Analisis Sentimen Tanggapan Masyarakat Terhadap Pembelajaran Daring di Masa Pandemi COVID-19 Melalui Sosial Media Twitter Menggunakan Klasifikasi Naïve Bayes

Zulfachmi Zulfachmi, Naufal Muhammad Kautsar, Zidan Azhari Ramadhan, Maria Brigita Mbembe, Yvonne Yvonne

Abstract


In Indonesia, the first case of COVID-19 was found on March 2, 2020. The Indonesian government then implemented PSBB (large-scale social restrictions) in every province, district, and city. The policies of the Ministry of Education and Culture include closing schools, limiting the capacity of practice rooms, implementing health standards, and providing internet quotas for students, instructors, and lecturers. However, there are positives and negatives to using online schools during the pandemic. On the other hand, many individuals claim that online learning adds to the burden of their daily activities, not to mention those who do not fully understand technology. Additionally, many individuals lack the necessary resources for online learning. Social media users generally share all their opinions through posts, status updates, and comments. This study uses the Naive Bayes approach to sentiment analysis. The majority of the data is derived from social media sites such as Twitter in the form of tweets. The tweets are then analyzed in such a way that an accuracy value can be generated as an evaluation of public satisfaction with the online learning process during the COVID-19 outbreak. Using the Nave Bayes algorithm, the level of accuracy in this study is 80.67%.

Keywords


Covid-19 Pandemic, Naïve Bayes Methods, Online Learning, Sentiment Analysis, , Social Media.

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References


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