TY - GEN
T1 - USK-FEMO
T2 - 2nd International Conference on Computer System, Information Technology, and Electrical Engineering, COSITE 2023
AU - Muhajir, Muhajir
AU - Oktiana, Maulisa
AU - Muchtar, Kahlil
AU - Fitria, Maya
AU - Akhyar, Akhyar
AU - Pratama, Muhammad Dandy
AU - Lin, Chih Yang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Emotions play an essential role in the learning process and have an impact on how the learning process is eventually carried out. Facial expressions can be used to visually identify a person's emotions. Along with the advancement of computer vision and deep learning techniques, the study of human-computer interaction is increasingly focusing on the recognition of facial expressions. One of the main issues is the availability of sufficient datasets, especially for students. This study examined the deep learning architecture for face emotion classification. In addition, this research also introduces a new emotional dataset acquired from the junior high school student at SMP Negeri 1 Darul Imarah, Aceh Besar Regency, Indonesia. This dataset contains five classes that include the emotions of happiness, sadness, anger, surprise, and boredom. The dataset was then tested using the Mobile-Net architecture, the highest accuracy was achieved with a learning rate of 0.0001% of 88.492%. %. The dataset can be explored via the link https://muhajir2111.github.io/USK-FEMO-DATASET/.
AB - Emotions play an essential role in the learning process and have an impact on how the learning process is eventually carried out. Facial expressions can be used to visually identify a person's emotions. Along with the advancement of computer vision and deep learning techniques, the study of human-computer interaction is increasingly focusing on the recognition of facial expressions. One of the main issues is the availability of sufficient datasets, especially for students. This study examined the deep learning architecture for face emotion classification. In addition, this research also introduces a new emotional dataset acquired from the junior high school student at SMP Negeri 1 Darul Imarah, Aceh Besar Regency, Indonesia. This dataset contains five classes that include the emotions of happiness, sadness, anger, surprise, and boredom. The dataset was then tested using the Mobile-Net architecture, the highest accuracy was achieved with a learning rate of 0.0001% of 88.492%. %. The dataset can be explored via the link https://muhajir2111.github.io/USK-FEMO-DATASET/.
KW - Deep Learning
KW - Emotion Classification
KW - Emotion Dataset
KW - Mobile-Net architecture
UR - http://www.scopus.com/inward/record.url?scp=85173527732&partnerID=8YFLogxK
U2 - 10.1109/COSITE60233.2023.10249834
DO - 10.1109/COSITE60233.2023.10249834
M3 - 會議論文篇章
AN - SCOPUS:85173527732
T3 - Proceeding - 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering: Sustainable Development for Smart Innovation System, COSITE 2023
SP - 199
EP - 203
BT - Proceeding - 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 August 2023 through 3 August 2023
ER -