Halal Ingredient Detection in Packaged Food Products Using Multi-Layer Perceptron (MLP) - Suci Ramadhani; Yulita Molliq Rangkuti; Hermawan Syahputra; Elmanani Simamora; Zulfahmi Indra, (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
The halal status of ingredients in packaged food products is a significant concern for Muslim consumers, yet variations in label formats and technical terminology often hinder manual verification. This study introduces Halal Ingredient Detection in Packaged Food Products Using a Multi-Layer Perceptron (MLP). A dataset of 55,149 ingredient entries from OpenFoodFacts was automatically labeled using internationally recognized lists of prohibited ingredients. Preprocessing included case folding, stopword removal, and TF-IDF text representation. Various MLP architectures were evaluated by considering macro F1-score, training time, and model generalization. The best-performing architecture was a simple MLP with 16–8 neurons, using the Adam optimizer and binarry cross-entropy loss. Using an 80:20 training–testing data split, the proposed MLP model achieved an accuracy of 94%, with the confusion matrix indicating low misclassification rates and strong discrimination between halal and non-halal ingredients. These results demonstrate that a straightforward MLP architecture combined with TF-IDF is sufficient to capture relevant textual patterns, providing an efficient and reliable approach for automated halal ingredient classification.