<![CDATA[Stock is one of investment instrument which is popular because of the high profit potential and risk. These profit potential and risk are caused by fluctuations of the stock price in the stock market. To minimalize the risk, a system which is able to predict closing price of the next day is required. The architecture which is used in this research is multi-layer neural network. This architecture is trained with 2 different training methods, which is backpropagation and genetic algorithm. Both of the methods aim to gain weights of all network's architecture. Backpropagation's parameters which obtained during the research are 4500 iteration and 0.7 learning rate. For genetic algorithm's parameters which obtained during the research are 2000 generations, population size of 200, crossover rate 0.1 and mutation rate 0.9. By using those parameters, average RMSE value which produced using backpropagation algorithm is 0.048006. Meanwhile when using genetic algorithm as a training method, average RMSE value which produced by the network is 0.065205. So in this research, average error value which is produced by using backpropagation training is smaller than using genetic algorithm training method.]]>
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