PRATAMA, ANDHIKA (2021) ANALISIS SENTIMEN PADA KOMENTAR AKUN INSTAGRAM JOKOWI MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER. Tugas Akhir thesis, University of Technology Yogyakarta.
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Abstract
ANDHIKA PRATAMA Department of Informatics, Faculty of Science & Technology University of Technology Yogyakarta North Ringroad St., Jombor Sleman Yogyakarta E-mail: advanvandroid5566@gmail.com ABSTRACT Instagram is a social media that is quite popular nowadays. Users ranging from children, teenagers to adults also boosted the popularity of Instagram. In an Instagram post, everyone can freely write a comment. Not infrequently, Instagram users comment with harsh words do not even hesitate to issue hate speech. Similarly, on the Instagram account of the President of the Republic of Indonesia, Mr. Joko Widodo, with the account name @jokowi, usually criticism, praise, insults warganet contained in the comments column on each post. Social media can analyze the sentiment of Instagram user comments to describe how satisfied the public is with Mr. Jokowi's performance. Sentiment analysis is a branch of text mining science used to extract, understand, and process text data. In this study, sentiment analysis in the textual document classification process is divided into two classes: negative and positive sentiment classes. To know the classification of each sentiment on the comment, using naïve Bayes classifier method. From the research that has been done, researchers have managed to create a sentiment analysis system with average results - average accuracy of 83%, the precision of 90.25%, and recall of 74.49% of 750 training data and 250 test data. Keywords: Sentiment Analysis, Naïve Bayes Classifier, Instagram
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
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| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 07 Sep 2021 04:44 |
| Last Modified: | 07 Sep 2021 04:44 |
| URI: | http://eprints.uty.ac.id/id/eprint/8000 |
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