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All Journal Jurnal Teknologi dan Manajemen Informatika TEKNOLOGI: Jurnal Ilmiah Sistem Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer INTEGER: Journal of Information Technology Teknika: Engineering and Sains Journal Knowledge Engineering and Data Science JICTE (Journal of Information and Computer Technology Education) SMARTICS Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Konvergensi Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Antivirus : Jurnal Ilmiah Teknik Informatika Journal of Information System,Graphics, Hospitality and Technology Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Teknika Teknika Journal of Electrical Engineering and Computer (JEECOM) Best : Journal of Applied Electrical, Science and Technology Insyst : Journal of Intelligent System and Computation J-Intech (Journal of Information and Technology) Joutica : Journal of Informatic Unisla Jurnal Nasional Teknik Elektro dan Teknologi Informasi Insand Comtech : Information Science and Computer Technology Journal Jurnal Indonesia Sosial Teknologi JEECS (Journal of Electrical Engineering and Computer Sciences) Eksplorasi Teknologi Enterprise & Sistem Informasi (EKSTENSI) EduTech Journal
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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

Klasifikasi Ketertarikan Belajar Anak PAUD Melalui Video Ekspresi Wajah Dan Gestur Menggunakan Convolutional Neural Network Ajeng Restu Kusumastuti; Yosi Kristian; Endang Setyati
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 10, No 2 (2021): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v10i2.1146

Abstract

Abstract—The Covid-19 pandemic has transformed the offline education system into online. Therefore, in order to maximize the learning process, teachers were forced to adapt by having presentations that attract student's attention, including kindergarten teachers. This is a major problem considering the attention rate of children at early age is very diverse combined with their limited communication skill. Thus, there is a need to identify and classify student's learning interest through facial expressions and gestures during the online session. Through this research, student's learning interest were classified into several classes, validated by the teacher. There are three classes: Interested, Moderately Interested, and Not Interested. Trials to get the classification of student's learning interest by teacher validation, carried out by training and testing the cut area of the center of the face (eyes, mouth, face) to get facial expression recognition, supported by the gesture area as gesture recognition. This research has scenarios of four cut areas and two cut areas that were applied to the interest class that utilizes the weight of transfer learning architectures such as VGG16, ResNet50, and Xception. The results of the learning interest classification test obtained a minimum validation percentage of 70%. The result obtained through scenarios of three learning interest classes four cut areas using VGG16 was 75%, while for two cut areas using ResNet50 was 71%. These results proved that the methods of this research can be used to determine the duration and theme of online kindergarten classes.