Implementasi YOLOV11 untuk Deteksi Multi-Objek Kesegaran Ikan Cakalang Beku
Abstract
Memastikan kesegaran ikan merupakan tantangan penting dalam industri perikanan modern, terutama untuk komoditas bernilai tinggi seperti Cakalang. Makalah ini mengusulkan sistem deteksi multi-objek secara real-time menggunakan model YOLOv11 khusus untuk secara otomatis mengklasifikasikan kualitas kesegaran ikan Cakalang (Katsuwonus pelamis) beku dan tidak beku. Tiga varian YOLOv11, yaitu YOLOv11S, YOLOv11M, dan YOLOv11L, dilatih dan divalidasi pada set data gambar yang telah dianotasi. YOLOv11S mencapai akurasi pengujian sebesar 88,4%, akurasi validasi sebesar 90,1%, dan akurasi pelatihan sebesar 88,4%. YOLOV11M mencapai kinerja yang lebih tinggi, dengan akurasi pengujian 92,9%, akurasi validasi 94,8%, dan akurasi pelatihan 85,7%. Hasil terbaik diperoleh dari YOLOv11L, yang mencapai akurasi pengujian sebesar 91,5%, akurasi validasi 94,9%, dan akurasi pelatihan 83,7% Temuan ini menunjukkan bahwa YOLOv11L menawarkan kinerja tertinggi, sementara YOLOv11M dan YOLOv11S juga menunjukkan hasil yang kompetitif. Pendekatan berbasis YOLOv11 yang diusulkan menunjukkan potensi deteksi kesegaran ikan secara real-time dalam sistem kontrol kualitas otomatis, yang bertujuan untuk mengurangi kerugian pascapanen dan meningkatkan daya saing produk makanan laut Indonesia di tingkat global.
Kata Kunci-YOLOv11, pembelajaran mendalam, deteksi objek, waktu nyata, kesegaran ikan
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