Implementasi Hybrid Algoritma Genetika dan Simulated Annealing untuk Optimasi Nutrisi Makanan Atlet Endurance - Shafira Eka Aulia Putri; Bayu Rahayudi; Imam Cholissodin (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Food is the main source of energy for humans. Sufficient energy can support the performance of daily activities, especially for athletes, such as endurance athletes. Endurance athlete sports activities have a long enough duration so they require a lot of energy. Therefore, the selection of food components for athletes every day can be one of the supporting factors in providing their best performance. The current research provides a solution in the form of recommendations for food ingredients in 1 day for endurance athletes by considering their nutritional needs using a hybrid genetic algorithm and simulated annealing method. The food ingredients component has a total of 125 data, consisting of staple foods, vegetables, animals, and complementary foods that have carbohydrates, proteins and fats in them. Recommendations are given in the form of chromosomal representation of 14 food ingredients consisting of 3 food packages. Based on the testing process, the optimal parameter has a population of 1000, 2000 generations, the combination of Cr = 0.1 and Mr = 0.9, T0 = 0.7, T = 0.5, alpha value = 0.2. The results of this study resulted in recommendations for food components by considering the nutritional needs of athletes within the tolerance limit of nutritional content, which is Â±10% for endurance sports athletes.