Optimasi Algoritma Naïve Bayes Menggunakan Algoritma Genetika Untuk Memprediksi Kelulusan

Studi Kasus: Mahasiswa Jurusan Matematika FMIPA Universitas Mulawarman

  • Elisa Feronica Laboratorium Matematika Komputasi FMIPA Universitas Mulawarman
  • Yuki Novia Nasution Laboratorium Matematika Komputasi FMIPA Universitas Mulawarman
  • Ika Purnamasari Laboratorium Statistika Ekonomi dan Bisnis FMIPA Universitas Mulawarman

Abstract

The Naïve Bayes algorithm is classification method that uses the principle of probability to create predictive models. Naïve Bayes is based on the assumption that all its attributes are independent which can be optimized by genetic algorithms. Genetic algorithm is an optimization technique which works by imitating the process of evaluating and changing the genetic structure of living creatures. In this study, the Naive Bayes algorithm was optimized using by genetic algorithm to predict student graduation with attributes, namely gender, regional origin, admission path and employment status. The data used is the students of the Mathematics Department, Faculty of Mathematics and Natural Sciences, Mulawarman University who graduated in March 2018 to December 2020. The results of this study indicate the accuracy value generated by Naïve Bayes of 50% increased by 16,67% after the attributes were optimized by using the genetic algorithm to 66,67% with 3 selected attributes, namely regional origin, admission path and employment status

Downloads

Download data is not yet available.

References

Arhami, M & Nasir, M. (2020). Data Mining: Algoritma dan Implementasi. Yogyakarta: Andi.
Arkeman, Y, Seminar, K. B & Gunawan, H. (2012). Algoritma Genetika: Teori dan Aplikasinya untuk Bisnis dan Industri. Bogor: IPB Press.
Busono, S. (2020). Optimasi Naïve Bayes Menggunakan Algoritma Genetika sebagai Seleksi Fitur untuk Memprediksi Performa Siswa. Jurnal Ilmiah Teknologi Informasi Asia, 14(1), 31-40.
Fithri, D. L & Darmanto E. (2014). Sistem Pendukung Keputusan untuk Memprediksi Kelulusan Mahasiswa Menggunakan Metode Naïve Bayes. Prosiding SNATIF.
Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques. ISRL 12. Springerlink.
Han, J, Kamber, M & Pei, J. (2012). Data Mining: Concepts and Techniques. Third Edition. USA: Elsevier.
Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi. (2018). Standar Nasional Pendidikan Tinggi (SN-DIKTI) Berdasarkan Permenristekdikti RI Nomor 44 tahun 2015 dan Perubahan Permenristekdikti RI Nomor 50 tahun 2018.
Kurniawan, D. (2020). Pengenalan Machine Learning dengan Python. Jakarta: PT Elex Media Komputindo.
Lembang, F. K & Fendjalang, M. (2015). Klasifikasi Variabel Penentu Kelulusan Mahasiswa FMIPA Unpatti Menggunakan Metode CHAID. Jurnal Statistika, 15(1), 1-6.
Suryani, N & Priyanti, E. (2019). Optimasi Naïve Bayes dan Algoritma Genetika untuk Prediksi Penerimaan Beasiswa Pendidikan pada SMP Utama. Jurnal Teknik Komputer AMIK BSI, 5(2), 189-196.
Suyanto. (2019). Data Mining untuk Klasifikasi dan Klasterisasi Data. Edisi Revisi. Bandung: Informatika.
Published
2022-11-01
How to Cite
FERONICA, Elisa; NASUTION, Yuki Novia; PURNAMASARI, Ika. Optimasi Algoritma Naïve Bayes Menggunakan Algoritma Genetika Untuk Memprediksi Kelulusan. EKSPONENSIAL, [S.l.], v. 13, n. 2, p. 147-152, nov. 2022. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/1057>. Date accessed: 10 dec. 2024. doi: https://doi.org/10.30872/eksponensial.v13i2.1057.
Section
Articles