Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal G-Tech : Jurnal Teknologi Terapan
Aditya Pratama
Universitas Nahdlatul Ulama Kalimantan Barat, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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

Found 1 Documents
Search

 1 
Forest Fire Detection Based on Digital Imagery Using Convolutional Neural Network (CNN) Model Candra Gudiato; Aditya Pratama; Christian Cahyaningtyas
G-Tech: Jurnal Teknologi Terapan Vol 10 No 2 (2026): G-Tech, Vol. 10 No. 2 April 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i2.9422

Abstract

This study explores the implementation of a Convolutional Neural Network (CNN) for automated forest fire identification using digital image processing. Utilizing the USTC 'Forest Fire' dataset, the research framework involved systematic data preprocessing, including a 70:30 training-validation split and the application of image augmentation techniques to enhance model robustness. The proposed architecture features a sequential design with dual convolution and pooling layers, integrated with ReLU and Sigmoid activations. Although initial training over seven epochs yielded a deceptive validation accuracy of 99%, granular performance analysis exposed critical limitations. Evaluation via a Confusion Matrix revealed that while the model excelled at identifying 'non-fire' scenarios, it struggled significantly with actual fire detection, failing to recognize 301 out of 331 fire instances. These results highlight a severe class imbalance issue, suggesting that standard accuracy metrics are insufficient for this application and emphasizing the need for more balanced sampling or advanced architectural adjustments in future fire detection systems.
Co-Authors Cahyaningtyas, Christian Gudiato, Candra