Analisis Model Inferensi Fuzzy Tsukamoto dalam Prediksi Kondisi Jalan

Abstract

Fuzzy logic begins with the concept of fuzzy set. A fuzzy set describing the relationship between the quantity given x and the membership function (p), which ranges between 0 and l, fuzzy provides a simple way to arrive at a definite conclusion based on the input information is vague, ambiguous, imprecise, noisy, or missing- Tsukamoto fuzzy inference modeling provides better performance and more consistent and mathematically in the handling of uncertainty due to input linguistic variable process provides better output results with the results output using mathematical models other classics. In this research of three input variables that affect road conditions in general, and as many as four years of data, from the data generated 27 rules to make a prediction of road conditions niali future dating. From the results obtained prediction that the results are very accurate which reached 98% accuracy rate, from these results it can be concluded that the model is suitable Tsukamoto fuzzy inference in terms of prediction and prediction for cases in some road in North Sumatera-