Hybrid CNN-BiLSTM untuk Analisis Sentimen Multi-Platform terhadap Insiden Keamanan Pangan Program Makan Bergizi Gratis
-
Mohamad Rival Farid Riwaldi
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Aripin Aripin Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
The decline in stunting prevalence in Indonesia has not been accompanied by improvements in the quality of nutritional intervention program implementation, including the Free Nutritious Meal Program (MBG), which sparked public controversy following food safety incidents in several regions. The high volume of cross-platform public opinion on social media requires an analytical approach capable of simultaneously capturing diverse linguistic styles from various sources. This study proposes a hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) classification model to analyze public sentiment regarding the incidents, with CNN extracting local feature patterns and BiLSTM modeling bidirectional word-sequence dependencies. A total of 3,416 comments were collected from five social media platforms (X, Instagram, TikTok, YouTube, and Facebook), then processed through text preprocessing and initial lexicon-based labeling into three sentiment classes: negative, neutral, and positive. To strengthen label validity, the labeling quality was validated through manual annotation by two independent annotators, yielding a Cohen’s Kappa value of κ = 0.828. The dataset was split using an 80:20 stratified scheme, with class weight applied to reduce bias caused by class imbalance without changing the number of samples in each class. The hybrid model was compared with two baseline models, CNN and BiLSTM, using macro F1-score as the primary metric, while accuracy was used as a supporting metric. The experimental results show that the hybrid CNN–BiLSTM model achieved a macro F1-score of 90.38% and an accuracy of 94.59%, outperforming both baseline models. Misclassification analysis revealed that most errors occurred in argumentative comments, negation, and contrastive sentences, reflecting the limitations of lexicon-based labeling in capturing nuanced language. Overall, this approach demonstrates the potential of cross-platform deep learning-based sentiment analysis as an initial component for monitoring public opinion on national-scale government policies. This study contributes by providing a manually validated multi-platform Indonesian dataset, developing a hybrid CNN-BiLSTM architecture with a class weight scheme effective for three-class sentiment classification on informal text, and opening opportunities for applying deep learning as a means of data-driven public opinion monitoring.
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