Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine
DOI:
https://doi.org/10.14421/jiehis.4672Keywords:
Classification, Naïve Bayes Classifier, PeduliLindungi, Sentiment Analysis, Support Vector Machine, Word AssociationAbstract
The Indonesian government is attempting to track the spread of the virus by creating an application named “PeduliLindungi” to deal with the coronavirus's exponential increase in cases across the country. Because it has a feature to disclose the user's location immediately, it is envisaged that this program can reduce the transmission of viruses in monitoring. Indonesians have used the PeduliLindungi, and there are user reviews of both positive and negative experiences. Therefore, to enhance these services, an assessment is required. The text mining method can extract information from users' reviews to collect this data. This method's application additionally uses the Naive Bayes Classifier and Support Vector Machine algorithms, which analyze word associations and do a classification evaluation of the data's accuracy. Based on the two methods' calculations, the NBC algorithm's average classification accuracy was 83.81%, and the SVM algorithm was 93.84%. Following that, discoveries on words that frequently exist or are used by people are obtained through word associations in the sentiment analysis of positive or negative reviews.
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Copyright (c) 2024 Rizki Rahmatullah, Jundi Nourfateha Elquthb, Fanya Nindha Al-Qurani, Annisa Uswatun Khasanah
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