FT PENERAPAN ARSITEKTUR YOU ONLY LOOK ONCE (YOLO11) UNTUK DETEKSI TINGKAT KEMATANGAN BUAH TOMAT
Abstract
Penyortiran manual buah tomat di Indonesia menjadi salah satu penghambat efisiensi dalam rantai pasok pertanian karena sifatnya yang subjektif, memakan waktu, dan mahal. Kesenjangan antara penurunan produksi dan peningkatan konsumsi menuntut adanya inovasi teknologi untuk mengatasi kerugian pascapanen. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem deteksi tingkat kematangan buah tomat secara otomatis dan real-time dengan mengimplementasikan algoritma You Only Look Once versi terbaru, YOLO11. Metode penelitian meliputi beberapa tahapan utama: pengumpulan 1088 data citra, persiapan data meliputi anotasi dan augmentasi hingga 2610 citra, serta pelatihan empat varian model YOLO11 (n, s, m, dan l) menggunakan platform Google Colab untuk mengklasifikasikan tiga kategori kematangan untuk mentah, setengah matang, dan matang. Hasil evaluasi menunjukkan bahwa model YOLO11m memiliki performa paling optimal dengan nilai precision 85.9%, recall 84.1%, dan mAP50 tertinggi sebesar 89.1%. Model ini berhasil diuji pada citra baru dan input video melalui webcam dan mendapatkan 16 fps. Penelitian ini bertujuan untuk menyediakan solusi dengan teknologi yang efektif untuk otomatisasi proses penyortiran tomat dan mengoptimalkan efisiensi pada sektor pertanian.
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