Shofiana, Dewi Asiah and Utami, Yohana Tri and Heningtyas, Yunda (2022) HOTSPOT PREDICTIVE MODELING USING REGRESSION DECISION TREE ALGORITHM. Jurnal TEKNOINFO, 16 (2). pp. 460-466. ISSN 2615-224X
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2051-5943-2-PB-Teknoinfo-HOTSPOT PREDICTIVE MODELING USING REGRESSION DECISION TREE ALGORITHM.pdf Download (454kB) | Preview |
Abstract
Forest fires had always become an international issue influencing many life sectors, including environmental, social, and economic. The forest fire in 2013 was regarded as one of the worst forest fire tragedies in history, not only in Indonesia but also in the world. Detection of hotspots on the earth's surface by the satellite can be an indication of land and forest fire occurrence. This research aims to build a predictive model of monthly hotspots in Rokan Hilir Regency using the regression tree algorithm. Several variables related to weather information are included, such as rainfall, sea surface temperature, and southern oscillation index. This research used 245 training data and 43 testing data, resulting a predictive model with a correlation of 0.875 and an error rate of 0.166. Based on the values, we can conclude that the performance of the model is considerably good.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science S Agriculture > SD Forestry |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer |
| Depositing User: | M.Kom. Dewi Asiah Shofiana |
| Date Deposited: | 25 Jul 2022 09:04 |
| Last Modified: | 25 Jul 2022 09:04 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/43602 |
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