Naïve Bayes Algorithm for Classification of Student Major’s Specialization

Astia Weni Syaputri(1*), Erno Irwandi(2), Mustakim Mustakim(3)


(1) UIN Sultan Syarif Kasim Riau
(2) UIN Sultan Syarif Kasim Riau
(3) UIN Sultan Syarif Kasim Riau
(*) Corresponding Author

Abstract


Majors are important in determining student specialization. If there is an error in the direction of the student, it will certainly affect the education of subsequent students. In SMA Negeri 1 Kampar Timur, there are two majors, namely Natural Sciences and Social Sciences. To determine these majors, it is necessary to reference the average value of student grades from semester 3 to semester 5 which includes the average value of Islamic religious education, Indonesian, Citizenship Education, English, Natural Sciences, Social Sciences, and Mathematics. Naive Beyes algorithm is an algorithm that can be used in classifying majors found in SMA Negeri 1 Kampar Timur. To determine the classification of majors in SMA Negeri 1 Kampar Timur, training data and test data are used, respectively at 70% and 30%. This data will be tested for accuracy using a confusion matrix and produces a fairly high accuracy of 96.19%. With this high accuracy, the Naive Bayes algorithm is very suitable to be used in determining the direction of students in SMA Negeri 1 Kampar Timur.

Keywords


Confusion Matrix; Classification; Naive Bayes; Student Majors

Full Text:

PDF

References


Baker, R. C. (1989). Nonlinear unstable systems. International Journal of Control, 23(4), 123–145. Bisri, M. H. (2015). Implementasi Algoritma Naïve Bayes untuk Memprediksi Penjurusan Siswa di SMA Kesatrian 1 Semarang. Jurnal Informatika, 1–7.

Bustami. (2014). Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Data Nasabah Asuransi. TECHSI - Jurnal Teknik Informatika, 8(1), 127–146. https://doi.org/10.26555/jifo.v8i1.a2086

Hasan, M. (2017). Menggunakan Algoritma Naive Bayes Berbasis. ILKOM Jurnal Ilmiah, 9(3), 317–324.

Hastuti, K. (2012). Analisis Komparasi Algoritma Klasifikasi Data Mining untuk Prediksi Mahasiswa Non-Aktif. Seminar Nasional Teknologi Informasi & Komunikasi Terapan 2012 (Semantik 2012), 2(1), 241–249.

Hayuningtyas, R. Y. (2017). Aplikasi Filtering of Spam Email Menggunakan Naïve Bayes. IJCIT (Indonesian Journal on Computer and Information Technology), 2(1), 53–60.

Kadafi, A. R. (2018). Perbandingan Algoritma Klasifikasi Untuk Penjurusan Siswa SMA. Jurnal ELTIKOM, 2(2), 67–77. https://doi.org/10.31961/eltikom.v2i2.86

Khasanah, F. N. (2016). Klasifikasi Proses Penjurusan Siswa Tingkat SMA Menggunakan Data Mining. Informatics for Educators and Professionals, 1(1), 65–69.

Liliana Swastina. (2013). Penerapan Algoritma C4.5 untuk Penentuan Jurusan Mahasiswa. Jurnal Gema Aktualita, 2(1), 93–98.

Naparin, H. (2016). Klasifikasi Peminatan Siswa SMA Menggunakan Metode Naive Bayes. Systemic: Information System and Informatics Journal, 2(1), 25–32. https://doi.org/10.29080/systemic.v2i1.104

Nugroho, Y. S. (2015). Klasifikasi dan Klastering Penjurusan Siswa SMA Negeri 3 Boyolali. Khazanah Informatika: Jurnal Ilmu Komputer Dan Informatika, 1(1), 1. https://doi.org/10.23917/khif.v1i1.1175 Rosandy,

T. (2016). Perbandingan Metode Naive Bayes Classifier dengan Metode Decision Tree (C4.5) untuk Menganalisa Kelancaran Pembiayaan (Study Kasus : KSPPS / BMT AL-FADHILA). Jurnal Teknologi Informasi Magister Darmajaya, 2(01), 52–62.

Saleh, A. (2015). Klasifikasi Metode Naive Bayes Dalam Data Mining Untuk Menentukan Konsentrasi Siswa. KeTIK, 200–208.

Saleh, Alfa, & Nasari, F. (2018). Penggunaan Teknik Unsupervised Discretization pada Metode Naive Bayes dalam Menentukan Jurusan Siswa Madrasah Aliyah. Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(3), 353. https://doi.org/10.25126/jtiik.201853705

Sofanudin, A. (2017). Program studi sistem informasi fakultas teknik universitas nusantara pgri kediri tahun 2017. Simki-Techsain, 01(03), 1–6.

Yusra, Olivita, D., & Vitriani, Y. (2016). Perbandingan Klasifikasi Tugas Akhir Mahasiswa Jurusan Teknik Informatika Menggunakan Metode Naïve Bayes Classifier dan K-Nearest Neighbor. Sains, Teknologi Dan Industri, 14(1), 79–85. https://doi.org/10.1002/mame.201200226


Article Metrics

Abstract view : 2119 times
PDF - 369 times

DOI: https://doi.org/10.26714/jichi.v1i1.5570

Refbacks

  • There are currently no refbacks.


____________________________________________________________________________
Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN 2715-6923 (print) | 2721-9186 (online)
Organized by
Department of Informatics
Faculty of Engineering
Universitas Muhammadiyah Semarang

W : https://jurnal.unimus.ac.id/index.php/ICHI
E : [email protected], [email protected]

View My Stats