Naufaldy Purwanto, . (2024) KLASIFIKASI DATA KOMENTAR COVID 19 PADA TWITTER MENGGUNAKAN METODE KLASIFIKASI NAIVE BAYES. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
A lot of Twitter users express their opinions through the tweets they send on Twitter social media. One of the tweets that became a conversation in 2019 was about the Covid19 Pandemic. Lots of tweets discussing the Covid19 Pandemic. They always discuss how the positive and negative impacts of the Covid19 Pandemic. From these problems, research was conducted on sentiment analysis on Twitter data related to the Covid19 Pandemic and using the Naïve Bayes method. The data taken is data from tweets using the tweet-harvest library that has been integrated with the Twitter API. Before carrying out the data classification process, the data will be labeled, namely positive labels and negative labels manually. After that, preprocessing is carried out with the stages of data cleaning, tokenization, case folding, stopword removal, stemming. Then do word weighting using CountVectorizer. The next stage is to compare the results of data division of 80% training data and 20% test data with 70% training data and 30% test data. By classifying using the Naïve Bayes method. The results of data sharing with 70% training data and 30% test data resulted in the highest accuracy rate 91%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1910511128] [Pembimbing: Jayanta] [Penguji 1: Widya Cholil] [Penguji 2: Musthofa Galih Pradana] |
| Uncontrolled Keywords: | Sentiment Analysis, Classification, Covid19 Pandemic, Naïve Bayes Classification, Twitter |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Naufaldy Purwanto |
| Date Deposited: | 25 Mar 2024 01:42 |
| Last Modified: | 25 Mar 2024 01:42 |
| URI: | http://repository.upnvj.ac.id/id/eprint/29046 |
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