Abstract
Forest damage due to fire is unique of the catastrophes that can disrupt and damage the existing ecosystem. There needs to be a quick response to fires because disaster management takes longer, and the impact of the damage will be more severe. To process images to detect fire in the forest, we need to build a suitable deep-learning model. This study proposed research on forest fire detection using an Xception and MobileNet model. Moreover, this research optimizes the accuracy of the model by applying Contrast-Limited-Adaptive-Histogram-Equalization (CLAHE) and data augmentation to tackle the problem of the forest fire image dataset. Based on the experiment, MobileNet with CLAHE obtained 99,66% accuracy in the test phase. In the same phase, MobileNet with CLAHE obtained a value F1-score of 1.00, a value of precision of 0.99, and a value of recall of 1.00. If compared to other model performances, MobileNet with CLAHE obtained the best result.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
