ANALISIS PENGGUNAAN ALGORITMA KLASIFIKASI DALAM PREDIKSI KELULUSAN MENGGUNAKAN ORANGE DATA MINING

  • Dinda Safitri Universitas Buana Perjuangan Karawang
  • Shofa Shofiah Hilabi Universitas Buana Perjuangan Karawang
  • Fitria Nurapriani Universitas Buana Perjuangan Karawang
: https://doi.org/10.36341/rabit.v8i1.3009

Abstract

Timely graduation in Higher Education is the expectation of students. One of the requirements to graduate in their studies, students must take the final stage, namely completing the final project or thesis. But the time of graduation is not always able to predict when college students will graduate. Many factors cause student graduation such as GPA, credits, employment status and so on. Seeing this, it is important to have a method that can predict student graduation, but some universities do not have their own method to be able to estimate student graduation whether the student can graduate on time or not. To overcome this, a model is needed to be able to predict student graduation. In this study, an analysis of 3 methods was carried out, namely Naive Bayes, K-NN and Neural Network. The purpose of this study is to find out which method is more appropriate to use in predicting graduation. In this study, a comparison was also made between the three methods, and the best method was obtained, namely the K-NN method with an accuracy value of 89%.

Keywords: Graduated, Clasification, Naive Bayes, K-NN, Neural Network

References

H. Priyatman, F. Sajid, and D. Haldivany, “Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswa,” J. Edukasi dan Penelit. Inform., vol. 5, no. 1, p. 62, 2019, doi: 10.26418/jp.v5i1.29611.

V. Virtusena, A. Johar, and A. Wijanarko, “Mahasiswa Fakultas Teknik Unib Menggunakan Algoritme K- Means,” J. Rekursif, vol. 9, no. 2, pp. 206–225, 2021.

J. Zeniarja and A. Salam, “PERANCANGAN SISTEM PREDIKSI KELULUSAN MAHASISWA UNIVERSITAS DIAN NUSWANTORO MENGGUNAKAN UNIFIED MODELLING LANGUAGE,” vol. 3, no. 1, pp. 25–36, 2021.

A. Khaerunnisa, “Analisis Tingkat Kelulusan Mahasiswa di Unisba dengan menggunakan Algoritma K-Means Clustering,” J. Ris. Mat., pp. 67–76, 2022.

N. Azwanti, “Algoritma C4.5 Untuk Memprediksi Mahasiswa Yang Mengulang Mata Kuliah (Studi Kasus Di Amik Labuhan Batu),” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 11–22, 2018, doi: 10.24176/simet.v9i1.1627.

N. Khasanah, A. Salim, N. Afni, R. Komarudin, and Y. I. Maulana, “Prediksi Kelulusan Mahasiswa Dengan Metode Naive Bayes,” Technologia, vol. 13, no. 3, pp. 207–214, 2022.

Amril, Sutan, Yana, and Bayu, “Accounting Information System,” pp. 17–30, 2019.

N. A. Sinaga, B. H. Hayadi, and Z. Situmorang, “Perbandingan Akurasi Algoritma Naïve Bayes, K-Nn Dan Svm Dalam Memprediksi Penerimaan Pegawai,” J. Tek. Inf. dan Komput., vol. 5, no. 1, p. 27, 2022, doi: 10.37600/tekinkom.v5i1.446.

E. Purwaningsih and E. Nurelasari, “Penerapan K-Nearest Neighbor Untuk Klasifikasi Tingkat Kelulusan Pada Siswa,” Syntax J. Inform., vol. 10, no. 01, pp. 46–56, 2021, doi: 10.35706/syji.v10i01.5173.

R. Ridwan, H. Lubis, and P. Kustanto, “Implementasi Algoritma Neural Network dalam Memprediksi Tingkat Kelulusan Mahasiswa,” J. Media Inform. Budidarma, vol. 4, no. 2, p. 286, 2020, doi: 10.30865/mib.v4i2.2035.

Published
2023-01-10
How to Cite
[1]
D. Safitri, S. Hilabi, and F. Nurapriani, “ANALISIS PENGGUNAAN ALGORITMA KLASIFIKASI DALAM PREDIKSI KELULUSAN MENGGUNAKAN ORANGE DATA MINING”, rabit, vol. 8, no. 1, pp. 75-81, Jan. 2023.
Issue
Vol 8 No 1 (2023): Januari
Section
Articles
Creative Commons License

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

Copyright Notice

 The copyright of the received article shall be assigned to the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to published articles. Therefore, the author must submit a statement of the Copyright Transfer Agreement.*)

Creative Commons License

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

In line with the license, authors and any users (readers and other researchers) are allowed to share and adapt the material only for non-commercial purposes. In addition, the material must be given appropriate credit, provided with a link to the license, and indicated if changes were made. If authors remix, transform or build upon the material, authors must distribute their contributions under the same license as the original.

Please find the rights and licenses in RABIT : Jurnal Teknologi dan Sistem Informasi Univrab. By submitting the article/manuscript of the article, the author(s) accept this policy.

1. License

The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 

2. Author’s Warranties

The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).

3. User Rights

RABIT's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, RABIT permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and RABIT on distributing works in the journal.

4. Rights of Authors

Authors retain all their rights to the published works, such as (but not limited to) the following rights;

  • Copyright and other proprietary rights relating to the article, such as patent rights,
  • The right to use the substance of the article in own future works, including lectures and books,
  • The right to reproduce the article for own purposes,
  • The right to self-archive the article,
  • The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (RABIT : Jurnal Teknologi dan Sistem Informasi Univrab).

5. Co-Authorship

If the article was jointly prepared by other authors, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. RABIT will not be held liable for anything that may arise due to the author(s) internal dispute. RABIT will only communicate with the corresponding author.

6. Royalties

This agreement entitles the author to no royalties or other fees. To such extent as legally permissible, the author waives his or her right to collect royalties relative to the article in respect of any use of the article by RABIT.

7. Miscellaneous

RABIT will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. RABIT's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.

PDF (Bahasa Indonesia)
Abstract views: 1007
downloads: 788