Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes
 Samsir Samsir (Universitas Al Washliyah, Rantauprapat, Indonesia)
Ambiyar Ambiyar (Universitas Negeri Padang, Padang, Indonesia)
Unung Verawardina (IKIP PGRI Pontianak, Pontianak, Indonesia)
Firman Edi (Institut Teknologi Batam, Batam, Indonesia)
(*)Ronal Watrianthos  (Universitas Al Washliyah, Rantauprapat, Indonesia)
(*) Corresponding Author Submitted: November 16, 2020; Published: January 22, 2021 |
Abstract
The WHO announced that more than 52 million people tested positive for Covid-19, and 1.2 million died in the second week of November 2020. Meanwhile, Indonesia recorded 463 thousand individuals with 15,148 deaths that were confirmed positive. Strategy against pandemics by incorporating socialization. However, learning that was initially bold as a technique became controversial due to the briefness of the adaptation process. a wide continuum of social reactions has resulted in the sudden transition from face-to-face learning to bold learning on a large scale. This research focuses on public opinion on online learning during the Indonesian COVID-19 pandemic in early November 2020. The analysis was carried out on Twitter by mining document-based text that was interpreted using the Naïve Bayes algorithm. The results show that online learning has a positive sentiment of 30 percent, a negative sentiment of 69 percent, and a neutral 1 percent over the period. Due to community dissatisfaction about online learning, a significant amount of negative sentiment is created. Some tweets indicate disappointment with the words' stress 'and' lazy 'in the conversation being high-frequency words.
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