HARIS, MUHAMMAD IMTIYAZ NURDIANSYAH (2023) IMPLEMENTATION OF DEEP LEARNING ALGORITHM IN WASTE CLASSIFICATION USING YOLOV5S REAL-TIME OBJECT DETECTION. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Ineffective waste management may have a negative influence on the surrounding environment, which may also prevent it from functioning at its best. a more cuttingedge approach to trash management may be possible with the assistance of item detection. This research study makes a proposal for the use of image processing YOLO (You Only Look Once) deep learning technology is used to determine the categorization of many types of rubbish objects. Garbage can be deconstructed more reliably and quickly if a classification mechanism is in place. It is well known that YOLOv5s is capable of providing rapid real-time object identification, which enables it to identify different types of trash according to categorization. Keywords: Waste management, Deep learning, Object detection, Classification Pengelolaan sampah yang tidak efektif dapat memberikan pengaruh negatif terhadap lingkungan sekitar, yang juga dapat mencegahnya berfungsi secara maksimal. pendekatan yang lebih mutakhir untuk pengelolaan sampah dimungkinkan dengan bantuan deteksi barang. Studi penelitian ini membuat proposal penggunaan teknologi pengolahan citra YOLO (You Only Look Once) deep learning yang digunakan untuk menentukan kategorisasi berbagai jenis objek sampah. Sampah dapat didekonstruksi dengan lebih andal dan cepat jika ada mekanisme klasifikasi. Diketahui dengan baik bahwa YOLOv5s mampu memberikan identifikasi objek real-time yang cepat, yang memungkinkannya mengidentifikasi berbagai jenis sampah berdasarkan kategorisasi. Kata kunci: Pengelolaan limbah, Pembelajaran mendalam, Deteksi objek, Klasifikasi
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