OPTIMASI DETEKSI SAMPAH ORGANIK DAN ANORGANIK MENGGUNAKAN YOLO UNTUK SISTEM MONITORING REAL-TIME PADA LINGKUNGAN OUTDOOR
DOI:
https://doi.org/10.63448/pm9jw281Keywords:
Real-Time Waste Detection, YOLO, Organic and Inorganic Waste, Deep Learning, Outdoor EnvironmentAbstract
Permasalahan sampah di lingkungan outdoor, khususnya di area publik perkotaan, terus meningkat
seiring pertumbuhan populasi dan rendahnya kesadaran masyarakat dalam memilah sampah organik dan
anorganik. Penelitian terdahulu telah mengembangkan berbagai sistem deteksi sampah berbasis
computer vision dan deep learning, namun sebagian besar difokuskan pada lingkungan tertutup atau
kondisi pencahayaan yang terkendali, sehingga performa model seringkali menurun secara signifikan
ketika diterapkan di lingkungan outdoor yang memiliki variasi pencahayaan, latar belakang, dan sudut
kamera yang kompleks. Penelitian ini bertujuan mengoptimalkan deteksi sampah organik dan anorganik
menggunakan algoritma YOLO yang diintegrasikan ke dalam sistem monitoring real-time untuk
lingkungan outdoor. Hasil evaluasi menunjukkan bahwa model YOLOv8s mencapai precision sebesar
96,8%, recall 94,5%, dan mAP@0.5 sebesar 95,7% pada dataset uji, dengan waktu inferensi rata-rata
8,2 milidetik per frame. Sistem monitoring real-time yang dibangun berhasil mendeteksi dan
mengklasifikasikan sampah organik dan anorganik secara akurat di berbagai kondisi pencahayaan
outdoor. Penelitian ini menyimpulkan bahwa optimasi model YOLO dengan augmentasi data yang tepat
dan arsitektur yang ringan dapat menghasilkan sistem deteksi sampah yang handal dan efisien untuk
diterapkan pada infrastruktur monitoring lingkungan outdoor secara real-time.
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