IMPLEMENTASI ALGORITMA FASTER R-CNN DALAM DETEKSI AKTIVITAS MEROKOK DI LINGKUNGAN KAMPUS - Istanto, Nazla Abay Daud; Bagus Satrio Waluyo Poetro (Jurnal Rekayasa Sistem Informasi dan Teknologi, 2025)
The implementation of smoke-free area regulation in campus environment faces challenges in terms of supervision and enforcement. This study aims to determine the performance of deep learning-based object detection algorithm, namely Faster R-CNN, in detecting smoking activity in the campus area of ​​Universitas Islam Sultan Agung (UNISSULA). The dataset used consists of 1935 annotated images of smoking activity obtained from Roboflow, with data division of 85% training and 15% validation. The model was trained using Google Colab and tested based on evaluation metrics such as accuracy, precision, recall, and f1-score. The results show that Faster R-CNN has superior performance with the best evaluation value reaching 100% at a threshold of 0.5. These findings conclude that Faster R-CNN is suitable for use in a smoking activity detection system in a campus environment, especially in the context of detection accuracy and consistency.