Klasifikasi Sampah Plastik di Laut Menggunakan Algoritma Faster R-CNN (Studi Kasus Tanjungpinang) - Novrizal Fattah Fahmitra; Farel Putra Albana; Muhamad Radzi Rathomi (Jurnal Sains dan Informatika, 2026)
The global environment is facing a serious challenge in the form of increasing plastic waste. Indonesia is one of the world’s largest producers of plastic waste, and Tanjungpinang City faces a similar issue, generating approximately 100–150 tons of waste per day, predominantly plastic waste. A significant portion of this waste originates from marine environments, while its management remains limited by inadequate facilities and technological resources. Therefore, a waste detection solution is needed to improve the efficiency of plastic waste management in the region.. This study aims to evaluate the detection performance of a Faster R-CNN model using pre-trained weights with a ResNet-50 Feature Pyramid Network (FPN) backbone. Experimental results show an mAP@0.50:0.95 of 59.55% and an mAP@0.50 of 82.64%. Further evaluation for each plastic waste category resulted in accuracies of 90% for plastic bottles, 88% for food wrappers, 78% for beverage wrappers, and 86% for plastic bags. These findings indicate that the pre-trained Faster R-CNN model with a ResNet-50 FPN backbone is capable of effectively detecting and classifying different types of plastic waste in marine environments. Consequently, this model has the potential to support more effective waste management and sorting processes in the coastal waters of Tanjungpinang City.