Training on the Importance of Halal Food Certification

Abstract

This study aims to evaluate the effectiveness of online training on the theme of AI Application for Halal Food Detection in improving public halal literacy, particularly regarding the application of artificial intelligence (AI) technology to detect non-halal food ingredients. The study used a qualitative participatory approach with a case study method, involving 150 participants from various regions in Indonesia and abroad. Data were collected through observation, digital documentation, and pre- and post-tests. The training materials covered the concept of halal and haram, the challenges of detecting ingredients such as pork gelatin, and the use of AI methods such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN) based on FTIR/NIR spectrum. The pre-test results showed a high understanding of general concepts (99.1%) but low understanding of technical aspects such as CNN (71.3%). After the training, the average score increased from 93.09 to 95.45, reflecting a significant increase in technical understanding. The findings suggest that case study- and technology-based training can bridge the halal literacy gap and strengthen community capacity in ensuring halal consumption. This study recommends the integration of digital technology in the national halal literacy program as well as the development of advanced training based on practical workshops with the integration of technologies such as IoT and blockchain.