IMPLEMENTASI ALGORITMA NAIVE BAYES DALAM ANALISIS POLARISASI OPINI MASYARAKAT TERKAIT VAKSIN COVID-19
DOI:
https://doi.org/10.36341/rabit.v7i2.2403Keywords:
naive bayes, opinion polarization, covid-19, vaccineAbstract
Until 2022, Indonesia was hit by the Covid-19 pandemic. Covid-19 is a virus that spreads very easily, so the World Health Organization (WHO) has declared the Covid-19 virus status as a global pandemic. The first case of Covid-19 was detected in Indonesia on March 2, 2020. Since then, many cases have been confirmed positive. The Indonesian government has made efforts to suppress the spread of Covid-19 so that the negative impacts caused by COVID-19 can be controlled, including the Covid-19 vaccination program which is divided into several stages, namely the administration of the Covid-19 vaccine, dose one, dose two, and the booster vaccine. However, the COVID-19 vaccination program for the community has raised various opinions in the social media universe, especially Twitter. Opinions expressed tend to be polarized into sentiments of support and rejection. For this reason, this study aims to determine the polarization of public opinion about the Covid-19 vaccination program using the Naive Bayes algorithm. The process of opinion polarization analysis includes collecting data via Twitter using the RapidMiner tools, then preprocessing the data by means of case folding, tokenizing, filtering and stemming. The last step is to classify public opinion using the Naive Bayes algorithm. This study resulted in polarization of public opinion including tweets of positive sentiment by 67%, tweets of neutral sentiment 9% and tweets of negative sentiment 32%. Also obtained the accuracy and recall rates of 88% and 97%.
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