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All Journal Mimbar PGSD Undiksha Jurnal NERS Jurnal Keperawatan Sriwijaya Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Keperawatan MAHESA : Malahayati Health Student Journal Jurnal Ilmu Kesehatan Masyarakat
Arie Kusumaningrum
Program Studi Ilmu Keperawatan Fakultas Kedokteran Universitas Sriwijaya
Computer Science & IT Dentistry Education Engineering Health Professions Nursing Public Health

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi Melalui Citra Wajah Dengan YOLO Tomy Abuzairi; Nurdina Widanti; Arie Kusumaningrum; Yeni Rustina
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (537.602 KB) | DOI: 10.29207/resti.v5i4.3184

Abstract

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%.
Co-Authors Adelia Pradita Annis Pertiwi Annis Pertiwi Antarini Idriansari, Antarini Arfilia Wijayanti, Arfilia Asmaripa Ainy Devi Ekasafitri Dwi Cahyati Eka Yulia Fitri Erlina Lina Julia Erlina Lina Julia, Erlina Lina Eva Citra Dewi Eva Citra Dewi Hikayati Hikayati Jum Natosba Jum Natosba, Jum Mutia Dwi Sagita Nurdina Widanti Nurna Ningsih Putri Widita Muharyani Rofian, Rofian Rofian, Rofian Sri Maryatun Tiara Septi Arini Tomy Abuzairi Vivop Marti Lengga Vivop Marti Lengga Yeni Rustina Yesica Tria Enggriani