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Devi Fitrianah
Department of Informatics, Faculty of Computer Science, Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS Devi Fitrianah; Hisyam Fahmi
SINERGI Vol 23, No 3 (2019)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (217.061 KB) | DOI: 10.22441/sinergi.2019.3.002

Abstract

This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.
Co-Authors Hisyam Fahmi