Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Sosial Media Twitter

I Wayan Sudiartana(1*), Wayan Gede Suka Parwita(2), Anak Agung Gede Bagus Ariana(3),

(1) STMIK STIKOM Indonesia, Denpasar, Bali, Indonesia
(2) STMIK STIKOM Indonesia, Denpasar, Bali, Indonesia
(3) STMIK STIKOM Indonesia, Denpasar, Bali, Indonesia
(*) Corresponding Author

Abstract


Twitter is one of the social media platforms that are widely known among the public, Twitter itself is a platform commonly used by the public in sharing experiences and opinions, Twitter is also used in finding information that is trending in cyberspace, trending information that is currently still hot and widely discussed on Twitter is the issue of covid-19 vaccination wherefrom the trending topic about covid-19 vaccination many people participate. In uploading, discussing, and giving opinions about covid-19 vaccination, where the opinions are given by the public actually make those who consume information become worried about clarity, and less clear opinions lead to positive, negative, or neutral posts. Therefore, data collection is carried out on Twitter about the covid-19 vaccination to conduct sentiment analysis of the data. Covid-19 vaccination data was clarified in 3 classes, namely positive, neutral, and negative. In research using the Lexicon method based with the lexicon dictionary approach (InSet Lexicon) as an opinion dictionary Indonesian. The results in the determination of sentiment analysis are obtained from the results of polarity score calculations using confusion matrix with total accuracy results on vaccine Sinovac data of 73.5%, Astra Zeneca vaccine data of 70.5%, Moderna vaccine data of 72.6%, and Pfizer vaccine data of 80% with the comparison of training data and test data of 1:1.

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