Classification of Dog Emotion Using Transfer Learning on Convolutional Neural Network Algorithm

Authors

  • Steven Tribethran Universitas Multi Data Palembang
  • Nicolas Jacky Pratama Hasan Universitas Multi Data Palembang
  • Abdul Rahman Universitas Multi Data Palembang

DOI:

https://doi.org/10.31294/p.v26i2.5295

Keywords:

Dog, Convolutional Neural Network, Emotion classification, Transfer Learning, VGG16

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.

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Published

2024-09-30

How to Cite

Tribethran, S., Jacky Pratama Hasan, N., & Rahman, A. (2024). Classification of Dog Emotion Using Transfer Learning on Convolutional Neural Network Algorithm. Paradigma - Jurnal Komputer Dan Informatika, 26(2), 102-108. https://doi.org/10.31294/p.v26i2.5295

Issue

Vol. 26 No. 2 (2024): September 2024 Period

Section

Articles

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Copyright (c) 2024 Steven Tribethran, Nicolas Jacky Pratama Hasan, Abdul Rahman

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