DZAKY, DEWA SHEVA and Abdiansah, Abdiansah (2025) SISTEM TANYA JAWAB EKSTRAKTIF PADA TEKS BERBAHASA INDONESIA DENGAN FINE-TUNING INDOBERT. Undergraduate thesis, Sriwijaya University.
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Abstract
The abundance of digital information in today's era makes the extraction of relevant information a major challenge, especially in Indonesian, which has unique linguistic characteristics. As an effort to overcome this challenge, this study develops an extractive question-answering system for Indonesian text by fine-tuning the IndoBERT model, which enables the system to extract specific parts of a context paragraph as answers to given questions. The dataset used in this study is the Indonesian-translated version of the Stanford Question Answering Dataset (SQuAD) 2.0, which contains more than 100,000 question-answer pairs derived from Wikipedia articles. The fine-tuning process was carried out in eight scenarios, which are combinations of dataset type (the full dataset including unanswerable questions and a modified dataset with all unanswerable questions removed), learning rate (2e-5 and 5e-5), and batch size (16 and 48). The results of the study show that the model with a learning rate of 5e-5 and batch size of 16 delivers the best performance. On the dataset with unanswerable questions, the model achieved an exact match score of 60.57% and an f1-score of 70.84%. Meanwhile, on the dataset without unanswerable questions, the model achieved an exact match score of 54.79% and an f1-score of 73.06%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Extractive Question Answering System, SQuAD, IndoBERT, fine-tuning, exact match, f1-score |
| 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: | Dewa Sheva Dzaky |
| Date Deposited: | 22 May 2025 02:12 |
| Last Modified: | 22 May 2025 02:12 |
| URI: | http://repository.unsri.ac.id/id/eprint/173583 |
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