Pengembangan Aturan Fuzzy Mamdani Menggunakan Algoritma Gray Wolf Optimizer (GWO) dalam Menentukan Prediksi Keselamatan Pelayaran

Abstract

The Gray Wolf Optimizer (GWO) algorithm is an optimization method inspired by the social hierarchy and hunting behavior of gray wolves. Gray wolves are categorized as alpha, beta, delta, and omega. From the initial population of wolves ‘P’, the one closest to the prey becomes the alpha. In the fuzzy rule development system, the Gray Wolf Optimizer algorithm is used to optimize Mamdani fuzzy rules, contributing to improved accuracy in predicting maritime safety. This is essential to ensure operational safety in maritime navigation by considering various factors that influence sailing conditions. The Mamdani fuzzy system used in this model is capable of handling uncertainty and ambiguity in the data related to factors affecting maritime safety. The Gray Wolf Optimizer algorithm aids in optimizing the fuzzy rules to produce more accurate predictions. Based on a comparison of optimization results, the model developed using the Gray Wolf Optimizer and Mamdani fuzzy system demonstrates better performance than conventional methods in terms of maritime safety prediction accuracy.