Learners mood detection using Convolutional Neural Network (CNN)

Rosa Ariani Sukamto; Munir; Siswo Handoko

doi:10.1109/ICSITech.2017.8257079

Learners mood detection using Convolutional Neural Network (CNN)

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Rosa Ariani Sukamto, Munir, Siswo Handoko

2017 Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017 Vol. 2018-January Conference paper Cited by 3

Abstract

This research concerns about classroom learners mood detection in learning process which is believed to be an important thing to increase learning process effectiveness. Convolutional Neural Network (CNN), a branch of deep learning architectures and a part of Machine Learning, was used as a method in this research. The experiments were conducted through several stages such as face detection, image improvement and model formation. There are 660 images used as training data and the classification process result showed a good result. The accuracy average result was considered as a good result by using 4 layers of CNN i.e. 2 convolutional layers and 2 subsampling layers. Based on the experiments result, the system needs to be developed further by adding more specific data class and training data. © 2017 IEEE.

Affiliations

Computer Science Education Department, Universitas Pendidikan Indonesia, Bandung, Indonesia

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