Recent Integrating Machine Learning and Malay-Arabic Lexical Mapping for Halal Food Classification

The rapid growth of e-commerce has changed the way people engage with businesses, notably in the food industry. For the Muslim community, guaranteeing Halal conformity in digital transactions is critical. This study provides a comprehensive framework for improving Halal E-Commerce systems that include machine learning, pattern libraries, and multilingual support, specifically in Malay and Arabic. The study examines the role of pattern libraries in designing user-friendly interfaces, as well as lexical mapping strategies for enhancing Malay-Arabic translation accuracy. Natural language processing (NLP) and machine learning are combined to create an application that classifies food items into two categories: Halal or Haram. With an accuracy of 85%, a Random Forest classifier is trained on labeled datasets. Preparing the text, extracting features using TF-IDF, and evaluating the results using precision, recall, and F1-score are all steps in the classification process. To increase classification accuracy, a rule-based approach is also applied to conditional logic and keyword matching. By adjusting the parameters, the model is further improved, leading to strong performance. By taking into account the cultural and linguistic requirements of the Muslim community, multilingual support enhances accessibility and user confidence. The suggested method increases translation accuracy by employing lexical mapping at the word, phrase, and context levels. The paper also assesses several machine learning models, demonstrating that Random Forest outperforms the other methods examined. The findings contribute to the growth of Halal E-Commerce by outlining a systematic strategy to ensure compliance and usability. The proposed system can serve as a platform for future research into AI-driven Halal certification and digital marketplace optimization, blockchain with an e-Commerce framework.