THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS
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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Mercu Buana
2019-10-01
|
| Series: | Jurnal Ilmiah SINERGI |
| Subjects: | |
| Online Access: | http://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/5700 |
| _version_ | 1797727914155835392 |
|---|---|
| author | Devi Fitrianah Hisyam Fahmi |
| author_facet | Devi Fitrianah Hisyam Fahmi |
| author_sort | Devi Fitrianah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T11:06:12Z |
| format | Article |
| id | doaj.art-1de7983578ec4d77b3c0a8e1b6e450df |
| institution | Directory Open Access Journal |
| issn | 1410-2331 2460-1217 |
| language | English |
| last_indexed | 2024-03-12T11:06:12Z |
| publishDate | 2019-10-01 |
| publisher | Universitas Mercu Buana |
| record_format | Article |
| series | Jurnal Ilmiah SINERGI |
| spelling | doaj.art-1de7983578ec4d77b3c0a8e1b6e450df2023-09-02T03:44:13ZengUniversitas Mercu BuanaJurnal Ilmiah SINERGI1410-23312460-12172019-10-0123318419010.22441/sinergi.2019.3.0022967THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMSDevi Fitrianah0Hisyam Fahmi1Department of Informatics, Faculty of Computer Science, Universitas Mercu BuanaDepartment of Maths, Universitas Islam Negeri Maulana Ibrahim MalangThis 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.http://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/5700forest firedata miningfeature selectionsffs algorithmsbfs algorithm |
| spellingShingle | Devi Fitrianah Hisyam Fahmi THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS Jurnal Ilmiah SINERGI forest fire data mining feature selection sffs algorithm sbfs algorithm |
| title | THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS |
| title_full | THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS |
| title_fullStr | THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS |
| title_full_unstemmed | THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS |
| title_short | THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS |
| title_sort | identification of determinant parameter in forest fire based on feature selection algorithms |
| topic | forest fire data mining feature selection sffs algorithm sbfs algorithm |
| url | http://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/5700 |
| work_keys_str_mv | AT devifitrianah theidentificationofdeterminantparameterinforestfirebasedonfeatureselectionalgorithms AT hisyamfahmi theidentificationofdeterminantparameterinforestfirebasedonfeatureselectionalgorithms AT devifitrianah identificationofdeterminantparameterinforestfirebasedonfeatureselectionalgorithms AT hisyamfahmi identificationofdeterminantparameterinforestfirebasedonfeatureselectionalgorithms |