Implementation of Data Mining to Predict Graduation of SMK Al Huda Kedungwungu Students Using the Naïve Bayes Classifier Algorithm

Authors

  • Odi Nurdiawan STMIK IKMI Cirebon

DOI:

https://doi.org/10.58330/ese.v1i4.202

Keywords:

Probability, Naive Bayes, Accuracy

Abstract

The purpose of prediction is to become decision makers and make policies. Understanding the uncertainties and risks that may arise can be considered when making plans. By making these predictions, planners and decision makers will be able to consider other alternatives, so they can take advantage of student graduation data. The algorithm that will be used is the Naive Bayes Classifier Algorithm which is a simple probability classification method based on the application of Bayes' theorem with the assumption that explanatory variables are independent, clues and supporting data in predicting student graduation, namely student behavior, school exams, grades. In practice, the application of the Naive Bayes method applies data train to produce the probability of each criterion for different classes, so that the probability value of these criteria can be optimized to determine predictions of student graduation quickly and efficiently based on the classification carried out using the Naive Bayes method, then from the results of testing with the Naive Bayes method the results obtained an accuracy value of 76 .25%, so this result has very good accuracy. That way this method can be applied in predicting student graduation.

References

A. Yudi Permana, D. N. M. (2019). SIGMA - Jurnal Teknologi Pelita Bangsa SIGMA - Jurnal Teknologi Pelita Bangsa. SIGMA - Jurnal Teknologi Pelita Bangsa 167, 10(September), 167–172.

A, P. T., Rityarna, B. S., & Arifianto, D. (2015). Memprediksi Hasil Kelulusan Siswa Menggunakan Metode Naive Bayes. 1110651248, 0–2.

Amalia, R. (2020). Penerapan Data Mining Untuk Memprediksi Hasil Kelulusan Siswa menggunakan Metode Naïve Bayes. Jurnal Informatika Dan Sistem Informasi, 6(1), 33–42.

Anugrah Putra, D., & Kamayani, M. (2020). Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Naive Bayes di Program Studi Teknik Informatika UHAMKA. Prosiding Seminar Nasional Teknoka, 5(2502), 34–40. https://doi.org/10.22236/teknoka.v5i.331

Fadrial, Y. E. (2021). Algoritma Naive Bayes Untuk Mencari Perkiraan Waktu Studi Mahasiswa. INTECOMS: Journal of Information Technology and Computer Science, 4(1), 20–29. https://doi.org/10.31539/intecoms.v4i1.2219

Munawir, M., & Iqbal, T. (2019). Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh). Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 3(2), 59. https://doi.org/10.35870/jtik.v3i2.77

Nurul, C., & Ari, W. I. (2018). Implementasi Data Mining Untuk Clustering Daerah Penyebaran Penyakit Demam Berdarah Di Kota Tangerang Selatan Menggunakan Algoritma K-Means. Jurnal Satya Informatika, 3(1), 12–24.

Setiyani, L., Wahidin, M., Awaludin, D., & Purwani, S. (2020). Analisis Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Data Mining Naïve Bayes : Systematic Review. Faktor Exacta, 13(1), 35. https://doi.org/10.30998/faktorexacta.v13i1.5548

Syarli, S., & Muin, A. (2016). Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi). Jurnal Ilmiah Ilmu Komputer, 2(1), 22–26.

Waru, D., Astuti, R. W., & Kahar, N. (2021). Penerapan Data Mining Untuk Memprediksi Daya Serap Lulusan Siswa Menggunakan Algoritma Native Bayes. Arcitech: Journal of Computer Science and Artificial Intelligence, 1(1), 57. https://doi.org/10.29240/arcitech.v1i1.3294

Downloads

Published

2023-04-28

Issue

Vol. 2 No. 2 (2023): December

Section

Articles

License

Copyright (c) 2023 Putriyana Putriyana, Odi Nurdiawan

Creative Commons License

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

Authors who choose to publish their work with Experimental Student Experiences agree to the following terms:

  1. Copyright Retention: Authors retain the copyright to their work and acknowledge that Experimental Student Experiences (ESE) is the first publisher. The work is licensed under a Creative Commons Attribution-ShareAlike License (CC BY-SA). This license allows others to distribute, remix, adapt, and build upon the work, even for commercial purposes, as long as proper attribution is given to the original source and any derived works are shared under the same license.

  2. Additional Distribution: Authors have the freedom to enter into separate contractual arrangements for the non-exclusive distribution of the published version of their work by ESE. This includes the ability to post the work in institutional repositories, publish it in books, or distribute it through other channels. In such cases, authors should provide appropriate acknowledgment of the initial publication in ESE.

  3. Online Posting and Preprints: Authors are permitted and strongly encouraged to share their work online before and during the submission process. This includes posting the work in institutional repositories, personal websites, or preprint servers. Such practices can foster productive scholarly exchanges and lead to increased visibility and citation of the published work.

How to Cite

Implementation of Data Mining to Predict Graduation of SMK Al Huda Kedungwungu Students Using the Naïve Bayes Classifier Algorithm. (2023). Experimental Student Experience, 2(2), 332-338. https://doi.org/10.58330/ese.v1i4.202