Andi Lala
School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

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Journal : Leuser Journal of Environmental Studies

TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection Ghazi Mauer Idroes; Aga Maulana; Rivansyah Suhendra; Andi Lala; Taufiq Karma; Fitranto Kusumo; Yuni Tri Hewindati; Teuku Rizky Noviandy
Leuser Journal of Environmental Studies Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i1.42

Abstract

Forest fires have emerged as a significant threat to the environment, wildlife, and human lives, necessitating the development of effective early detection systems for firefighting and mitigation efforts. In this study, we introduce TeutongNet, a modified ResNet50V2 model designed to detect forest fires accurately. The model is trained on a curated dataset and evaluated using various metrics. Results show that TeutongNet achieves high accuracy (98.68%) with low false positive and false negative rates. The model's performance is further supported by the ROC curve analysis, which indicates a high degree of accuracy in classifying fire and non-fire images. TeutongNet demonstrates its effectiveness in reliable forest fire detection, providing valuable insights for improved fire management strategies.