Rini, Indah Setia and ., Helmi Imaduddin, S.Kom., M.Eng (2026) Cyberbullying Detection Using INDOBERT and XGBOOST. Skripsi thesis, Universitas Muhammadiyah Surakarta.
![[img]](https://eprints.ums.ac.id/style/images/fileicons/application_pdf.png) |
PDF (Naskah Publikasi.)
Naskah Publikasi library.pdf Download (597kB) |
|
PDF (Surat Pernyataan Publikasi.)
Unggah Indah Rini.pdf Restricted to Repository staff only Download (289kB) |
Abstract
Social media has experienced massive growth and widespread adoption in recent times. While it is a product of technological advancement offering numerous positive impacts, it also entails unavoidable negative consequences that require special attention. Cyberbullying is a tangible negative outcome as-sociated with social media; the intent behind such acts is often to threaten victims for personal gratifi-cation. Victims suffer various adverse effects that impact their well-being and daily lives, necessitating solutions to address this issue. Previous research combining IndoBERT and SVM to detect cyberbullying achieved an accuracy of 89.59%. In this study, the researchers introduce an innovation by combining the IndoBERT algorithm with XGBoost, utilizing an Instagram dataset sourced from the Hugging Face platform. The methodology involved pre-processing steps—specifically case folding and data clean-ing—followed by feature extraction using IndoBERT. During the IndoBERT phase, tokenization was per-formed using IndoBERT’s built-in autotokenizer prior to feature extraction. Subsequently, the feature extraction output—a 768-dimensional numeric vector—was fed into an XGBoost classifier. The combi-nation of IndoBERT for feature extraction and XGBoost for classification yielded an accuracy of 83,33% in detecting cyberbullying on Instagram.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | cyberbullying, IndoBERT, NLP, social media, XGBoost |
| Subjects: | T Technology > Information Technology > Artificial Intelligence T Technology > TI Industrial Engineering > Otomasi, Robot, Mesin Pabrik |
| Divisions: | Fakultas Komunikasi dan Informatika > S1 Teknik Informatika |
| Depositing User: | INDAH SETIA RINI |
| Date Deposited: | 19 Aug 2026 06:42 |
| Last Modified: | 19 Aug 2026 06:42 |
| URI: | http://eprints.ums.ac.id/id/eprint/148656 |
Actions (login required)
 |
View Item |
