<![CDATA[Plastic bottle waste management has become one of the most pressing environmental issues, especially in countries with high plastic usage rates, such as Indonesia. This research uses the YOLOv5 (You Only Look Once version 5) algorithm to detect plastic bottle waste automatically. The YOLOv5 algorithm was chosen because it has efficient detection performance and high accuracy in small object recognition. The dataset consists of 500 images of plastic bottles obtained through cameras and internet sources. The data is processed through several stages: annotation (bounding box and labeling using Roboflow), split dataset (70% for training, 20% for testing, and 10% for validation), pre-processing (resizing images to 460x460 pixels), and augmentation (adding data variations to improve model performance). Training and evaluation of the YOLOv5 model using the precision metric of 89.8% indicates the ability of the model to accurately identify plastic bottles from the overall prediction, recall of 83.1% indicates the success of the model in detecting the majority of plastic bottles in the test data, and mean average precision (mAP) of 89.2% represents the average precision at various prediction thresholds. Test results on varied bottle image test data obtained detection accuracy between 82%-93%, indicating the model can recognize plastic bottles consistently. Sometimes, this model needs help detecting overlapping picture objects. However, this research proves the potential of the yolov5 algorithm as an automated litter detection solution that will be integrated with a system and support faster and better plastic waste management.]]>
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