Image Identification System for Beef and Pork Using a Convolutional Neural Network - Nadiyah Salsabila Fauzi; Irma Salamah; Irawan Hadi (LPPM Universitas Islam 45 Bekasi, 2024)
In the modern era, assurance of the halalness of meat products has become a fundamental need for Indonesian Muslims, as awareness and sensitivity towards the consumption of halal products increases. This has led to the development of innovative solutions to ensure the authenticity of beef and distinguish it from pork. This research presents an Android-based meat image identification tool that relies on the Convolutional Neural Network (CNN) algorithm to process and analyze images. The research includes hardware design, deep learning model with CNN algorithm, and Android application for real-time integration of detection results. This tool is equipped with an LCD screen and speaker to display identification results. The results show the accuracy of the CNN model reaches 99% in distinguishing beef and pork on the test dataset. In real-time testing of the tool using fresh beef and pork samples, the system achieved 92% accuracy, demonstrating good performance under practical conditions. The system provides a reliable and practical solution for consumers to verify the type of meat, while contributing to efforts to ensure the halalness of food products in society.