Halal Ingredient Detection in Packaged Food Products Using Multi-Layer Perceptron (MLP)

Suci Ramadhani; Yulita Molliq Rangkuti; Hermawan Syahputra; Elmanani Simamora; Zulfahmi Indra

doi:10.59934/jaiea.v5i2.2110

Authors

Suci Ramadhani Universitas Negeri Medan
Yulita Molliq Rangkuti Universitas Negeri Medan
Hermawan Syahputra Universitas Negeri Medan
Elmanani Simamora Universitas Negeri Medan
Zulfahmi Indra Universitas Negeri Medan

DOI:

https://doi.org/10.59934/jaiea.v5i2.2110

Keywords:

Halal Detection, , Ingredients, Packaged Food, TF-IDF, Multilayer Perceptron

Abstract

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.

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