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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
Yasa, I Wayan Restama
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Computer Science & IT

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Klasifikasi Ekspresi Wajah Menggunakan Metode CNN: Studi Kasus Dataset Kaggle Yasa, I Wayan Restama; Karyawati, AAIN Eka
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p05

Abstract

This research aims to implement a Convolutional Neural Network (CNN) in facial expression classification using the Kaggle dataset which consists of five types of facial expressions, namely anger, disgust, fear, happiness and sadness. This method is considered important in supporting various applications such as emotion detection, facial recognition, and better human-machine communication. In this research, data preprocessing and augmentation were carried out using ImageDataGenerator to increase data diversity and prevent overfitting. Next, a CNN architecture is built which consists of convolution layers, pooling layers, and Dense layers. The model was trained using the Adam optimizer with a categorical crossentropy loss function for 50 epochs. The results show that the model achieves approximately 51% accuracy on the validation set. However, further analysis showed variations in model performance among facial expression classes, with some classes performing better than others. Keywords: Facial Expression Classification, Convolutional Neural Network, Kaggle Dataset, Data Augmentation, Image Processing.
Co-Authors Anak Agung Istri Ngurah Eka Karyawati