Abstract

Halal cuisine is an essential requirement for every Muslim. However, the number of halal-certified products are less than number of non-halal-certified products. Therefore, we determine whether a non-halal-certified food product is similar to halal-certified products based on the relationship of shared ingredients using machine learning and knowledge graphs. This study compares the product from an online grocery website with the Halal Linked Open Data (LOD) dataset using the Naive Bayes, KNN and Random Forest methods. Features extraction using several graph algorithms: Common Neighbors, Preferential Attachment, Total Neighbors, Label Propagation and Louvain. The results of the performance calculation are assessed from accuracy, precision, recall and F1-Score. Random Forest performance surpasses other machine learning performance. We found that performing link prediction can be done with high accuracy rate by using the traditional machine learning method and can be optimized further by performing hyperparameter tuning with large datasets.

Original languageEnglish
Title of host publicationProceedings - ICCTEIE 2023
Subtitle of host publication2023 International Conference on Converging Technology in Electrical and Information Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages65-70
Number of pages6
ISBN (Electronic)9798350370645
DOIs
Publication statusPublished - 2023
Event2nd International Conference on Converging Technology in Electrical and Information Engineering, ICCTEIE 2023 - Hybrid, Bandar Lampung, Indonesia
Duration: 25 Oct 202326 Oct 2023

Publication series

NameProceedings - ICCTEIE 2023: 2023 International Conference on Converging Technology in Electrical and Information Engineering

Conference

Conference2nd International Conference on Converging Technology in Electrical and Information Engineering, ICCTEIE 2023
Country/TerritoryIndonesia
CityHybrid, Bandar Lampung
Period25/10/2326/10/23

Keywords

  • Halal Product
  • Knowledge Graphs
  • Machine learning

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