COMPARATION OF CLASSIFICATION ALGORITHM ON SENTIMENT ANALYSIS OF ONLINE LEARNING REVIEWS AND DISTANCE EDUCATION
Abstract
As of January 27, 2021, confirmed cases of COVID-19 nationally stood at 1,024,298 people, this data is data that has been officially announced by the Indonesian Ministry of Health. Meanwhile, in Jakarta, there are 256,416 confirmed cases of COVID-19. In July 2021, there was a very significant increase, seeing the data caused the Central government to make a decision to continue the Large-Scale Social Restrictions (PSBB), followed by the Enforcement of Restrictions on Community Activities (PPKM), which affected all aspects, especially the education aspect. In the education aspect, the government applies distance and online learning. Of course, many people agree or disagree with this decision, because there must be sacrifices, both in terms of time and cost. Seeing these conditions makes the authors interested in discussing and processing public opinions on distance and online learning systems which certainly have positive and negative responses from learning implementers, to process the data the author uses Data Mining, namely using the Text Mining Classification method with several The classification algorithms are the Naïve Bayes Algorithm (NB), the k-Nearest Neighbor (k-NN) Algorithm and the Support Vector Machine (SVM) Algorithm to see which classification algorithm has the highest accuracy and diagnostic value in processing this opinion. After the calculations are done, the algorithm that is more suitable for analyzing reviews or opinions in this study is to use the Support Vector Machine (SVM) classification algorithm with the highest accuracy value of 87.67% and an AUC value of 0.939 with an Excellent Classification diagnostic level.
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