EDUCATIONAL DATA MINING FOR STUDENT ACADEMIC PREDICTION USING K-MEANS CLUSTERING AND NAÏVE BAYES CLASSIFIER

  • Dewi Ayu Nur Wulandari (1*) Universitas Bina Sarana Informatika
  • Riski Annisa (2) Universitas Bina Sarana Informatika
  • Lestari Yusuf (3) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Titin Prihatin (4) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

  • (*) Corresponding Author
Keywords: Student Academic Prediction, K-Means, Data Mining, Naive Bayes

Abstract

This study proposes the merging of the K-Means clustering data mining method and the Naïve Bayes classifier (K-Means Bayes) for better results in data processing for Student Academic Performance data. Data was taken from the Student Academic Performance dataset which is used as a test case. The amount of data used in this study were 131 data and 21 attributes. The accuracy of the results obtained from the combination of the proposed method is 97.44%. The results obtained when compared with calculations using the K-Means method and calculations using the Naïve Bayes method, the proposed method (K-Means Bayes) gives better results. Although the initial centroid determination on the K-Means method is done randomly, the impact can be reduced by adding the Naive Bayes classifier method which results in a better accuracy value, thereby increasing the accuracy of the method used. Compared to the K-Means and Naïve Bayes methods, the proposed method increases the accuracy of about 27% of the Naïve Bayes algorithm and about 23% of the K-Means algorithm. With the results obtained, it can be concluded that the proposed method can improve predictions of student academic performance data. The initial centroid determination for grouping in the K-Means method can affect the quality of the accuracy of the data produced

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Author Biographies

Dewi Ayu Nur Wulandari, Universitas Bina Sarana Informatika

Sistem Informasi Kampus Kota Bogor

Riski Annisa, Universitas Bina Sarana Informatika

Sistem Informasi Kampus Kota Pontianak2

Lestari Yusuf, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

Sistem Informasi

Titin Prihatin, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

Teknik Informatika

References

Adekitan, A. I., & Salau, O. (2019). The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250. https://doi.org/10.1016/j.heliyon.2019.e01250

Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers and Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007

Bansode, J. (2016). Mining Educational Data to Predict Student‘s Academic Performance. January, 1–5.

Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. Van. (2019). Educational data mining: Predictive analysis of the academic performance of public school students in the capital of Brazil. Journal of Business Research, 94(August 2017), 335–343. https://doi.org/10.1016/j.jbusres.2018.02.012

Hamsa, H., Indiradevi, S., & Kizhakkethottam, J. J. (2016). Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm. Procedia Technology, 25, 326–332. https://doi.org/10.1016/j.protcy.2016.08.114

Handayani, F., & Pribadi, S. (2015). Implementasi Algoritma Naive Bayes Classifier dalam Pengklasifikasian Teks Otomatis Pengaduan dan Pelaporan Masyarakat melalui Layanan Call Center 110. Jurnal Teknik Elektro, 7(1), 19–24.

Hussain, S., Dahan, N. A., Ba-alwi, F. M., & Ribata, N. (2018). Educational Data Mining and Analysis of Students ’ Academic Performance Using WEKA. 9(2), 447–459. https://doi.org/10.11591/ijeecs.v9.i2.pp447-459

Miguéis, V. L., Freitas, A., Garcia, P. J. V., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modeling approach. Decision Support Systems, 115(July 2017), 36–51. https://doi.org/10.1016/j.dss.2018.09.001

Muda, Z., Yassin, W., Sulaiman, M. N., & Udzir, N. I. (2011). Intrusion detection based on K-Means clustering and Naïve Bayes classification. 7th International Conference on Information Technology in Asia.

Risnawati. (2018). Analisis Kelulusan Mahasiswa Menggunakan Algoritma C.45. Jurnal Mantik Penusa, 2(1), 71–76.

Satrianansyah, S., & Wulandari, C. (2019). Penerapan Algoritma Naive Bayes Untuk Memprediksi Kelulusan Ujian Siswa Berbasis Web Pada Smk. Seminar Nasional AVoER XI 2019, 23–24.

Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157

Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting the academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104(October 2019), 106189. https://doi.org/10.1016/j.chb.2019.106189

Widaningsih, S. (2019). Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm. Jurnal Tekno Insentif, 13(1), 16–25. https://doi.org/10.36787/jti.v13i1.78

Wulandari, D. A. N., Annisa, R., & Yusuf, L. (2020). An Educational Data Mining For Student Academic Prediction Using K-Means Clustering And Naïve Bayes Classifier.

Published
2020-09-08
How to Cite
Wulandari, D. A., Annisa, R., Yusuf, L., & Prihatin, T. (2020). EDUCATIONAL DATA MINING FOR STUDENT ACADEMIC PREDICTION USING K-MEANS CLUSTERING AND NAÏVE BAYES CLASSIFIER. Pilar Nusa Mandiri : Journal of Computing and Information System, 16(2), 155-160. https://doi.org/10.33480/pilar.v16i2.1432
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