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All Journal International Journal of Electrical and Computer Engineering MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management
Hidayat, Assad
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Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering

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Development of Post Fire Severity Assessment Module in Indonesian Forest and Land Fire Prevention Patrol System Sitanggang, Imas Sukaesih; Hidayat, Assad; Syaufina, Lailan
Jurnal Manajemen Hutan Tropika Vol. 32 No. 1 (2026)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.32.1.97

Abstract

The severity of forest and land fires is a crucial indicator for assessing their impact on ecosystems, particularly vegetation and soil. The assessment results serve as a foundation for forest and land restoration, rehabilitation, and conservation efforts. This study employs a deep learning algorithm to develop a forest and land fire severity assessment module. The CNN model used is MobileNetV2 that has an accuracy of 88.8%. The smart module is integrated into the Indonesian Forest and Land Fire Prevention Patrol Mobile Application and follows the Software Development Life Cycle approach in its development. Field observation images are input to the CNN module in the mobile application. The module then analyzes the fire severity and classifies it into very light, light, moderate, severe, and very severe categories. Testing results indicate that the module accurately predicts fire severity based on established assessment standards. The optimal time for capturing images is a few days after the fire, during daylight hours, to ensure the majority of images depict burned areas. Additionally, the findings highlight that lighting conditions and image quality significantly influence the accuracy of severity predictions. Further development is required to enhance the module's compatibility and flexibility, enabling its use across various devices.
Hyperparameter tuning of MobileNetV2 on forest and land fire severity classification Hidayat, Assad; Sitanggang, Imas Sukaesih; Syaufina, Lailan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp964-972

Abstract

Forest and land fires pose significant environmental challenges, causing economic and ecological damage depending on their severity. This study proposes a deep learning-based classification model to assess fire severity using the MobileNetV2 architecture. A dataset of 560 post-fire images was categorized into five severity levels, with dataset preprocessing involving resizing, rescaling, and image augmentation. To enhance model performance, K-means clustering was applied for balanced data distribution across classes. The model was trained using grid search for hyperparameter tuning, with the optimal combination being a batch size of 8, learning rate of 0.0001, and dropout of 0.3. Training was conducted in 50 epochs, and evaluation using the confusion matrix demonstrated an accuracy of 85%, precision of 86%, and recall of 81%. The results indicate that MobileNetV2 effectively classifies post-fire severity levels, offering a reliable tool for post-disaster assessment. This study highlights the significance of dataset preprocessing and hyperparameter tuning in improving model accuracy. Future research should explore alternative architectures and expand the dataset to enhance model generalization. These findings can aid authorities in assessing fire impact, supporting mitigation strategies, and improving post-fire land management.
Co-Authors Imas Sukaesih Sitanggang Lailan Syaufina