Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes18-25495
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
Creative Commons License

Volume 18 article 678 pages: 207 - 215

Anugrah Indah Lestari*
Faculty of Engineering, Universitas Indonesia, Depok, Indonesia
Indonesian National Institute of Aeronautics and Space, Jakarta, Indonesia

Dyah Lalita Luhurkinanti
Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

Hajar Indah Fitriasari
Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

Ruki Harwahyu
Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

Riri Fitri Sari
Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

Forest or land fire is a disaster that commonly occurred in Indonesia mainly in Kalimantan and Sumatera. Optical remote sensing satellite becomes a promising technology that can be utilized to identify the burned area in quick time for disaster management response.This study evaluated the use of supervised machine learning, such as Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN) to classify burned area in the Central Kalimantan province on June and August 2019 as pre-fire event and post-fire event using Sentinel-2 imageries. An imbalanced and a balanced dataset with varying hyper-parameter were used on those classifiers. Hotspot data derived from MODIS and Suomi NPP data are also used as training and testing dataset. Based on the study, the imbalanced dataset influences precision and recall values, as well as the accuracy of SVM and DNN classifiers, but not as much in RF. RF classifier outperforms SVM and DNN in terms of precision, recall, and accuracy for both a balanced dataset and an imbalanced dataset with the accuracy ranged from 98.2 -99.3%. The accuracy of SVM classifier is ranged from 94.7-98.1% for an imbalanced dataset and 90.4 % - 98.2 % for a balanced dataset. Although the high accuracy is still can be achieved in DNN classifier, there is a changing accuracy from 98.5-98.8 % in a balanced dataset to 95.5-95.7% in an imbalanced dataset. These fi ndings imply that the high accuracy is still can be achieved by SVM, RF, and DNN classifiers with an imbalanced or a balanced dataset.

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This research is funded by Universitas Indonesia under the PITQQ Grant number NKB-00321/UN2.R3.1/ HKP.05.00/2019. Also, the authors thank the Disaster Division, Remote Sensing Application Center - Indonesian National Institute of Aeronautics and Space for giving valuable advice to this research.

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