APPLICATION OF AN ARTIFICIAL NEURAL NETWORK (ANN) MODEL FOR PREDICTING OIL PALM FRESH FRUIT BUNCH (FFB) YIELD BASED ON RAINFALL AND THE YIELD PREVIOUSLY

  • Iman Yani Harahap Indonesian Oil Palm Research Institute
  • M. Edwin Syahputra Lubis Indonesian Oil Palm Research Institute
Keywords: Artificial Neuron Network, External input, training, validation, testing, model architecture

Abstract

Abstract. To predict oil palm yield in 2018 at 4 Indonesian Oil Palm Research Institue field Trial Plantation (Padang Mandarsah, Dalu-dalu, Bukit Sentang, and Aek Pancur), then it was built an Artificial Neuron Network (ANN) model. The data used were monthly yield and rainfall during 2013-2017. The model output taking by the relation of non-linear Autoregressive to  the rainfall external input (NARX). The model built processing including training using the data 2013-2015, validation using the data 2016, testing using the data 2017. From the testing model result, were taken a good fit model architecture n-d-h-o (variable input,n ; d-tapped delayed , d, node hidden, h; output layer, o) and correlation coefficient (r) between output model and actual data for each plantation. Padang Mandarsah 2-3-4-1 with r= 0,84 ; Dalu-dalu 2-24-5-1 with r = 0,74; Bukit Sentang 2-24-10-1 with r = 0,84, and Aek Pancur 2-3-5-1 with r = 0,86.

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Author Biographies

Iman Yani Harahap, Indonesian Oil Palm Research Institute
M. Edwin Syahputra Lubis, Indonesian Oil Palm Research Institute
Published
2018-08-01