Fikri Adams, . (2021) ANALISIS SENTIMEN VAKSIN COVID-19 PADA TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Indonesia is currently experiencing a COVID-19 pandemic, the spread of COVID-19 cases is so large that it has an impact on the economy, social, culture and health. The Indonesian government is currently taking policy steps to overcome the problem of the spread of COVID-19 cases, one of which is by vaccinating the community. With this policy, Twitter has become a source of information that can influence the COVID-19 vaccine policy because there are still many people who are pro and contra about the COVID-19 vaccine. In this study, sentiment analysis uses the support vector machine algorithm method with a radial basis function kernel. The data used comes from social media Twitter with the topic of public opinion on the COVID-19 vaccine. Experiments on January 13 to January 20, 2021 get an accuracy value of 82.6%. The classification results obtained are quite good in analyzing sentiment towards the COVID-19 vaccine on positive and negative tweets. From the labeling of positive sentiment data of 251 and negative sentiment of 237, the public response to the COVID-19 vaccine on Twitter social media still dominates positive sentiment because many people support and invite the COVID-19 vaccine.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [N0 Panggil : 1 NIM : 1710511029] [Pembimbing 1 : lin Ernawati] [Pembimbing 2 : Nurul Chamidah] [Penguji 1 : Didit Widyanto] [Penguji 2 : Ria Astriratma] |
| Uncontrolled Keywords: | Vaccines, COVID-19, analysis, sentiment, Support Vector Machine, twitter |
| Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > Q Science (General) Q Science > QR Microbiology > QR180 Immunology Q Science > QR Microbiology > QR355 Virology |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Fikri Adams |
| Date Deposited: | 21 Dec 2021 07:48 |
| Last Modified: | 21 Dec 2021 07:48 |
| URI: | http://repository.upnvj.ac.id/id/eprint/11134 |
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