Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Secara Daring Menggunakan Algoritma Naïve Bayes
DOI:
https://doi.org/10.14421/jiska.2021.6.3.161-170Keywords:
Online Learning, CRISP-DM, Naive Bayes, Classification, Data MiningAbstract
Since the spread of Covid-19 in Indonesia, in early March 2020, the activities of Educational Institutions have not been disrupted. As conventional learning. Learning at Singaperbangsa University began with regulation from the Ministry of Education and Culture of the Republic of Indonesia, from learning that boldly affects concentration, influences concentration, such as signals, learning atmosphere, and teaching methods, so that factors affect the level of student satisfaction in learning. This study aims to determine the level of student satisfaction with learning who dares to use the Bayes naive algorithm using RapidMiner tools with results obtained with an accuracy rate of 76.92%, class precision of 100.00%, class recall 57.14%, and an AUC value of 0.881 or close to, so the resulting model is good. In other words, the results obtained using the Naïve Bayes algorithm can be used as material for making decisions about the level of online learning satisfaction.
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