Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long ShortTerm Memory (LSTM)
Dublin Core
Title
Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long ShortTerm Memory (LSTM)
Subject
sentiment analysis; public acceptance; covid-19 vaccine; naïve bayes; support vector machine; lstm.
Description
The Covid-19 vaccination is a government program during the pandemic to create herd immunity so that people become more
productive in their activities. In Indonesia, the Covid-19 vaccination campaign employs a range of vaccines and has sparked
a range of responses from the public on social media, particularly Twitter. Users can tweet and communicate with one another
on the social networking site Twitter. This study uses a Sentiment Analysis technique using the Nave Bayes (NB), Support
Vector Machine (SVM), and Long Short-Term Memory (LSTM) algorithms to conduct a sentiment analysis of public acceptance
of the type of Covid-19 vaccine used in Indonesia using Twitter data. Various types of vaccines in Indonesia include Sinovac,
Vaksin Covid-19 Bio Farma, AstraZeneca, Pfizer, Moderna, Sinopharm, Novavax, Sputnik-V, Janssen, Convidencia, Zifivax,
often confuse the public in determining the objectivity of this opinion. In addition, theoretically, this study also seeks to contrast
the NB, SVM, and LSTM algorithms with experimental techniques to obtain the best algorithm model. The stages of the research
involved gathering information based on Twitter user opinions about the type of Covid-19 vaccine on Twitter from January
2021 to January 2022. The researcher used Indonesian language tweet data with the keywords #vaksincorona, #vaksincovid19,
#vaksinasi, #ayovaksin, #lawancovid19, and #vaksinindonesia. Before modelling, the pre-processing stage consists of case
folding, tokenizing, filtering, stemming, and word weighting using TF-IDF. After that, model testing was carried out using
Cross Validation with the Python programming language, and evaluation and validation of the test results using the Confusion
Matrix. The results showed that the accuracy score of the SVM method for the best model was 84.89%, while for the Naïve
Bayes and LSTM algorithms, they were 84.65% and 82.97%, respectively.
productive in their activities. In Indonesia, the Covid-19 vaccination campaign employs a range of vaccines and has sparked
a range of responses from the public on social media, particularly Twitter. Users can tweet and communicate with one another
on the social networking site Twitter. This study uses a Sentiment Analysis technique using the Nave Bayes (NB), Support
Vector Machine (SVM), and Long Short-Term Memory (LSTM) algorithms to conduct a sentiment analysis of public acceptance
of the type of Covid-19 vaccine used in Indonesia using Twitter data. Various types of vaccines in Indonesia include Sinovac,
Vaksin Covid-19 Bio Farma, AstraZeneca, Pfizer, Moderna, Sinopharm, Novavax, Sputnik-V, Janssen, Convidencia, Zifivax,
often confuse the public in determining the objectivity of this opinion. In addition, theoretically, this study also seeks to contrast
the NB, SVM, and LSTM algorithms with experimental techniques to obtain the best algorithm model. The stages of the research
involved gathering information based on Twitter user opinions about the type of Covid-19 vaccine on Twitter from January
2021 to January 2022. The researcher used Indonesian language tweet data with the keywords #vaksincorona, #vaksincovid19,
#vaksinasi, #ayovaksin, #lawancovid19, and #vaksinindonesia. Before modelling, the pre-processing stage consists of case
folding, tokenizing, filtering, stemming, and word weighting using TF-IDF. After that, model testing was carried out using
Cross Validation with the Python programming language, and evaluation and validation of the test results using the Confusion
Matrix. The results showed that the accuracy score of the SVM method for the best model was 84.89%, while for the Naïve
Bayes and LSTM algorithms, they were 84.65% and 82.97%, respectively.
Creator
Dinar Ajeng Kristiyanti, Sri Hardani
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
June 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Dinar Ajeng Kristiyanti, Sri Hardani, “Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long ShortTerm Memory (LSTM),” Repository Horizon University Indonesia, accessed February 4, 2026, https://repository.horizon.ac.id/items/show/9996.