The identification of determinant parameter in forest fire based on feature selection algorithms

Fitrianah, Devi and Fahmi, Hisyam (2019) The identification of determinant parameter in forest fire based on feature selection algorithms. SINERGI, 23 (3). pp. 184-190. ISSN 2460-1217

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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.

Item Type:	Journal Article
Keywords:	Forest fire; data mining; feature selection; SFFS algorithm; SBFS algorithm
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Hisyam Fahmi
Date Deposited:	09 Jun 2020 22:18

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