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All Journal Paradigma
Jacky Pratama Hasan, Nicolas
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Computer Science & IT

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Classification of Dog Emotion Using Transfer Learning on Convolutional Neural Network Algorithm Tribethran, Steven; Jacky Pratama Hasan, Nicolas; Rahman, Abdul
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.5295

Abstract

Recognizing your pet's emotions are very important to improve health, welfare and to detect certain diseases in the animal. The emotions in question are categorized into four categories, namely anger, happiness, calmness, and sadness. The model is designed by utilizing transfer learning techniques using the VGG16 architecture to perform image feature extraction for dog emotion classification based on the image of the animal's facial expression. The research produced an accuracy value of 96.72% on the training set and 88.05% on the validation set, as well as an average F1-Score value of 84.30% on the test set. This research shows the great potential of utilizing transfer learning in dog emotions classification and contributes to more advanced emotion recognition techniques to improve pet’s welfare.
Classification of Dog Emotion Using Transfer Learning on Convolutional Neural Network Algorithm Tribethran, Steven; Jacky Pratama Hasan, Nicolas; Rahman, Abdul
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.5295

Abstract

Recognizing your pet's emotions are very important to improve health, welfare and to detect certain diseases in the animal. The emotions in question are categorized into four categories, namely anger, happiness, calmness, and sadness. The model is designed by utilizing transfer learning techniques using the VGG16 architecture to perform image feature extraction for dog emotion classification based on the image of the animal's facial expression. The research produced an accuracy value of 96.72% on the training set and 88.05% on the validation set, as well as an average F1-Score value of 84.30% on the test set. This research shows the great potential of utilizing transfer learning in dog emotions classification and contributes to more advanced emotion recognition techniques to improve pet’s welfare.
Co-Authors Abdul Rahman Abdul Rahman Tribethran, Steven