Main Article Content
Abstract
Artificial neural network is an information processing paradigm that is inspired by biological neural cell systems, like the brain, that processes information. The purpose of this research is to develop neural networks to predict the price of food commodities using backpropagation method. The research was conducted by using the rate of monthly price of food commodities in Palu from January 2011 - December 2015. The data is used to predict food commodity prices forduring 2016. The backpropagation networks consists of three layers. The first layer of input is constructedin the form of monthly prices of IR 64, ciherang, membramo, cimandi, superwin, sintanur, cisantana, sticky black, sticky white, yellow corn dry, white corn, soybeans, peanuts, green beans, cassava, sweet potato, onion, garlic, red pepper large, red pepper curls, cayenne pepper, cabbage round, potatoes, tomatoes, carrots, cauliflower, beans, onion, avocado, red apples, green apples, oranges, jackfruit, mango, pineapple, papaya, banana, banana horns, rambutan, bark, olive, durian, watermelon, and mangosteen from January – December that consist of 12 variables. One hidden layer consistof five neurons and the other one is the output, that is the food commodity prices. The training process shows that on a maximum iterations on 500, constant learning rate 0,3 and 0,6 momentum, the predictions have 97.92% of level accuracy. The identification resultof food commodity prices behavior in Palu is predicted as follow: IR 64 Rp7.387, ciherang Rp8.182, membramo Rp8.150, cimandi Rp8.131, superwin Rp8.228, sintanur Rp8.660, cisantana Rp8.122, black sticky rice Rp21.383, white sticky rice Rp16.558, dry yellow corn Rp5.983, white corn Rp9.283, soybeans Rp14.600, peanuts Rp20.008, green beans Rp16.375, cassava Rp8.225, sweet potato Rp8. 542, red onion Rp28.550, garlic Rp21.208, red chili Rp27.308, curly red chili Rp23.650, cayenne Rp36.450, round cabbage Rp6.833, Rp12.067 potatoes, tomatoes Rp6.108, carrots 11.000, cauliflower Rp8.625, beans Rp10.333, scallion Rp25.242, avocado 11.000, red apple Rp29.023, green apple Rp31.067, orange Rp6.083, jackfruit Rp23.483, mango Rp11.187, pineapple Rp8.183, papaya Rp10.600, bananas Rp8.481, horn banana Rp2.683, rambutan Rp8.450, barking Rp5.625, tan Rp8.366, durian Rp19.208, watermelon Rp14.528 and mangosteen Rp18.067. It is predicted that the food commodity prices increased monthly.