<![CDATA[Rice is very important for human beings, especially to the ASEAN community. Indonesia is one of the ASEAN countries that cultivate rice. In 2015, Indonesia ranked as the third-highest in terms of the world's largest rice producer. However, Indonesia still have to import rice every year due to its high demand and to fulfil Indonesian's per-capita consumption. The other reason is the different amount of harvest on each areas resulting in a scarcity of rice because the country can not be able to optimize the farming techniques that are used. This research use the methods of backpropagation neural network to predict the results of the rice productivity. In its implementation, the data is normalized using the min - max normalization and weighting initialization using Nguyen - Widrow. Based on the results of testing the parameters for the method of backpropagation, shows the most minimum RMSE i.e. 8.6918 with parameter values learning rate = 0.8, hidden layer neurons, hidden = 3 = 4 with the number of epoch 10000 against 135 training and 13 test data. Based on result of 5 fold cross validation against the stability testing data gets an average RMSE of 8.2126.]]>
Copyrights © 2018
