| dc.description.abstract | Halal food plays a critical role in the Islamic faith, as it represents food that is considered
lawful according to Islamic law. Muslims are encouraged to eat only Halal foods to ensure
that it aligns to their religious beliefs. However, locating and verifying Halal-certified foods
can be challenging, especially for Muslim travelers unfamiliar with the local food market.
Muslims ensure Halal foods that ingredients are prepared in accordance with Islamic
Shariah law. Indicators like the Halal emblem have been used to help Muslims identify
Halal food. Unfortunately, many packaged items are not Halal-certified. To address this
issue, this study presents a method for detecting Halal items using deep learning and
machine learning techniques. The purpose is to determine if an unknown product is Halal
(legal) or Haram (Illegal) based on its ingredients. The suggested system examines
packaged food product images and identifies the ingredients using the Yolo v5 algorithm.
The text on the images of the ingredients is then recognized using optical character
recognition (OCR). Various machine learning algorithms, artificial neural networks, and
fuzzy interference rule are applied to determine the status of the food. The final outcome is
to categorize Halal and Haram food products accurately. This approach has the potential to
assist Muslim consumers in identifying Halal-certified products quickly and efficiently,
particularly when traveling to new locations or encountering unfamiliar products. Using
intelligent technology; this study presents a new and innovative technique for detecting
Halal food. The result shows that the suggested approach is effective and it might be a
useful tool for Muslim consumers in ensuring that the things they buy are compatible with
their religious views | en_US |
