TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection

Authors

  • Ghazi Mauer Idroes Department of Occupational Health and Safety, Faculty of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Aga Maulana Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Rivansyah Suhendra Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
  • Andi Lala School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Taufiq Karma Department of Occupational Health and Safety, Faculty of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Fitranto Kusumo Centre for Technology in Water and Wastewater, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo 2007 NSW Australia
  • Yuni Tri Hewindati Department of Natural Sciences, Faculty of Science and Technology, Universitas Terbuka, Tangerang Selatan 15437, Indonesia
  • Teuku Rizky Noviandy Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/ljes.v1i1.42

Keywords:

Convolutional neural network, Deep learning, Forest fire detection, ResNet50V2

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.

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Published

2023-06-22

How to Cite

Idroes, G. M., Maulana, A., Suhendra , R., Lala, A., Karma, T., Kusumo, F., Hewindati, Y. T., & Noviandy, T. R. (2023). TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection. Leuser Journal of Environmental Studies, 1(1), 1–8. https://doi.org/10.60084/ljes.v1i1.42

Issue

Vol. 1 No. 1 (2023): July 2023

Section

Articles

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Copyright (c) 2023 Ghazi Mauer Idroes, Aga Maulana, Rivansyah Suhendra , Andi Lala, Taufiq Karma, Fitranto Kusumo, Yuni Tri Hewindati, Teuku Rizky Noviandy

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.