Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying
Public Opinion about COVID-19 Vaccine in Indonesia

Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying
Public Opinion about COVID-19 Vaccine in Indonesia

Vaccine COVID-19, IndoBERT, IndoBERTweet, CNN-LSTM, Indonesia, Sentiment

COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over
the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge
transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In
responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading
text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and
IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such
as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about
vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to
classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach
53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement
for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic

Siti Saadah1
, Kaenova Mahendra Auditama2
, Ananda Affan Fattahila3
, Fendi Irfan Amorokhman4
,
Annisa Aditsania5
, Aniq Atiqi Rohmawati6

Telkom University

30-08-2022

Fajar bagus W

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Indonesia

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