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Segmentasi Berbasis Warna Untuk Pengelompokan Kualitas Cacing Anc Menggunakan Yolov8
Abstract
Mengembangkan metode otomatisasi dalam pengelompokan kualitas cacing African Night Crawler (ANC) menggunakan model YOLOv8 yang didukung oleh segmentasi berbasis warna. Metode manual yang selama ini digunakan dalam menilai kualitas cacing sering kali memakan waktu dan cenderung tidak konsisten, sehingga pendekatan berbasis teknologi diperlukan untuk meningkatkan efisiensi dan akurasi. Penelitian ini dimulai dengan pengumpulan dataset gambar cacing ANC, yang kemudian dianotasi berdasarkan tiga kelas utama, yaitu merah, putih, dan putih-biru. Dataset ini diproses melalui langkah-langkah preprocessing untuk memastikan kualitas data yang konsisten, kemudian dibagi menjadi training set (80%), validation set (15%), dan test set (5%). Model YOLOv8 diterapkan untuk mendeteksi dan mengklasifikasikan objek, dengan arsitektur yang terdiri dari Backbone, Neck, dan Head yang dirancang untuk mengoptimalkan deteksi multi-skala. Hasil penelitian menunjukkan bahwa model YOLOv8 memiliki performa yang sangat baik, dengan mean Average Precision (mAP) rata-rata sebesar 93.8% pada training set, 94.0% pada validation set, dan 95.0% pada test set. Nilai precision mencapai 95.6% pada training set, sementara recall mencatat 85.4%, menghasilkan F1-Score sebesar 90.3%. Hasil ini menunjukkan bahwa model tidak hanya akurat dalam mendeteksi objek yang relevan tetapi juga memiliki sensitivitas yang baik terhadap variasi data. Kesimpulannya, penggunaan YOLOv8 untuk segmentasi berbasis warna pada cacing ANC memberikan solusi yang efisien dan akurat dalam mengklasifikasikan kualitas cacing. Selain mendukung otomatisasi dalam industri peternakan, penelitian ini juga membuka peluang penerapan lebih lanjut dalam sektor agrikultur lainnya, dengan rekomendasi untuk meningkatkan generalisasi model pada variasi data yang lebih kompleks di kondisi dunia nyata.
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DOI: http://dx.doi.org/10.26798/jiko.v9i1.1779
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