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All Journal Jurnal Psikologi Jurnal RAP JURNAL PSIKOLOGI UDAYANA Jurnal Psikologi Jurnal Antropologi: Isu-Isu Sosial budaya MEDIAPSI Jurnal Ecopsy JurTI (JURNAL TEKNOLOGI INFORMASI) JURNAL PENDIDIKAN TAMBUSAI Psychopolytan (Jurnal Psikologi) Journal of Information Technology and Computer Engineering Jurnal Ilmu Perilaku Jurnal Hilirisasi IPTEKS (JHI) JOMIS (Journal of Midwifery Science) Psibernetika Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks MANAJEMEN Jurnal Pendidikan Dasar dan Sosial Humaniora Jurnal Psikologi Insight JIPP
Nelia Afriyeni
Program Studi Psikologi, Fakultas Kedokteran, Universitas Andalas
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Medicine & Pharmacology Neuroscience Public Health Social Sciences Veterinary Other

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Journal : Journal of Information Technology and Computer Engineering

 1 
Sistem Pendeteksi Gejala Awal Tantrum Pada Anak Autisme Melalui Ekspresi Wajah Dengan Convolutional Neural Network Nefy Puteri Novani; Dini Ramadhani Salsabila; Ratna Aisuwarya; Lathifah Arief; Nelia Afriyeni
JITCE (Journal of Information Technology and Computer Engineering) Vol 5 No 02 (2021): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.02.93-106.2021

Abstract

Tantrums are outbursts of anger and they can occur at any age. An attitude tantrum or what is commonly referred to as a temper tantrum is a child's outburst of anger that often occurs when a child shows negative behavior. Emotional outbursts of tantrums that occur in children with autism are not only to seek the attention of adults, but also as an outlet for a child's feelings for parents and those around him on a whim or feeling he is feeling, but the child cannot convey it. For this reason, researchers propose a system for detecting early symptoms of tantrums in children with autism through facial expressions with CNN. The CNN method is one of the deep learning methods that can be used to recognize and classify an object in a digital image. Then the preprocessing process is carried out using labeling on the data. Then the CNN architecture is designed with input containing 48x48x1 neurons. The data was then trained using 357 epochs with an accuracy rate of 72.67%%. Then tested using test data for children with autism to get an average accuracy value of 72.67%%.
Sistem Pendeteksi Gejala Awal Tantrum Pada Anak Autisme Melalui Ekspresi Wajah Dengan Convolutional Neural Network Novani, Nefy Puteri; Salsabila, Dini Ramadhani; Aisuwarya, Ratna; Arief, Lathifah; Afriyeni, Nelia
JITCE (Journal of Information Technology and Computer Engineering) Vol. 5 No. 02 (2021)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.02.93-106.2021

Abstract

Tantrums are outbursts of anger and they can occur at any age. An attitude tantrum or what is commonly referred to as a temper tantrum is a child's outburst of anger that often occurs when a child shows negative behavior. Emotional outbursts of tantrums that occur in children with autism are not only to seek the attention of adults, but also as an outlet for a child's feelings for parents and those around him on a whim or feeling he is feeling, but the child cannot convey it. For this reason, researchers propose a system for detecting early symptoms of tantrums in children with autism through facial expressions with CNN. The CNN method is one of the deep learning methods that can be used to recognize and classify an object in a digital image. Then the preprocessing process is carried out using labeling on the data. Then the CNN architecture is designed with input containing 48x48x1 neurons. The data was then trained using 357 epochs with an accuracy rate of 72.67%%. Then tested using test data for children with autism to get an average accuracy value of 72.67%%.
Co-Authors Anip Febtriko, Anip Armen Danur Armen Danur Aulia, Wina Chintia Aulia Rahmah Dinda Marisa Dini Ramadhani Salsabila Diny Amenike Dwi Puspasari Erwin Erwin Eryandra, Alvin Feri Fernandes Fitria Rahmi Helyuza Ridwan Helyuza Ridwan Hermalinda Hermalinda Karlina Agustin Lathifah Arief, Lathifah Liliyana Sari Maya Suryani Mutia Skunda Ramadani Nabilah, Farah Salsabil Nasa, Amatul Firdausa Nefy Puteri Novani Nila Anggreiny, Nila Nola Chentya Yuni Novani, Nefy Puteri NURUL AZIZAH Pratama, Weno Puteri Rahmadina Rani Armalita Ratna Aisuwarya Resti Wahyuni Rico Dwi Putra Anggara Rika Sarfika Rizal Kurniawan Rozi Sastra Purna Salsabila, Dini Ramadhani Sarry, Septi Mayang Sartana Sartana Sartana Sartana, Sartana Siska Oktari Subandi Subandi Suryani, Fadila Susanti, Meria Tri Rahayuningsih Tri Rahayuningsih Tri Rahayuningsih Yantri Maputra Yuni Dwi Rahma