Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi
Melalui Citra Wajah Dengan YOLO

Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi
Melalui Citra Wajah Dengan YOLO

NVDIA Jetson Nano Developer Kit, Pain, Baby, YOLO, PyTorch, CNN, Facial Expresion.

Pain in a baby is difficult to detect is because the method for detecting pain is self-reporting even though babies themselves
still cannot describe the pain verbally, then by observing changes in behavior in the form of facial expressions. Statistically, it
is also recorded that about 80% of the world's population pays less attention to pain assessment, especially for children, even
though this pain gives children a bad experience so that it can interfere with pain responses in the future or psychological
trauma. Based on these problems, a prototype system was made using the NVIDIA Jetson Nano Developer kit to help detect
pain, especially in infants 0-12 months by using the Convolutional Neural Network (CNN) model with the PyTorch framework
and the You Only Look Once (YOLO) algorithm with three detection classification is sad, neutral and sick. From the results of
the study, it was found that the YOLO algorithm was able to detect the three classifications with [email protected] value of sad 97,9%,
neutral 99,2%, pain 96,9%, model accuracy 70%. The result of random check and the data from Puskesam Imogiri 1 have
accuracy value 90%.

Tomy Abuzairi1
, Nurdina Widanti2
, Arie Kusumaningrum3
, Yeni Rustina4

Universitas Indonesia

20-08-2021

Fajar bagus W

PDF

Indonesia

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