ZOHRO, FAUZAN AZIMAN and Rodiah, Desty and Satria, Hadipurnawan (2024) KLASIFIKASI CYBERBULLYING DI TWITTER MENGGUNAKAN METODE SVM (SUPPORT VECTOR MACHINE) DAN INFORMATION GAIN. Undergraduate thesis, Sriwijaya University.
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Abstract
Twitter, with 237.8 million daily active users, the majority aged 18-29 years, shows great potential as a communication medium. However, it also brings the problem of uncontrolled online behavior due to bullying comments. This research classifies bullying comments on Indonesian language social media using Support Vector Machine (SVM) and Information Gain. The process includes feature selection with Information Gain, calculating term weights with TF-IDF, and classification analysis with SVM. With a total of 365 bully data and 365 non-bully data. The test results show a significant increase in classification thanks to the use of Information Gain. The accuracy value of SVM without Information Gain is 0.822, while with Information Gain it is 0.830. This shows that selection of Information Gain features in SVM classification provides an increase in classification performance, which is caused by Information Gain's ability to select relevant features and the selection of C values to improve model adjustment. Keywords: bully, Information Gain, Classification, Support Vector Machine, Twitter
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | bully, Information Gain, Classification, Support Vector Machine, Twitter |
| Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | Fauzan Aziman Zohro |
| Date Deposited: | 05 Jul 2024 03:58 |
| Last Modified: | 05 Jul 2024 03:58 |
| URI: | http://repository.unsri.ac.id/id/eprint/149440 |
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