Implementasi YOLO (You Only Look Once) untuk Klasifikasi Kesegaran Daging Ayam Berdasarkan Citra Digital - Pasya, Joanna Andini Prabaningrum; Fachrie, Muhammad (Journal of Information System Research (JOSH), 2026)
Manual assessment of chicken meat freshness is prone to subjectivity, limited sensory perception, and inconsistent environmental conditions, leading to inaccuracy in freshness determination and potential risks to consumer health and safety. The quality of chicken meat that is not properly maintained can negatively impact consumer health and reduce trust in food businesses. This study aims to develop a chicken meat freshness classification system using the Convolutional Neural Network (CNN) algorithm with the YOLOv8 model approach. The dataset of fresh and non-fresh chicken meat images was obtained through manual documentation and processed using Roboflow platform for augmentation and data splitting. The CNN model was trained using YOLOv8 with a configuration of 50 epochs and an image size 416x416 pixels. The model was then implemented into a web-based application system using the Streamlit framework. The classification result are presented visually (bounding box and class label), along with an automatic conclusion and confidence score that the YOLOv8-based CNN model can accurately classify chicken meat freshness with an accuracy of 98,71%, demonstrating its potential as a rapid and objective food quality assessment tool.