FINE-TUNING INDOBERT UNTUK KLASIFIKASI KATEGORI BERITA BERBAHASA INDONESIA

FITRIYANI, KIAGUS MUHAMMAD EFAN and Yusliani, Novi and Marieska, Mastura Diana (2024) FINE-TUNING INDOBERT UNTUK KLASIFIKASI KATEGORI BERITA BERBAHASA INDONESIA. Undergraduate thesis, Sriwijaya University.

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Abstract

The availability of Indonesian news articles on the internet has greatly increased, making it more challenging to recognize and categorize news accurately. Therefore, a solution to this issue is to develop a classification system for Indonesian news article categories. This research aims to classify Indonesian news category using fine-tuning on the pre-trained IndoBERT model. The dataset consists of 31,993 articles divided into five news categories: education, health, technology, sports, and automotive. Articles were collected from two of the largest and most trusted online news portals, kompas.com and detik.com, using web scraping method. The fine-tuning process was divided into 8 scenarios, which are combinations of dataset type configurations, learning rate, and batch size. Based on the test results, the highest accuracy was obtained in scenario 2, where the model trained with a learning rate of 2e-5 and batch size of 32, reaching an accuracy of 98.37%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	News Classification, Pre-trained Model, IndoBERT, Fine-tuning, Web Scraping, Accuracy
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:	Kiagus Muhammad Efan Fitriyan
Date Deposited:	07 Jan 2025 01:56
Last Modified:	07 Jan 2025 01:56
URI:	http://repository.unsri.ac.id/id/eprint/162736

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