Emeraldi, Muhammad Aqil (2022) Analisis Sentimen Masyarakat Terhadap Kebijakan Pemerintah Selama Pandemi Covid-19 Menggunakan Algoritma Naive Bayes. Diploma (D3) thesis, Universitas Islam “45” Bekasi.
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Abstract
The abundance of various kinds of information on the internet along with the development of the world of information and communication technology has caused a massive increase in data, one of the sources coming from social media, especially Twitter, which is currently discussion Covid-19 a lot. Reporting through Twitter media regarding the impact of the Covid-19 virus is widely discussed because it causes unrest for the public which has led to the issuance of various government policies to prevent the spread of Covid-19. Related to this, it is necessary to conduct a sentiment analysis of the text on Twitter media. In this study, sentiment analysis was carried out regarding public sentiment towards government policies during the Covid-19 pandemic in Indonesia on Twitter social media using the Naive Bayes Classifier method where the data was classified into 2 sentiment values, namely positive and negative sentiment. The data used are 300 positive tweets and 300 negative tweets, where 80% of the total data is used as training data and 20% of the data is used as test data. From the test results, with a total of 120 tweets, the results of the measurement of recall value are 93.33%, precision is 93.33%, F - Score is 93.33% and the average accuracy is 93.33%. Keywords: sentiment analysis, covid-19, naive Bayes classification
| Item Type: | Thesis (TA, Skripsi, Tesis, Disertasi) (Diploma (D3)) | |||||||||
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| Contributors/Dosen Pembimbing,NIDN Dosen bisa diakses di LINK https://bit.ly/NIDNdosenunismabekasi: |
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| Keywords / Kata Kunci: | sentiment analysis, covid-19, naive Bayes classification | |||||||||
| Subjects: | Pemrograman | |||||||||
| Faculty: | Fakultas Teknik > Teknik Komputer D3 | |||||||||
| Depositing User: | Users 220 not found. | |||||||||
| Date Deposited: | 22 Aug 2022 02:15 | |||||||||
| Last Modified: | 22 Aug 2022 02:15 | |||||||||
| URI: | http://repository.unismabekasi.ac.id/id/eprint/698 |
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