Implementasi YOLO untuk Deteksi Jenis Pakaian dan ResNet untuk Klasifikasi Musim Penggunaan Berdasarkan Citra Digital
Abstract
Abstrak — Perkembangan teknologi telah meningkatkan variasi jenis pakaian yang beredar di masyarakat, menimbulkan tantangan dalam pengelompokan dan rekomendasi pakaian secara otomatis. Penelitian ini bertujuan untuk mengembangkan sistem deteksi jenis pakaian dan klasifikasi musim penggunaannya menggunakan metode You Only Look Once (YOLO) dan Residual Network (ResNet). Proses penelitian meliputi studi literatur, pengumpulan dan pre processing data, pelatihan model YOLO untuk deteksi objek, serta model ResNet untuk klasifikasi musim (Fall, Spring, Summer & Winter). Hasil penelitian menunjukkan bahwa YOLOv8n dengan 100 Epoch mampu mencapai mAP sebesar 0.807, sedangkan ResNet18 menghasilkan akurasi klasifikasi sebesar 83.68%. Sistem ini berpotensi diterapkan dalam sistem rekomendasi fashion berbasis web.
Kata kunci— YOLO, ResNet, deteksi objek, klasifikasi musim, pakaian, citra digital
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