Automation of Halal Food Classification Using Bidirectional Long Short-term Memory on Ingredients List
DOI:
https://doi.org/10.15575/kjrt.v3i2.1774Keywords:
BiLSTM, Deep Learning, food ingredient list, halal classification, halal food detection, NLP, word embeddingAbstract
The global demand for halal food products continues to increase, particularly among Muslim consumers, necessitating an efficient and accurate halal classification system. This study proposes a deep learning-based automatic classification approach using Bidirectional Long Short-Term Memory (BiLSTM) to determine the halal or haram status of a product based on its ingredient list. The system utilizes comprehensive text preprocessing techniques such as normalization, stopword removal, and dictionary-based term mapping. Word representations are converted into dense semantic vectors using word embeddings such as Word2Vec and GloVe. A BiLSTM model is used to capture bidirectional contextual relationships in ingredient sequences, thereby enhancing semantic understanding. Testing results on a dataset of 3,979 samples show that the proposed model achieves a classification accuracy of 99.75%, outperforming traditional machine learning methods such as Naive Bayes and SVM. The system is proven effective in handling ingredient ambiguity and context-based classification, and has potential for real-world applications such as mobile-based halal scanners. Future research can adopt attention mechanisms and transform-based models to improve performance and interpretability.
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