SISTEM PERAMALAN HASIL PRODUKSI JAGUNG DI KABUPATEN SUMENEP DENGAN PENDEKATAN JARINGAN SYARAF TIRUAN BACKPROPAGATION

Ach Dafid, Hanifudin Sukri, Mahrus Sholeh

Abstract


Forecasting is an attempt to predict future conditions by testing past data. This forecasting is carried out on corn harvest results based on previous corn harvest data including land area, harvest area, and productivity, using the Backpropagation Artificial Neural Network forecasting system. Because the amount of corn harvest data in Sumenep Regency is very complex and changing, the backpropagation method is very suitable to be applied because it is able to handle complex and changing data. The data used in this study were collected from the book “Sumenep in Figures”. The corn production data used were from 2011 to 2023. The results of the study showed that in the scenario of varying the number of learning rates with values of 0.001, 0.2, 0.4, and 0.8, it was found that the smaller the learning rate in the Backpropagation Artificial Neural Network, the better the MSE value in the validation process. The MSE value from the results of testing learning rates of 0.001, 0.2, 0.4, and 0.8 is 0.008998. In the scenario of varying the number of iterations of 100, 500, and 1000, it is concluded that the more iterations in the Backpropagation Neural Network training, the better the MSE value in the validation process. The prediction results in the 2024 corn harvest test showed good and accurate results with a predicted value per June of 336 tons and a monthly error value of 0.0256 so that the prediction results were higher than the actual data.

Keywords: ANN, Backpropagation, Forcasting System, Maize.


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DOI: https://doi.org/10.21107/simantec.v12i2.26036

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