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Selivan, Diana
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Computer Science & IT

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Analisis Sentimen Pada Pembelajaran Daring Di Indonesia Melalui Twitter Menggunakan Naïve Bayes Classifier Sarasvananda, Ida Bagus Gede; Selivan, Diana; Radhitya, Made Leo; Putra, I Nyoman Tri Anindia
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1241

Abstract

Education is one of the areas most affected by the covid-19 pandemic. Education during the pandemic must continue. To reduce the spread of covid-19 and learning activities can run as usual, the government, in this case the Ministry of Education and Culture, has implemented a distance education system in Indonesia. In addition, the response from the community is very important for an evaluation of the applied online learning. With the implementation of the policy regarding online learning in Indonesia, it is necessary to conduct a sentiment analysis to find out how the responses, opinions, or comments from the public and online learning actors related to online learning are currently being implemented. So the author conducted a research entitled Sentiment Analysis on Online Learning in Indonesia Through Twitter Using the Naïve Bayes Classifier Method to measure student responses regarding online learning during the covid -19 pandemic in Indonesia. The results of the accuracy of this study is 99.8% and the classification error is 0.12%. Of the total data entered, 83 tweets or 20% were included in the positive class, the negative class was 317 tweets or 80%.
Co-Authors I Nyoman Tri Anindia Putra Ida Bagus Gede Sarasvananda Radhitya, Made Leo