Forest Fire Detection Using the FireNet-Convolutional Neural Network Model with Image Augmentation

Fachri Ayudi Fitrony; Senjaya Machfudi Zulkif

Authors

Fachri Ayudi Fitrony Universitas Nahdlatul Ulama Pasuruan, Indonesia
Senjaya Machfudi Zulkif Universitas Nahdlatul Ulama Pasuruan, Indonesia

Keywords:

Forest Fire , FireNet-Convolutional, Neural Network Model , Image Augmentation

Abstract

Forest and land fires in Indonesia are frequent occurrences and cause significant losses in health, ecology, and social aspects. Both human and natural factors play a role in triggering these fires. However, forest and land fire management still faces challenges in accurately predicting hotspot locations, making optimal control difficult. Therefore, the development of intelligent systems to detect forest and land fires more effectively is necessary. This study aims to create a model capable of detecting forest and land fires using a convolutional neural network, utilizing image augmentation to improve detection accuracy. The dataset used in this study comes from the Fire Forest Dataset on the Kaggle website. The feature extraction process was carried out using the VGG16 architecture, and the resulting model was tested using the confusion matrix method to classify images into two classes: fire and non-fire. Through training using the VGG16 architecture, an effective model was obtained in detecting forest and land fires. The test results using 880 test data showed an accuracy rate of 97% in recognizing forest and land fire images. This research provides an important contribution to the development of forest and land fire detection technology. The use of image augmentation with the VGG16 architecture has the potential to improve fire detection accuracy.

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