Keywords
Deteksi Objek
Yolov8
How to Cite
Abstract
Menjaga kualitas daging ayam sangat penting untuk keamanan pangan dan kesehatan konsumen. Metode inspeksi tradisional seringkali subjektif dan tidak efisien. Studi ini mengusulkan sistem deteksi kualitas daging ayam otomatis yang memanfaatkan algoritma deteksi objek YOLOv8 yang terintegrasi ke dalam aplikasi web berbasis Flask. Dataset terdiri dari tiga kategori yaitu ayam segar, ayam tiren, dan none. Model dilatih menggunakan Google Colaboratory dan dievaluasi melalui metrik presisi, recall, dan mean Average Precision (mAP). Model YOLOv8 menunjukkan kinerja tinggi, dengan presisi 0,975, recall 0,92, mean Average Precision (mAP@0.5) 0,972, dan mean Precision (mAP@0.5:0.95) 0,84. Sistem yang dikembangkan terbukti mampu mendeteksi kualitas daging ayam secara akurat dan efisien. Inovasi ini diharapkan dapat meningkatkan keamanan pangan serta membantu konsumen dan pelaku industri dalam memastikan mutu produk secara lebih objektif dan praktis.
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