Comparison Analysis of Fuzzy Sugeno & Fuzzy Mamdani for Household Lights - Miftah Alfian Firdausy; Ema Utami; Anggit Dwi Hartanto (Universitas Respati Yogyakarta, 2022)
The rapid growth of knowledge and technology in the IoT field encourages scientists to make discoveries in facilitating daily activities by utilizing artificial intelligence, one of which is the Smart Home. Smart home technology is needed that has better advantages over existing building materials, one of which is home lighting or the use of lights. If all this time, houses still use manual control to turn the lights on and off, it will potentially cause the lights to turn on still even though they are not needed, for that we need a method used in the implementation of automatic light control. From several studies, the Fuzzy method is widely used in this case. This method has several models, but the author uses the Fuzzy Mamdani method and the Sugeno method to apply in this study. The variable at the input is the LDR sensor, while the output is a lamp. From the trial results of the two methods, and accuracy test was sought as a parameter for the better ana method. It can be concluded that the Sugeno method has a better accuracy rate of 88.25%, compared to Mamdani's, which is only 84.5%.