SISTEM PENDUKUNG KEPUTUSAN TINGKAT KEMACETAN LALU LINTAS BERDASARKAN FUZZY LOGIC
Keywords:
Fuzzy Logic, Congestion Level, Vehicle Count, Vehicle Speed, Vehicle DensityAbstract
Traffic congestion is a common problem that occurs in many large cities. To accurately determine the level of congestion, a system is needed that is able to handle data uncertainty, such as the number and speed of vehicles. In this study, the Mamdani type fuzzy logic method is used to determine the level of congestion based on two input variables, namely the number of vehicles and vehicle speed. Data is obtained from vehicle counting sensors and speed sensors. The linguistic values of the input variables consist of "few," "medium," and "many" for the number of vehicles, and "slow," "normal," and "fast" for vehicle speed. The results of defuzzification produce output in the form of congestion levels: "smooth," "dense," and "congested". This application of fuzzy logic has proven effective in providing flexible and realistic results to dynamic traffic conditions.
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