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dc.contributor.advisorNababan, Esther Sorta Mauli
dc.contributor.advisorGio, Prana Ugiana
dc.contributor.authorPurba, Rani Natalia
dc.date.accessioned2025-07-02T07:19:30Z
dc.date.available2025-07-02T07:19:30Z
dc.date.issued2025
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/104799
dc.description.abstractIn the implementation of fuzzy logic, the Sugeno fuzzy method faces several challenges, such as issues in determining the fuzzy rule base and the occurrence of undefined outputs (defuzzification) with values of 0/0. This study examines the application of the Sugeno fuzzy method in identifying the level of forest fire risk by considering various variables, such as temperature, humidity, and wind speed. The model is developed using fuzzy rules constructed based on the relationships among the variables. The test results show that after modifying the membership function boundaries to decimal values approaching the original lower bounds, the Zero-Order Sugeno fuzzy method is capable of producing an average forest fire risk level of 68.83 (high category) in Tanjung Puting National Park. In addition, the application of the First-Order Sugeno fuzzy method produces a multiple linear regression model that can be applied within the rule base, resulting in an average forest fire risk level of 68.89 (high category) in the same location. During the evaluation phase, the First-Order Sugeno model achieved a lower RMSE value (15.47) compared to the Zero-Order model (16.03), indicating that it is more suitable for handling extreme conditions such as dangerous spikes in risk. Therefore, this approach has the potential to serve as an effective early warning system for forest fire mitigation, supporting decision-making processes.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectFuzzy Sugenoen_US
dc.subjectforest fireen_US
dc.subjectrisk identificationen_US
dc.subjectearly warning systemen_US
dc.subjectdecision makingen_US
dc.titleKajian Metode Fuzzy Sugeno dalam Mengidentifikasi Tingkat Risiko Kebakaran Hutanen_US
dc.title.alternativeA Study of The Sugeno Fuzzy Method for Identifying Forest Fire Risk Levelsen_US
dc.typeThesisen_US
dc.identifier.nimNIM210803063
dc.identifier.nidnNIDN0018036102
dc.identifier.nidnNIDN0001108907
dc.identifier.kodeprodiKODEPRODI44201#Matematika
dc.description.pages71 Pagesen_US
dc.description.typeSkripsi Sarjanaen_US
dc.subject.sdgsSDGs 15. Life On Landen_US


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