<![CDATA[Oil palm is a tropical plant of the Indonesian natural palmae group which has a tropical climate. The growth and harvest of oil palm also depends on fertilizers and the rainfall that falls every day. To get good production results it requires high ability and a lot of labor. Each production result certainly does not always increase, there must be a time when the production will decrease, therefore an algorithm is needed to predict it so that companies can find out the development of palm oil production in the future. In this study, researchers used the Backpropagation Algorithm. The Backpropagation Algorithm is an algorithm that functions to reduce the error rate by adjusting the weight based on the desired output and target, there are 5 training and data testing architectural models, namely 2-21-1, 2-22-1, 2-24-1, 2-26 -1 and 2-28-1. From the results of testing data on oil palm production, the best architectural model is obtained, namely 2-22-1 which shows that the target is reduced by the output that SSE is 0.35206024, from the data obtained, the performance of the calculation of artificial neural networks with the Backpropagation Algorithm gets an accuracy of 83.3%. . So that it can be used as a benchmark in predicting palm oil production, seen from the comparison of the desired target with the predicted target]]>
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