Optimasi Fungsi Keanggotaan Fuzzy Tsukamoto dengan Algoritme Genetika pada Peramalan Harga Emas untuk Stock Trading - Ficry Agam Fathurrachman; Fitra Abdurrachman Bachtiar; Imam Cholissodin (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Investors and stock traders need knowledge of forecasting when the price of gold will rise or will decline to minimize the risk in investing. This forecasting requires an appropriate method in order to give good results. FIS Tsukamoto is used to forecast the price of gold based on existing exchange rate data. The parameters used by Tsukamoto FIS are the currency rates of USD / GBP, CHF / USD, JPY / USD, EUR / USD based on the previous three days and the price of gold based on the previous day. To maximize Tsukamoto FIS performance, Tsukamoto FIS membership function will be optimized using Genetic Algorithm. The chromosome representation used is real-coded with a double data type. The reproduction of the crossover method used is one-cut point, while the mutation method used is random mutation. In the selection process, the method used is elitism selection to get the best individuals. Based on parameter testing carried out with 10 experiments each parameter, the best population size is 180, combination of cr = 0.9 and mr = 0.1, and the best number of generations is 325, the best fitness value is 8.6972. The Root-Mean Squared Error (RMSE) value obtained before optimization is 13.3611, while after optimization it is obtained that the smaller RMSE value is 12.5801. These results indicate an increase in the value of accuracy in Tsukamoto FIS after being optimized using Genetic Algorithm.