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All Journal Jurnal Riset Informatika
Neneng Rachmalia Feta
Bank Rakyat Indonesia Institute of Technology and Business
Computer Science & IT

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Journal : Jurnal Riset Informatika

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CLASSIFICATION OF BURNED PEATLAND USING PROBABILISTIC NEURAL NETWORK ALGORITHM BASED ON HIGH TEMPORAL DATA Neneng Rachmalia Feta
Jurnal Riset Informatika Vol. 4 No. 2 (2022): March 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (548.161 KB) | DOI: 10.34288/jri.v4i2.151

Abstract

Land fires in Indonesia occur on dry land as well as on peatlands. Fires on peatlands are more dangerous and more challenging to tackle than fires on non-peatlands, and the consequences of peatland fires that occur are very detrimental to communities. One of the solutions offered in assessing forest and peatland fires is remote sensing technology. Satellite images obtained from remote sensing technology are usually classified for further analysis. The main objective of this study is to develop a classification model using the Probabilistic Neural Network (PNN) to classify areas in peatlands before, during, and after burning on Landsat 7 ETM+ satellite imagery. Furthermore, the model is used to obtain the trajectory pattern of the burned area using the DBScan algorithm. The research area is Ogan Komering Ilir Regency; South Sumatra Province Landsat 7 ETM+ images were taken from January 2015 – December 2015.
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