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SISTEM TANYA JAWAB EKSTRAKTIF PADA TEKS BERBAHASA INDONESIA DENGAN FINE-TUNING INDOBERT

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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%.

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Inventory Code Barcode Call Number Location Status
2507002880T173583T1735832025Central Library (REFERENCE)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1735832025
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xvii, 61 hlm.; ill.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Teknik Informatika
Specific Detail Info
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Statement of Responsibility
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