ANALISIS SENTIMEN TERKAIT APLIKASI CHATGPT MENGGUNAKAN BERT DAN RNN

FITRIANI, WIDYA and Yusliani, Novi and Rachmatullah, M. Naufal (2025) ANALISIS SENTIMEN TERKAIT APLIKASI CHATGPT MENGGUNAKAN BERT DAN RNN. Undergraduate thesis, Sriwijaya University.

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Abstract

ChatGPT has become one of the most popular artificial intelligence tools worldwide, but its rapid adoption has also generated diverse user reactions and reviews. Understanding these perceptions is important for evaluating and improving service quality. Sentiment analysis is a suitable approach to explore such opinions. This study employs BERT (Bidirectional Encoder Representations from Transformers) as the embedding technique and RNN (Recurrent Neural Network) as the classification model. Two classification scenarios are used: binary (positive and negative) and multi-class (positive, neutral, and negative). To overcome the limitation of labeled data, a semi supervised pseudo-labeling strategy is applied during fine tuning. Experimental results show that binary classification achieved the highest accuracy of 91%, while the three-label scenario reached only 77%. The lower result in the three label setting is mainly caused by difficulties in identifying ambiguous neutral sentiments and the influence of imbalanced labels. Overall, combining BERT and RNN is effective for sentiment analysis, particularly in binary classification, though challenges remain in multi-label contexts.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, BERT, RNN
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Widya Fitriani
Date Deposited:	22 Jul 2025 08:47
Last Modified:	22 Jul 2025 08:47
URI:	http://repository.unsri.ac.id/id/eprint/179852

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