81% accuracy level with precision equal to 83.563% and recall 88.41%. The method used is included in Good Classification and will become the reference of the college management side, to address the problems that may arise in the decrease of the quality of education (e.g. decrease ratio of lecturers with students).

References

Ali Daud, Naif Radi Aljohani, Rabeeh Ayaz Abbasi, Miltiadis D. Lytras, Farhat Abbas, J. S. A. (2017). Predicting student performance using Advanced Learning Analytics. Pakistan Saudi Arab2018-01-11, C, 415–421. https://doi.org/10.1145/3041021.3054164
BADAN AKREDITASI NASIONAL PERGURUAN TINGGI. (2008). Akreditasi Program Studi Sarjana Buku VI Pedoman Penilaian Akreditasi Program Studi Sarjana (BAN-PT (ed.)). BAN-PT. https://banpt.or.id/download_instrumen
Bagus, I., Peling, A., Arnawan, I. N., Arthawan, I. P. A., & Janardana, I. G. N. (2017). Implementation of Data Mining To Predict Period of Students Study Using Naive Bayes Algorithm. International Journal of Engineering and Emerging Technology, 2(1), 53–57.
Bhardwaj, B. K., & Pal, S. (2011). Data Mining : A prediction for performance improvement using classification. International Journal of Computer Science and Information Security, 9(4), 136–140.
Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1–2), 245–271. https://doi.org/10.1016/S0004-3702(97)00063-5
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). Crisp-Dm 1.0. CRISP-DM Consortium, 76. https://doi.org/10.1109/ICETET.2008.239
K, A. A., & Aljahdali, S. (2013). Comparative Prediction Performance with Support Vector Machine and Random Forest Classification Techniques. International Journal of Computer Applications, 69(11), 12–16.
Moertini, V. S. (2002). Data Mining Sebagai Solusi Bisnis. Integral, 7(1), 44–56.
Ogunde, & Ajibade. (2014). A Data Mining System for Predicting University Students’ Graduation Grades Using ID3 Decision Tree Algorithm. Computer Science and Information Technology, 2(1), 21–46. http://www.ejer.com.tr/index.php?git=22&kategori=103&makale=925
Osmanbegović, E., & Suljić, M. (2012). Data mining approach for predicting student performance. Journal of Economics and Business, X(1), 3–12.
Tahyudin, I., Utami, E., & Amborowati, A. (2013). Comparing Clasification Algorithm Of Data Mining to Predict the Graduation Students on Time. Information Systems International Conference (ISICO), December, 2–4.
Tekin, A. (2014). Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach. Eurasian Journal of Educational Research, 14(54), 207–226. https://doi.org/10.14689/ejer.2014.54.12
Thakar, P., Mehta, A., & Manisha. (2015). Performance analysis and prediction in educational data mining: A research travelogue. International Journal of Computer Applications, 110(15), 60–68.
Wang, H. (2012). An Empirical Study on the Stability of Feature Selection for Imbalanced Software Engineering Data. International Journal of Advanced Computer Research, 2(3), 1–5. https://doi.org/10.1109/ICMLA.2012.60
Published
2022-02-14
How to Cite
Sukarna, R., & Ansori, Y. (2022). IMPLEMENTASI DATA MINING MENGGUNAKAN METODE NAIVE BAYES DENGAN FEATURE SELECTION UNTUK PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU. Jurnal Ilmiah Sains Dan Teknologi, 6(1), 50-61. https://doi.org/10.47080/saintek.v6i1.1467
Issue
Vol 6 No 1 (2022): Jurnal Ilmiah Sains dan Teknologi
Section
Articles

In order to be accepted and published by Journal Agribest, author(s) submitting the article manuscript should complete all the review stages. By submitting the manuscript the author(s) agreed to these following terms:

  1. The copyright of received articles shall be assigned to Journal Agribest as the publisher of the journal. The intended copyright includes the right to publish articles in various forms (including reprints). Journal Agribest maintain the publishing rights to the published articles.
  2. Authors are permitted to disseminate published article by sharing the link/DOI of the article at Journal Agribest. authors are allowed to use their articles for any legal purposes deemed necessary without written permission from Journal Agribest with an acknowledgement of initial publication to this journal.
  3. Users/public use of this website will be licensed to Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Creative Commons License