DATA MINING APPROACH FOR PREDICTION OF RICE PRODUCTION USING BACKPROPAGATION ARTIFICIAL NEURAL NETWORK METHOD

Abstract

As an agricultural country, Indonesia is one of the major producers of rice. Up to this days, the regional government of Indonesia conducted calculations of rice production using trend prediction methods that produce predictions with low accuracy. Therefore, an effective solution was proposed to calculate the amount of rice production needed in the planning process and government activities. This research was conducted to create a system that can predict rice production using Artificial Neural Networks (ANN) on Data Mining. The stages of the research carried out were data collection, pre-processing, prediction by methods, and testing according to the design of prediction models, namely epoch parameters, momentum, learning levels, and hidden layers to produce high accuracy.