Santoso, Gading Teguh (2021) Analisis Sentimen Pada Tweet Dengan Tagar #bpjsrasarentenir Menggunakan Metode Support Vectore Machine (SVM). Other thesis, Universitas Islam Riau.
![[img]](https://repository.uir.ac.id/style/images/fileicons/text.png) |
Text
143510514.pdf - Submitted Version Download (2MB) |
Abstract
Social networking helps internet users communicate. This is because social network users can convey messages by utilizing the facilities prepared by each social media. Social media users' messages can be used in various ways, such as a review of a product or a review of a problem in politics or current social problems. This can be done by analyzing the sentiments of social media users. The support vectore machine method is one method that can be used to analyze sentiment. In sentiment analysis using the support vectore machine method, it is done by classifying sentiment into positive or negative classes. The accuracy rate of sentiment analysis for #BPJSrasarentenir using the support vectore machine method is 94% using 200 tweet data.
| Item Type: | Thesis (Other) | ||||||
|---|---|---|---|---|---|---|---|
| Contributors: |
|
||||||
| Uncontrolled Keywords: | Analisis Sentiment, Tweet, Support Vectore Machine (SVM) | ||||||
| Subjects: | Q Science > QA Mathematics > QA76 Computer software | ||||||
| Divisions: | > Teknik Informatika | ||||||
| Depositing User: | Budi Santoso S.E | ||||||
| Date Deposited: | 20 Oct 2022 09:43 | ||||||
| Last Modified: | 20 Oct 2022 09:43 | ||||||
| URI: | http://repository.uir.ac.id/id/eprint/16772 |
Actions (login required)
 |
View Item |
