Sentiment Analysis of ChatGPTon Indonesian Textusing Hybrid CNN and Bi-LSTM

Sentiment Analysis of ChatGPTon Indonesian Textusing Hybrid CNN and Bi-LSTM

sentiment analysis;CNN; Bi-LSTM; hybrid model

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 thatintegrates 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, stopword 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 ofhybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability

Vincentius Riandaru Prasetyo1*, Mohammad Farid Naufal2, Kevin Wijaya3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6334/1043

Departmentof Informatics, Facultyof Engineering, University of Surabaya, Surabaya, Indonesia

11-04-2025

FAJAR BAGUS W

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