HARDIANSYAH, TOMY (2022) TWITTER SENTIMENT ANALYSIS USING NAÏVE BAYES CLASSIFIER AND TF-IDF METHODS (CASE STUDY: #COVID19 VACCINE). Tugas Akhir thesis, University of Technology Yogyakarta.
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Abstract
ABSTRACT The new disease outbreak caused by 2019-nCoV or commonly referred to as COVID-19 was officially designated as a global pandemic by the World Health Organization (WHO) on March 11, 2020. Seeing the rapid spread of COVID-19 and the dangers that will arise if it is not immediately addressed, one of the most likely ways to prevent the spread of this virus is to develop a vaccine. The Indonesian government is also actively involved in planning vaccination activities to be given to its people. To find out the public's response to government policies related to vaccination, a system that can analyze public sentiment is needed through Twitter social media. Sentiment analysis is a branch of science from text mining that is used to extract, understand, and process text data. In this study, sentiment analysis is a process of classifying textual documents into two classes, namely the negative and positive sentiment classes. To find out the classification of each sentiment in the comments, the Naïve Bayes Classifier method is used. From the research that has been done, the researcher has succeeded in making a sentiment analysis system with an accuracy of 92.5% from 800 training data and 200 test data. Keywords: Sentiment Analysis, Naïve Bayes Classifier, Twitter, 92.5%
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 11 Apr 2022 02:06 |
| Last Modified: | 11 Apr 2022 02:06 |
| URI: | http://eprints.uty.ac.id/id/eprint/9664 |
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