Auto Halal detection products based on euclidian distance and cosine similarity

Although Indonesia is the world the world's most populous Muslim-majority country, the number of halal-certified products in Indonesia is only 20% of the products on the Indonesian market. Halal certification is voluntary as such there are many food products which are halal but are not certified as halal. In principle, these food products may have similar halal ingredients with halal-certified products. In this study, we build a system that can compare products that have not been certified halal with halal certified products based on its ingredients. The food products are collected from Open Food Facts, Institute For Foods, Drugs, And Cosmetics Indonesian Council Of Ulama (LPPOM MUI) and our halal system. As of this paper writing, the halal-certified products are obtained from LPPOM MUI. The system uses the Euclidean Distance and Cosine Similarity that generate top-5 similar products. Those two similarity calculations are based on Term Frequency-Inverse Entity Frequency weighting function. The weighting function calculates the frequency of a term on a product name and ingredients. If a similarity value of a product with no halal certification and a halal-certified product is higher than 75%, then the former could be indicated as a halal product. In the end, the system can give a recommendation of unknown products from a related pool of halal-certified products based on similarity of product composition. Cosine similarity accuracy is higher than Euclidean Distance and MoreLikeThis accuracy. Cosine similarity gets the highest precision because the cosine similarity is based on the vector angle of the term in a product.