PENERAPAN KLASIFIKASI CITRA PADA IDENTIFIKASI OBJEK DENGAN PAKAIAN SAFETY MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DI PT JAYATAMA SAFETINDO.
DOI:
https://doi.org/10.33884/comasiejournal.v12i2.9647Keywords:
Convolutional Neural Network, Deep Learning, TensorflowAbstract
Construction workers are essential to project execution but face high risks of workplace accidents, often caused by human factors. Advances in artificial intelligence, particularly image processing, provide opportunities to improve the detection of personal protective equipment (PPE), which is currently checked manually and inefficiently. PPE, such as
gloves, helmets, and safety shoes, is vital for worker safety but is often neglected due to discomfort. This study uses Convolutional Neural Network (CNN) algorithms to classify images and verify PPE usage at construction sites. CNN processes spatial information through layers for feature extraction, dimension reduction, and classification. A previous
study with Faster R-CNN achieved accuracies of 72.83% with TensorFlow and 88.07% with Faster R-CNN. Using a dataset of 200 images, this research, conducted at PT JAYATAMA SAFETINDO, applies Python and TensorFlow to improve PPE detection accuracy. The results aim to support safer workplaces, enhance productivity, and advance AI applications in safety and identification.
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