Investigating the Impact of ReLU and Sigmoid Activation Functions on Animal Classification Using CNN Models

  • M Mesran Universitas Budi Darma
  • Sitti Rachmawati Yahya Universitas Siber Asia (UNSIA)
  • Fifto Nugroho Universitas Bung Karno
  • Agus Perdana Windarto STIKOM Tunas Bangsa
Keywords: Convolutional Neural Network, activation function, sigmoid, relu, classification, images


VGG16 is a convolutional neural network model used for image recognition. It is unique in that it only has 16 weighted layers, rather than relying on a large number of hyperparameters. It is considered as one of the best vision model architectures. This study compares the performance of ReLU (rectified linear unit) and sigmoid activation functions in CNN models for animal classification. To choose which model to use, we tested 2 state-of-the-art CNN architectures: the default VGG16 with the proposed method VGG16. A data set consisting of 2,000 images of five different animals was used. The results show that ReLU achieves higher classification accuracy than sigmoid. The model with ReLU on convolutional and fully connected layers achieved the highest accuracy of 97.56% on the test dataset. However, further experiments and considerations are needed to improve the results. Research aims to find better activation functions and identify factors that influence model performance. The data set consists of animal images collected from Kaggle, including cats, cows, elephants, horses, and sheep. It is divided into training and test sets (ratio 80:20). The CNN model has two convolution layers and two fully connected layers. ReLU and sigmoid activation functions with different learning rates are used. Evaluation metrics include precision, precision, recall, F1 score, and test cost. ReLU outperforms sigmoid in accuracy, precision, recall, and F1 score. However, other factors such as the size, complexity and parameters of the data set must be taken into account. This study emphasizes the importance of choosing the right activation function for better classification accuracy. ReLU is identified as effective in solving the vanish gradient problem. These findings can guide future research to improve CNN models in animal classification.


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How to Cite
M Mesran, Sitti Rachmawati Yahya, Fifto Nugroho, & Agus Perdana Windarto. (2024). Investigating the Impact of ReLU and Sigmoid Activation Functions on Animal Classification Using CNN Models. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(1), 111 - 118.
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