IMPLEMENTASI ARSITEKTUR RECURRENT NEURAL NETWORK PADA ANALISIS SENTIMEN CLASH OF CHAMPIONS
DOI:
https://doi.org/10.36080/skanika.v8i2.3586Keywords:
sentiment analysis, clash of champions, Vanilla RNN, LSTM, GRUAbstract
Clash of Champions is an educational program by Ruangguru on YouTube that has received mixed responses. This study aims to perform sentiment analysis using three Recurrent Neural Network (RNN) architectures: Vanilla Recurrent Neural Network (Vanilla RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data consists of 2,100 training samples, 300 validation samples, and 600 testing samples collected from YouTube and enriched with data augmentation using GPT-4 technology. Additionally, 35 comments from a survey conducted via Google Form are used for generalization testing. Comments are classified into three sentiments: Pro, Neutral, and Contra. The analysis involves preprocessing, model training, and evaluation using standard metrics. GRU demonstrated the best performance with an accuracy of 99.2% and the highest F1 score. LSTM achieved an accuracy of 99.0% and a recall of 100% for the Pro class, while Vanilla RNN was less stable. On real-world data, GRU correctly predicted 16 comments, outperforming LSTM with 14 correct predictions and RNN with 13 correct predictions. GRU excels in accuracy, stability, and adaptability to the data.
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