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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Science and Information Organization
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29635/2/02719031120251356382404.pdf http://eprints.utem.edu.my/id/eprint/29635/ https://thesai.org/Downloads/Volume16No10/Paper_67-Recent_Integrating_Machine_Learning_and_Malay_Arabic_Lexical_Mapping.pdf |
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| Summary: | 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 TFIDF, 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. |
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