DOI : https://doi.org/10.25077/jitce.5.02.93-106.2021

Sistem Pendeteksi Gejala Awal Tantrum Pada Anak Autisme Melalui Ekspresi Wajah Dengan Convolutional Neural Network

Nefy Puteri Novani (1), Dini Ramadhani Salsabila (2), Ratna Aisuwarya (3), Lathifah Arief (4), Nelia Afriyeni (5)
(1) Universitas Andalas
(2) Universitas Andalas
(3) Universitas Andalas
(4) Universitas Andalas
(5) Universitas Andalas
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How to cite (JITCE) :
Novani, N. P., Salsabila, D. R., Aisuwarya, R., Arief, L., & Afriyeni, N. (2021). Sistem Pendeteksi Gejala Awal Tantrum Pada Anak Autisme Melalui Ekspresi Wajah Dengan Convolutional Neural Network. JITCE (Journal of Information Technology and Computer Engineering), 5(02), 93–106. https://doi.org/10.25077/jitce.5.02.93-106.2021
Citation Format :
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%%.
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