Abstract

Aim of study to optimize the oil palm yield amount by studying parameters of land quality and climate, determines which of them is distinctly effective on oil palm yield amount, develops ANN model and simulation of Oil Palm production by using MATLAB software and Design Expert software, conducted an experiment to determine the effect of the number of neurons and the number of hidden layers in the network ANN is used. Across the optimization procedures obtained the best ANN architecture is 8 neurons in input layer -5 neurons in the hidden layer and -2 neuron in the output layer to obtain the best model of oil palm productivity prediction with a value of R 0.989 and MSE: 0.013, training Error 1.1%, testing error 1.9% and validation error 1.19%. The results of simulation and Independent Variable Importance show that the average accuracy percentage simulation is 0.9867% and MSE 0.0513%. The climatic changes that influenced the simulation are very high, where the relative humidity recorded on the proportion of impact of up to 100%, while the recorded rainy days, which is ranked second in influence was almost 90% and the effect of temperature was up to 70%. The influence of several climatic changes that decrease the quantity of rainfall, Rain days, Temperature rise, Evaporation and increasing Humidity, reduces the productivity of oil palm plantations for 2.35 tons/ha/year. This research concludes that ANN can be used to predict the production of palm oil based on the quality of land and local climate with very good results.

Keywords:

Artificial neural network, climate, FFB, land quality, oil palm, stepwise method,

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