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Halal Restaurant Integration Using Bidirectional Recurrent Neural Networks

Author

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  • Islammia, Salsa Putri
  • Rakhmawati, Nur Aini

Abstract

Indonesia, with the most significant Muslim population worldwide, mandates the consumption of halal food. However, many websites, including Google Maps, do not provide information about halal restaurants. Data integration is essential for obtaining comprehensive and accurate information on halal restaurants from diverse sources, such as the Indonesia Halal Product Assurance Agency (BPJPH) and Google Maps. Preprocessing of these two datasets and their labeling using the Jaccard index were conducted. The Bidirectional Recurrent Neural Networks (BRNN) model was constructed using deepmatcher and evaluated using the F1-score metric. The integration of these two datasets resulted in 155 rows of matching pairs of data.

Suggested Citation

  • Islammia, Salsa Putri & Rakhmawati, Nur Aini, 2024. "Halal Restaurant Integration Using Bidirectional Recurrent Neural Networks," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 15(01), January.
    • Handle: RePEc:ags:ijofsd:346728
    DOI: 10.22004/ag.econ.346728
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