PERFORMANCE OF BACKPROPAGATION NEURAL NETWORKS FOR CULTIVATION DAILY LOADS IN JAW A CENTRAL-DIY - Kustono Kustono; Yuwono Indro Hatmojo (Institute of Research and Community Services, Universitas Negeri Yogyakarta, 2006)
Goals of this research are implementing Artificial Neural Network (ANN) algorithm for load forecasting and getting its performance. The training data was takenfrom UPB Ungaran. The performance can be got through comparing ANN test result with the real load at that time. The research methodology usc experimental and design models approach. The phases of this research were: I. analyzing and identifying of need 2. developing of load forecasting application software with C programming. 3. entering and training the data to get data pattern.The result of this research. the load forecasting result by ANN was close with UPB loadforecasting. but several ANN test result have more deviation than UPB. because number of training data was less. so the forecasting pattern 111as not too accurate Beside that. another possibility was the number of iteration must be more than / ()()(J timesiterations in order to get more less error. There was 33,3% of ANN result that has more less deviation, although the number of training data was not different, because that data has no extrem variation, sothe pattern was faster to be recognized. Generally, ANN will give an accurate pattern recognation if the data is valid and the number of the data is quite enough.