A Bidirectional GRU Approach with Hyperparameter Optimization for Sentiment Classification in Game Reviews
Pendekatan GRU Dua Arah dengan Optimasi Hiperparameter untuk Klasifikasi Sentimen dalam Ulasan Game
DOI:
https://doi.org/10.25134/ilkom.v19i2.399Keywords:
Sentiment Analysis, Game Reviews, Bidirectional GRU, Hyperparameter Optimization, Deep LearningAbstract
Sentiment analysis plays a vital role in understanding user perspectives, especially in domains such as game reviews where user feedback influences product perception and engagement. This study presents a comparative approach using Gated Recurrent Unit (GRU), hyperparameter-tuned GRU, and Bidirectional GRU models to classify sentiments in a dataset of game reviews. The experiment begins with standard preprocessing and tokenization steps, followed by vectorization and supervised training. Hyperparameter optimization is conducted using Keras Tuner to identify the most effective configuration of embedding dimensions, GRU units, dropout rates, and learning rates. The best model, a Bidirectional GRU with tuned parameters, achieves a validation accuracy of 85.37% and shows superior performance across key metrics such as precision, recall, and F1-score. Despite the relatively small and imbalanced dataset, the Bidirectional GRU model demonstrates robust generalization. This study also highlights future directions, including class balancing techniques and the integration of pretrained word embeddings to further improve model performance.
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Copyright (c) 2025 Nur Alamsyah, Titan Parama Yoga, Budiman, Imannudin Akbar, Acep Hendra, Alif Januantara Prima
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