Analisis Jaringan Saraf Tiruan Untuk Prediksi Luas Panen Biofarmaka di Indonesia - Eko Hartato; Daniel Sitorus; Anjar Wanto (semanTIK, 2018)
Analysis of a prediction is very important to do in a study, so that research becomes more precise and directed. Just as in predicting the extent of biopharmaceutical harvests in Indonesia, it is necessary to study and use appropriate methods to obtain optimal results. This research is expected to be widely used for both local government and biopharmaca farmers as one of the study materials in the development of biopharmaca harvest production, as well as for academics as research material especially related to agriculture and health. The data used in this research is the data of Harvested Area of Biopharmaceutical in Indonesia from National Bureau of Statistics from 2012 until 2016. This research uses the method of artificial neural network Backpropagation using 5 architectural models, namely: 3-3-1 later it will generate predictions with an accuracy rate of 80%, 3-4-1 = 87%, 3-5-1 = 73%, 3-6-1 = 60%, and 3-8-1 = 73% ,. So obtained the best architectural model using 3-4-1 model that yields an accuracy of 87%, MSE 0.062235528 with error rate used 0.001 to 0.05. Thus, this model is good enough to predict the area of biopharmaca harvest in IndonesiaKeywords—Analysis, Prediction, ANN, Backpropagation, BiopharmacaDOI : 10.5281/zenodo.1402402