Evaluasi Kinerja Arsitektur Ringan YOLOv11n dalam Deteksi Kecacatan Fisik Cangkang Telur untuk Pendukung Sistem Penjaminan Mutu
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Caesar Gian Indrarizky
*
Telkom University, Bandung,
Indonesia
- Figo Mandala Telkom University, Bandung, Indonesia
- Aisy Hafidzah Fadlillah Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
Kecacatan fisik pada cangkang telur dapat menurunkan kualitas produk dan meningkatkan risiko kontaminasi selama proses distribusi maupun konsumsi. Proses inspeksi yang masih dilakukan secara manual memiliki keterbatasan dalam hal konsistensi dan efisiensi, terutama pada skala produksi yang besar. Penelitian ini bertujuan untuk mengem-bangkan model deteksi kecacatan cangkang telur menggunakan arsitektur YOLOv11 guna mendukung proses quality assurance secara otomatis. Dataset yang digunakan terdiri atas 968 citra telur dengan dua kelas, yaitu Damaged dan Normal, serta 1.294 anotasi objek yang telah dibagi ke dalam data pelatihan, validasi, dan pengujian. Model dilatih menggunakan skema 5-Fold Cross Validation dan dievaluasi menggunakan metrik precision, recall, F1-Score, mAP50, dan mAP50-95. Hasil penelitian menunjukkan bahwa model memperoleh rata-rata precision sebesar 0,95, recall sebesar 0,93, F1-Score sebesar 0,94, mAP50 sebesar 0,96, dan mAP50-95 sebesar 0,92. Selain itu, model mampu mendeteksi objek telur normal maupun telur cacat dengan performa yang tinggi pada berbagai kondisi citra. Hasil tersebut menunjukkan bahwa YOLOv11 memiliki potensi yang baik untuk diterapkan pada sistem inspeksi kualitas telur secara otomatis.
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