Abstract: As a tropical country, Indonesia is situated in Southeast Asia nation has vast forests. Forest fire occur busy vary due to land conditions and forest conditions in drought season. The indicator used mitigated potential forest fire is to study the indicator behavior of the fire weather index (FWI). The data is gathered from the observation station in north Sumatra province, computation and estimation FWI by Canadian Forest Fire Weather Index based on the data gathered. It is found that there is gathered outlier data. to hope will it, it is necessary to conduct classification and predict this of the dataset by machine learning approach using Support Vector Machine Forest Fire (SVM-FF), which is a further development of the previous models, known as the c-SVM and v-SVM. This method includes a balancing parameter by determining the lower and upper limits of a support vector. Furthermore, it allowed the balancing parameter value to be negative. The results showed that the classification of FWI was at low, medium, high, and extreme levels. The low FWI value has an average of 0.5 which is in the 0 to 1 interval. There was an increase in the model’s accuracy and performance from its predecessor, which include the c-SVM and v-SVM with respective values of 0.96 and 0.89. Meanwhile, it was observed that with the SVM-FF model, the accuracy was quite better with a value of 0.99, indicating that it is useful as an alternative to classify and predict forest fires.

Darwis Robinson Manalu, Opim Salim Sitompul, Herman Mawengkang and Muhammad Zarlis, “Model Classification of Fire Weather Index using the SVM-FF Method on Forest Fire in North Sumatra, Indonesia” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140836

@article{Manalu2023,
title = {Model Classification of Fire Weather Index using the SVM-FF Method on Forest Fire in North Sumatra, Indonesia},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140836},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140836},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Darwis Robinson Manalu and Opim Salim Sitompul and Herman Mawengkang and Muhammad Zarlis}
}