Implementation of Naïve Bayes for Classification of Learning Types
Abstract
Learning is a process that is carried out by each individual from not knowing to knowing, or from bad behavior to being good, so that it has a good change for the individual, Each individual has a learning type in receiving the material presented by the teacher, but not all individuals understand what type of learning they need, The purpose of the research is to determine the type of learning of the students of the Faculty of Computer Science. The method used is nave Bayes for the accuracy of its calculations. The results of this study are the classification of visual learning types as many as 50 people, for audio as many as 24 people, while kinesthetic as many as 25 people, for the Informatics Engineering Study Program as many as 61, consists of 37 visual learning types, Auditory 14 people, Kinesthetic 10 people, While the Information Systems Study Program is 37 people, where is Visual 14 people, Auditory 9 people and Kinesthetic 14 people. With this classification, it can help lecturers apply learning methods that are suitable for their students. The best Naïve Bayes accuracy rate is 88.89%
Downloads
References
H. Naparin, “Klasifikasi peminatan siswa sma menggunakan metode naive bayes,” vol. 2, no. 1, pp. 25–32, 2016.
B. H. Fadillah, Annisa Paramitha, “Penerapan naïve bayes classifier untuk pemilihan konsentrasi mata kuliah,” 2016.
K. Samponu, Yohakim Benedictus, “Optimasi algoritma naive bayes menggunakan metode cross validation untuk meningkatkan akurasi prediksi tingkat kelulusan tepat waktu,” vol. 1, no. 2, pp. 56–63, 2017.
R. K. N. Farida, Intan Nur, “Penggunaan Algoritma Naive Bayes Untuk Mengevaluasi Prestasi Akademik Mahasiswa Universitas Nusantara PGRI Kediri,” vol. 3, no. November, pp. 122–127, 2017.
H. Annur, “Klasifikasi masyarakat miskin menggunakan metode naive bayes,” vol. 10, pp. 160–165, 2018.
N. Agustina, A. Cahyanto, J. Herwanto, R. Ratnasari, and S. Dewi, “Klasifikasi konten post pada media sosial instagram perguruan tinggi xyz menggunakan algoritma naive bayes,” vol. 6, no. 1, pp. 11–16, 2019.
I. Romli, E. Pusnawati, and U. P. Bangsa, “Penentuan tingkat penjualan mobil di indonesia dengan menggunakan algoritma naive bayes,” vol. x, no. x, 2019.
P. P. S. Elkin Rilvani, Ahmad Budi Trisnawan, “Pelita Teknologi : Jurnal Ilmiah Informatika , Arsitektur dan Lingkungan VALIDATION,” vol. 14, no. September, pp. 145–153, 2019.
J. I. S. Wiranto Horsen Silitonga, “Analisis Sentimen Pemilihan Presiden Indonesia Tahun 2019 di Twitter berdasarkan Geolocation Menggunakan Metode Naive Bayesian Classification.” pp. 1–13, 2019.
M. Sadikin, R. Rosnelly, and T. S. Gunawan, “Perbandingan Tingkat Akurasi Klasifikasi Penerimaan Dosen Tetap Menggunakan Metode Naive Bayes Classifier dan C4 . 5,” vol. 4, pp. 1100–1109, 2020.
Annur, H.. Klasifikasi Masyarakat Miskin Menggunakan Metode Naive Bayes. ILKOM Jurnal Ilmiah, 10(2), 160-165. (2018)
Fadlan, C., Ningsih, S., & Windarto, A. P.. Penerapan Metode Naïve Bayes Dalam Klasifikasi Kelayakan Keluarga Penerima Beras Rastra. JUTIM (Jurnal Teknik Informatika Musirawas), 3(1), 1-8. (2018)
M. Siddik, H. Hendri, R. Putri, Y. Desnelita, and G. Gustientiedina, “Klasifikasi Kepuasan Mahasiswa Terhadap Pelayanan Perguruan Tinggi Menggunakan Algoritma Naïve Bayes”, INTECOMS: Journal of Information Technology and Computer Science, vol. 3, no. 2, pp. 162-166, Nov. 2020
Romli, I., & Putra, B. M. Evaluasi Penilaian Kinerja Dalam Klasifikasi Data Mining Dengan Metode Naive Bayes. JURNAL TEKNIK INDUSTRI, 1(01), 36-45. (2021).
Wulandari, F., Jusia, P. A., & Jasmir, J.. Klasifikasi Data Mining Untuk Mendiagnosa Penyakit ISPA Menggunakan Metode Naïve Bayes Pada Puskesmas Jambi Selatan. Jurnal Ilmiah Mahasiswa Sistem Informasi, 2(3), 214-227. (2020)
Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Jurnal Digital Zone is licensed under aCreative Commons Attribution-ShareAlike 4.0 International License.
