Analisis Perbandingan Metode Sugeno dan Mandani (Studi Kasus : Sistem Tingkat Curah Hujan di DKI Jakarta)
Keywords:
Fuzzy Logic, Mandani, Sugeno.Abstract
: In DKI Jakarta, the level of rainfall affects several activities such as flights and daily activities, as well
as the effects of global warming which greatly affects the weather in Indonesia, especially Jakarta, which results in
changes and balancing of weather that are unavoidable and difficult to predict. the benefits of this research can
produce a system that is able to predict weather using the Sugeno and Mamdani methods in its application to the
weather prediction system. Mandani Method: Formation of Fuzzy Sets In the Mandani method, both output
variables are divided into one or more fuzzy sets. reasoning is almost the same as the Fuzzy Mandani model.
However, if the output of reasoning with the Fuzzy Mandani model is in the form of a Fuzzy Sugeno set, the output
is a constant or linear equation. Formation of a Fuzzy Set In the Mandani method, both the output variables are
divided into one or more Fuzzy sets. take the maximum value of the rule, then use it to modify the fuzzy area, and
apply it to the output using the OR (union) operator, Affirmation (Defuzzy) The input of the defuzzification process
is a fuzzy set obtained from the composition of fuzzy rules, while the resulting output is a number in the domain of
the fuzzy set. Thus, it can be concluded that the Sugeno method is better than the Mamdani method in the case study
of weather prediction. For further research, the author gives some suggestions, namely using more training data
and adding variables to the study such as wind direction or other weather variables related to weather changes.
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