Rizal Al Habsi, . (2021) ANALISIS SENTIMEN TERHADAP VAKSIN COVID-19 DI JEJARING SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
![[img]](http://repository.upnvj.ac.id/style/images/fileicons/text.png) |
Text
ABSTRAK.pdf Download (529kB) |
|
Text
AWAL.pdf Download (1MB) |
|
Text
BAB 1.pdf Download (701kB) |
|
Text
BAB 2.pdf Restricted to Repository UPNVJ Only Download (867kB) |
|
Text
BAB 3.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
BAB 4.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
BAB 5.pdf Download (669kB) |
|
Text
DAFTAR PUSTAKA.pdf Download (564kB) |
|
Text
RIWAYAT HIDUP.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
LAMPIRAN.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
ARTIKEL KI.pdf Restricted to Repository UPNVJ Only Download (693kB) |
Abstract
Twitter is a platform that allows people to express their aspirations, opinions, and criticisms directly. Since the outbreak of the corona virus or COVID-19 there have been many public opinions related to COVID-19, one of which is related to the COVID-19 vaccine policy. This study aims to find out how public opinion is formed against the COVID-19 vaccine in Indonesia on Twitter with the hashtags #vaksincovid19 and #vaksincorona. In this study, using the concept of public opinion which is categorized into positive and negative sentiments, the Naïve Bayes algorithm is used for the tweet classification process. The data retrieval process starts on January 13 to January 20, 2021 using data crawling techniques by utilizing the API facilities provided by Twitter. The results of the classification will enter the testing and evaluation phase using a confusion matrix to see the performance of the classification model against public opinion regarding the COVID-19 vaccine. From the results of testing the naïve Bayes classification model, the results obtained are 82.65% accuracy, 98% recall and 66.67% specificity. From the results of data labeling using 488 tweets, 251 positive sentiment tweets were obtained that were pro against the COVID-19 vaccine, and 237 tweets of negative sentiment were against the COVID-19 vaccine.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1710511027] [Pembimbing 1: Yuni Widiastiwi] [Pembimbing 2: Nurul Chamidah] [Penguji 1: Iin Ernawati] [Penguji 2: Noor Falih] |
| Uncontrolled Keywords: | Sentiment analysis, COVID-19 vaccine, Twitter, Naïve Bayes. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Rizal Al Habsi |
| Date Deposited: | 21 Dec 2021 07:50 |
| Last Modified: | 21 Dec 2021 07:50 |
| URI: | http://repository.upnvj.ac.id/id/eprint/12527 |
Actions (login required)
 |
View Item |