Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent
Neural Network dan Naïve Bayes

Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent
Neural Network dan Naïve Bayes

Sentiment Analysis, Vaccine COVID-19, TF-IDF, RNN, Naïve Bayes

COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive
measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about
the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very
important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine
the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000
COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral
sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of preprocessing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of
RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give
weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of
97.77% compared to Naïve Bayes (TF-IDF) of 80%.

Merinda Lestandy1
, Abdurrahim Abdurrahim2
, Lailis Syafa’ah3

Universitas Muhammadiyah Malang

26-08-2021

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Indonesia

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