Implementasi Model Deep Learning Pada Sistem Deteksi dan Klasifikasi Kualitas Batang Tebu untuk Optimasi Penentuan Kualitas
Abstract
Industri gula nasional mengalami penurunan produksi sebesar 7,01% pada tahun 2023, salah satunya disebabkan oleh rendahnya efisiensi pascapanen akibat proses klasifikasi mutu batang tebu yang masih dilakukan secara manual. Proses ini menimbulkan inkonsistensi, potensi konflik antara petani dan petugas lapangan, serta peningkatan biaya operasional. Penelitian ini mengembangkan sistem klasifikasi mutu batang tebu berbasis deep learning menggunakan pendekatan dua tahap. Tahap pertama menggunakan YOLOv11 untuk mendeteksi batang tebu, sedangkan tahap kedua menggunakan arsitektur EfficientNet (B0–B3) untuk mengklasifikasikan mutu ke dalam lima kategori (A–E). Dataset citra diperoleh dari jalur produksi PT Sinergi Gula Nusantara dan diproses melalui tahapan Knowledge Discovery in Database (KDD), meliputi data preprocessing, augmentasi, resizing, dan splitting. Hasil evaluasi menunjukkan bahwa YOLOv11 mencapai akurasi 93,5%, precision 95,7%, recall 94,4%, [email protected] sebesar 97,8%, dan [email protected]:0.95 sebesar 89,4%. Sementara itu, EfficientNet-B2 menghasilkan akurasi klasifikasi tertinggi sebesar 88,57% setelah proses fine-tuning. Sistem yang dikembangkan mampu beroperasi pada kondisi visual yang kompleks dan dinamis, serta memberikan hasil klasifikasi yang konsisten. Studi ini menunjukkan potensi teknologi deep learning dalam mendukung otomasi dan peningkatan objektivitas proses penilaian mutu di industri agroindustri.
Kata kunci— Deep Learning, EfficientNet, KDD, Klasifikasi Kualitas Batang Tebu, YOLOv11
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