A comparison of Mamdani and Sugeno method for optimization prediction of traffic noise levels
Alfa Saleh, Fujiati, Rika Rosnelly, Khairani Puspita, Andi Sanjaya · 2017 5th International Conference on Cyber and IT Service Management (CITSM) · 2017
The traffic noise is a disruption that leads to noise pollution if the noise is very loud. The traffic noise is caused by vehicle exhaust noise, engine and passenger of the transportation. The problem is the traffic noise has disturbed the comfort and health of living beings which are around the neighborhood. It is important that the strategy for controlling the noise of the traffic on the highway, so the noise pollution can be minimized. One of them is to control over the noise path. To predict the noise level of traffic on the highway, so in this study, researchers tested the approach using fuzzy inference systems that compare models mamdani and Sugeno in calculating the level of traffic noise based on the number of vehicles, the correction factor and the width of the road. From the data examined in this study, showed a percentage error of 1.77% for Fuzzy Mamdani models and 5.68% for the Model Sugeno, Fuzzy Mamdani models considered more accurate and effective than Sugeno models in predicting the traffic noise levels.