PENGGUNAAN ARTIFICIAL BEE COLONY UNTUK OPTIMASI BIAYA PEMENUHAN GIZI
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Institusion
Universitas Sriwijaya
Author
SHOLEHIN, SHOLEHIN
Rusdi Efendi (LECTURER ID : 8826630017)
Yunita Yunita (LECTURER ID : 0006068305)
Rusdi Efendi (LECTURER ID : 8826630017)
Yunita Yunita (LECTURER ID : 0006068305)
Subject
R858-859.7 Computer applications to medicine. Medical informatics
Datestamp
2019-10-01 03:43:25
Abstract :
Everyone needs adequate nutrition to perfonn activities and survive, expensive food will affect the fulfi llment of nutrition, optimization of nutrition fulfillment costs that is to minimize the amount of costs to be incurred by forming a combination of foodstuffs by selecting food from many n objects (ingredients) which has the best nutritional content and has the cheapest price, optimization of nutritional fulfillment cost equivalent to knapsack problem. The data of foodstuffs consumed amounted to 125 data of food stuff complete with price. The completion of this nutrient fulfillment optimization uses the artificial bee colony algorithm, to complete the optimization of the nutrient fulfillment cost of this algorithm using two control parameters namely the Number of Iterations or Maximum Cycle Number (MCN) and the Size of the Bee Population or Solution Number (SN). In the experiments the parameters given are the number of Iterations= 200, 400, 600, 800, 1000 and 1200, the value of the parameters of Bee Population Size = 25, 50, 75, 100, and 125. The results show that the greater the value of the given parameters the resulting fitness value the better. The best parameter combination value is obtained on Iteration value as much as 600 and Size of Bees Population as much as 125 with fitness value equal to 00.007408552
Everyone needs adequate nutrition to perfonn activities and survive, expensive food will affect the fulfi llment of nutrition, optimization of nutrition fulfillment costs that is to minimize the amount of costs to be incurred by forming a combination of foodstuffs by selecting food from many n objects (ingredients) which has the best nutritional content and has the cheapest price, optimization of nutritional fulfillment cost equivalent to knapsack problem. The data of foodstuffs consumed amounted to 125 data of food stuff complete with price. The completion of this nutrient fulfillment optimization uses the artificial bee colony algorithm, to complete the optimization of the nutrient fulfillment cost of this algorithm using two control parameters namely the Number of Iterations or Maximum Cycle Number (MCN) and the Size of the Bee Population or Solution Number (SN). In the experiments the parameters given are the number of Iterations= 200, 400, 600, 800, 1000 and 1200, the value of the parameters of Bee Population Size = 25, 50, 75, 100, and 125. The results show that the greater the value of the given parameters the resulting fitness value the better. The best parameter combination value is obtained on Iteration value as much as 600 and Size of Bees Population as much as 125 with fitness value equal to 00.007408552
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