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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Riset Informatika Jurnal AbdiMas Nusa Mandiri Journal of Informatics and Advanced Computing
Neneng Rachmalia Feta
Institut Teknologi dan Bisnis Bank Rakyat Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Environmental Science Mathematics Physics Social Sciences Other

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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): Period of March 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3121.532 KB) | DOI: 10.34288/jri.v4i2.336

Abstract

Forest or land fires in Indonesia do not only occur in drylands but also in peatlands. Peatland fires are more dangerous and more difficult to overcome compared to non-peatland fires and the impacts of peatland fires are very harmful to society. One of the solutions 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 Probabilistic Neural Network (PNN) to classify areas in peatland before, during, and after being burned on the satellite image Landsat 7 ETM +. Furthermore, the model is used to get the trajectory pattern of the burned area using the DBScan algorithm. The study area is Ogan Komering Ilir District, South Sumatera Province, image Landsat 7 ETM + taken from January 2015 – December 2015.
Co-Authors Fitria Fitria Ninuk Wiliani Ninuk Wiliani Rizki Hesananda Rizki Hesananda Satria, Deki