Deep Learning‑Based Sentiment Classification on Category Service and Resolution of Consumer Complaints in Digital Banking - Kusuma, Irnayanti Dwi; Fatichah, Chastine (ILKOMNIKA, 2025)
The growth of digital banking in Indonesia has transformed customer interactions. It emphasizes the need to understand user sentiment and feedback. This study aims to analyze public perceptions of the Jenius digital banking application through sentiment analysis using deep learning methods enhanced by easy data augmented (EDA). The dataset written in Indonesian related to Jenius from Twitter. Data collected between August 2016 and August 2024 were manually annotated for sentiment polarity (positif, netral, negatif) and complaint handling categories (edukasi, konsultasi, fasilitasi, none). The EDA technique was used to enhance linguistic diversity and reduce class imbalance before training two deep learning models, Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN). The results show that EDA + BiLSTM achieved an accuracy of 0.68, whereas EDA + CNN obtained 0.66. BiLSTM slightly outperforms CNN across precision, recall, and F1-score. These findings indicate that both models effectively handle augmented data, with the BiLSTM model demonstrating a better contextual understanding of Bahasa Indonesia. The integration of EDA significantly improves the robustness and performance of the model in sentiment and aspect-based classification. This study highlights the potential of EDA as a simple yet effective method for enhancing deep learning models.