Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal INCODING: Journal of Informatics and Computer Science Engineering
Ray, Raja Pahlefi
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analisis Pengaruh Fungsi Aktivasi CNN terhadap Performa Klasifikasi Hewan Ray, Raja Pahlefi; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.847

Abstract

This study aims to analyze the impact of five activation functions ReLU, LeakyReLU, ELU, Sigmoid, and Tanh—on the performance of a Convolutional Neural Network (CNN) model for image classification into three categories: cats, dogs, and wild animals. The evaluation was conducted using validation accuracy metrics, accuracy trends across training epochs, and confusion matrix analysis. The results show that modern activation functions such as LeakyReLU, ELU, and ReLU yield high accuracy and balanced predictions, demonstrating their effectiveness in mitigating vanishing gradient issues and enhancing the model's generalization capability. In contrast, classical functions like Sigmoid and Tanh performed poorly, producing imbalanced predictions and stagnant accuracy Therefore, the choice of activation function plays a critical role in building an optimal CNN model for image classification tasks. This study recommends ReLU-based activation functions, particularly LeakyReLU, as the primary choice for developing multi-class image classification models.
Co-Authors Sembiring, Arnes