Comparison of Naïve Bayes Algorithm and K-Nearst Neighbor Algorithm on Evaluation of Online Learning
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
Abstract
Since the outbreak of the endemic caused by the Corona virus in Indonesia, many methods have been tried, one of which is conducting remote training and encouraging students to practice from home each time. The use of digital technology in the midst of the COVID-19 endemic has a big contribution to learning institutions by practicing online learning. Students are expected to be able to accept the procedures that have been implemented by the state. However, this condition does not guarantee that students agree or accept this stage. Therefore, measurements are needed to determine the level of student happiness in carrying out online learning. With that in mind, the author conducted an experiment on the ability of the algorithm first, namely the form of grouping with the Naïve Bayes Algorithm and the K-Nearst Neighbor Algorithm. The information used is the basic information, meaning that the information obtained from the results of the questionnaire circulars for students in semester 3(3) semester 5(5) and semester 7(7) amounted to 352 respondents. In the development of the form of the algorithm using the type 9.3 rapid miner tools with the operators used are retrive, multiply, cross validation, Naïve Bayes Algorithm and knn, apply form and performance. The results of the accuracy of the Naïve Bayes Algorithm are 91.45%. The results of the accuracy of the K-Nearst Neighbor Algorithm are 97, 72%. The accuracy of the K-Nearst Neighbor Algorithm is greater than the Nave Bayes algorithm, so it can be concluded that the K-Nearst Neighbor Algorithm has good ability in grouping.
##plugins.themes.bootstrap3.article.details##
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The writer agreed that the article copyright by Smatika journal and the writer has the right to disseminate the paper published without permission in advance.
[2] Basori, Pemanfaatan Social Learning Network “Edmodo” dalam Membantu Perkuliahan Teori Bodi Otomotif di Prodi PTM JPTK FKIP UNS. 2013.
[3] A. Sadikin and A. Hamidah, “Pembelajaran Daring di Tengah Wabah Covid-19,” Biodik, vol. 6, no. 2, pp. 109–119, 2020, doi: 10.22437/bio.v6i2.9759.
[4] H. Hermanto, A. Mustopa, and A. Y. Kuntoro, “Algoritma Klasifikasi Naive Bayes Dan Support Vector Machine Dalam Layanan Komplain Mahasiswa,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 2, pp. 211–220, 2020, doi: 10.33480/jitk.v5i2.1181.
[5] J. Han, J, Kamber, M, & Pei, Data Mining: Concept and Techniques, Second Edition. Waltham. Morgan Kaufmann Publishers, 2006.
[6] D. T. Larose, Discovering Knowledge In Data: An Introduction to Data mining. JohnWilley& Sons. Inc, 2005.
[7] M. Hakim and A. Mulyapradana, “Pengaruh Penggunaan Media Daring dan Motivasi Belajar Terhadap Kepuasan Mahasiswa Pada Saat Pandemik Covid-19,” Widya Cipta J. Sekr. dan Manaj., vol. 4, no. 2, pp. 154–160, 2020, doi: 10.31294/widyacipta.v4i2.8853.
[8] K. D. R. Sianipar, S. W. Siahaan, M. Siregar, and P. P. P. A. N. W. F. I. R. H. Zer, “Penerapan Algoritma K-Means Dalam Menentukan Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Online,” Infomatek, vol. 22, no. 1, pp. 23–30, 2020, doi: 10.23969/infomatek.v22i1.2748.
[9] Bertalya, Konsep Data Mining, Klasifikasi: Pohon Keputusan. Jakarta: Universitas Gunadarma, 2009.
[10] A. Bode, “K-Nearest Neighbor Dengan Feature Selection Menggunakan Backward Elimination Untuk Prediksi Harga Komoditi Kopi Arabika,” Ilk. J. Ilm., vol. 9, no. 2, pp. 188–195, 2017, doi: 10.33096/ilkom.v9i2.139.188-195.