Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM

Prasetyo, Vincentius Riandaru and Naufal, Mohammad Farid and Wijaya, Kevin (2025) Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 9 (2). pp. 327-333. ISSN 2580-0760

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Abstract

This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). Due to the complex linguistic structure of the Indonesian language, sentiment classification remains challenging, necessitating advanced methods to capture both local patterns and sequential dependencies. The primary objective of this research is to improve sentiment classification accuracy by leveraging a hybrid model that integrates CNN for feature extraction and Bi-LSTM for contextual understanding. The dataset consists of 800 manually labeled samples collected from social media platforms, preprocessed using case folding, stop word removal, and lemmatization. Word embeddings are generated using the Word2Vec CBOW model, and the classification model is trained using a hybrid architecture. The best performance was achieved with 32 Bi-LSTM units, a dropout rate of 0.5, and L2 regularization, which was evaluated using Stratified K-Fold cross-validation. Experimental results demonstrate that the hybrid model outperforms conventional deep learning approaches, achieving 95.24% accuracy, 95.09% precision, 95.15% recall, and 95.99% F1 score. These findings highlight the effectiveness of hybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability.

Item Type:	Article
Uncontrolled Keywords:	sentiment analysis; CNN; Bi-LSTM; hybrid model
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	VINCENTIUS RIANDARU PRASETYO
Date Deposited:	02 May 2025 02:49
Last Modified:	02 May 2025 02:49
URI:	http://repository.ubaya.ac.id/id/eprint/48445

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