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

  • Tomy Abuzairi Departemen Teknik Elektro, Fakultas Teknik, Universitas Indonesia
  • Nurdina Widanti Universitas Indonesia
  • Arie Kusumaningrum Universitas Indonesia
  • Yeni Rustina Universitas Indonesia
Keywords: 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 a sad mAP value of 77.8%, neutral 76.7%, in pain 68.9%. With a precision value of 71.4%, recall 62.5% and f1-score 66.6%. The average value of Confidence is 53.57%.


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Miftakhurrokhmat, R. A. Rajagede, and R. Rahmadi, “Presensi Kelas Berbasis Pola Wajah, Senyum dan Wi-Fi Terdekat dengan Deep Learning,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 31–38, 2021.

R. mulyadi Yusni and Zaini, “Identifikasi Pengenalan Wajah Perokok Menggunakan Metode Principal Component Analysis,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 5, pp. 892–898, 2020.

L. Farokhah, “Implementasi Convolutional Neural Network untuk Klasifikasi Variasi Intensitas Emosi pada Dynamic Image Sequence,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 6, pp. 1070–1076, 2020.

S. S. Panna and Betrisandi, “Klasifikasi Kelompok Usia Melalui Citra Wajah Berbasis Image Texture Analysis pada Sistem Automatic Video Filtering,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 429–434, 2019.

Y. Kristian, “Analisa citra wajah bayi untuk deteksi nyeri dan tangis menggunakan multi stage classification dan deep learning,” 2018.

H. Kahsay, “Assessment and treatment of pain in pediatric patients,” Curr. Pediatr. Res., vol. 21, no. 1, pp. 148–157, 2017.

Starship, “paediatric pain assessment”, 2019.

M. J. Hockenberry, C. C. Rodgers, D. Wilson, "Wong's essentials of pediatric nursing", Elsevier Health Sciences, 2021.

R. W. Hall and K. J. S. Anand, “Physiology of Pain and Stress in the Newborn,” Neoreviews, vol. 6, no. 2, pp. e61–e68, 2005.

G. R. Lestari and T. Abuzairi, “Design of Portable Galvanic Skin Response Sensor for Pain Sensor,” Proceeding - ICoSTA 2020 2020 Int. Conf. Smart Technol. Appl. Empower. Ind. IoT by Implement. Green Technol. Sustain. Dev., pp. 5–9, 2020.

J. J. J. Braithwaite et al., “A Guide for Analysing Electrodermal Activity (EDA) Skin Conductance Responses (SCRs) for Psychological Experiments,” pp. 1–42, 2013, [Online].

A. Tjahya, “Penilaian nyeri,” Academia, pp. 133–163, 2017, [Online].

C. Kit, “Using Pediatric Pain Scales,” 2013, [Online].

D. K. Wati, A. Pudjiadi, and A. Latief, “Validitas Skala Nyeri Non Verbal Pain Scale RevisedSebagai Penilai Nyeri di Ruang Perawatan Intensif Anak,” Sari Pediatr., vol. 14, no. 1, p. 8, 2016.

D. M. Jensen, “towardd automated pain detection in children using facial and electrodermal,” Physiol. Behav., vol. 176, no. 1, pp. 1570–1573, 2018.

M. W. Sullivan and M. Lewis, “Emotional expressions of young infants and children a practitioner’s primer,” Infants Young Child., vol. 16, no. 2, pp. 120–142, 2003.

R. A. Rajagede, “Modul CNN With Pytorch 0.4,” 2018, [Online].

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016.

github, “YoloV3,” 2021.

R. Zhi, G. Zamzmi, D. Goldgof, T. Ashmeade, T. Li, and Y. Sun, “Infants’ pain recognition based on facial expression: Dynamic hybrid descriptions,” IEICE Trans. Inf. Syst., vol. E101D, no. 7, pp. 1860–1869, 2018.

G. Zamzmi et al., “Convolutional Neural Networks for Neonatal Pain Assessment,” IEEE Trans. Biometrics, Behav. Identity Sci., vol. 1, no. 3, pp. 192–200, 2019.


How to Cite
Abuzairi, T., Nurdina Widanti, Arie Kusumaningrum, & Yeni Rustina. (2021). Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi Melalui Citra Wajah Dengan YOLO. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 624 - 630.
Artikel Rekayasa Sistem Informasi