Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Computing and Smart Ecosystems
Muhammad Falah Altairgunna
Department of Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro
Computer Science & IT Electrical & Electronics Engineering

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

Found 1 Documents
Search

 1 
Comparative Evaluation of EfficientNet-B0 and MobileNetV3 for Multi-Class Fire Image Classification with Grad-CAM Interpretation Muhammad Fabian Rizky Fatah; Ravicenna Mahardhika; Muhammad Falah Altairgunna; Ricardus Anggi Pramunendar
Journal of Computing and Smart Ecosystems Vol. 2 No. 1 (2026): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v2i1.1129

Abstract

Fire detection using image-based computer vision is a promising alternative to conventional sensor systems, which are often limited by detection range and false alarms. This study presents a comparative evaluation of two lightweight deep learning architectures, EfficientNet-B0 and MobileNetV3 Small, for multi-class fire image classification across four categories: Urban Fire, Wild Fire, Non-Damage Building, and Non-Damage Wildlife. Both models were trained using transfer learning with ImageNet pre-trained weights on a curated dataset of 1,933 images with selective undersampling to address class imbalance. Grad-CAM (Gradient-weighted Class Activation Mapping) was applied to interpret model decisions and validate the learned visual features. EfficientNet-B0 achieved the highest overall accuracy of 93.8% with a macro F1-score of 0.925, while MobileNetV3 Small reached 91.0% validation accuracy with a macro F1-score of 0.900. Grad-CAM visualizations confirmed that EfficientNet-B0 develops more localized feature activations focused on flame-colored regions, while MobileNetV3 exhibited broader contextual activations. The results demonstrate that lightweight transfer learning models combined with Grad-CAM provide both high classification performance and meaningful interpretability for fire detection applications.
Co-Authors Muhammad Fabian Rizky Fatah Ravicenna Mahardhika Ricardus Anggi Pramunendar