Construction Model Using Machine Learning Techniques for the Prediction of Rice Produce for Farmers

Nowadays, the researches in data mining area have been continuous increasing. Appling data mining to agriculture; for example, the prediction of rice produce for farmers is still challenging. The objective of the research is to propose a model using Machine Learning Techniques comparing between Decision Tree Technique and Neural Network Technique (ANN) for the prediction of rice produce for farmers. Farmers can predict volume of rice produce and selling price. It is helpful for farmers to increase their income. The process of the research follows Cross-industry standard process for data mining (CRISP-DM) process. The model pattern is classified by machine learning techniques experiment with a dataset of farmer records. Performance measure of model pattern uses four options such as Test Options, Cross-Validation Folds 10, Split 80-20, and Use Training Set. After that, four options will be averaged for accuracy. The experimental result shows that the best technique which has highest accuracy can be helpful for farmers in real world.