CLASSIFYING POISONOUS AND EDIBLE MUSHROOMS IN THE AGARICUS AND LEPIOTA FAMILY USING MULTILAYER PERCEPTION

Classification is one of the applications of feed-forward Artificial Neural Network (ANN). Classification can map data to predefined classes or groups. It is referred to as a supervised learning, because before examining data the classes are always determined. Multi-Layer Perception, is a supervised neutral networks model that is use to train and test data to build a model. In this experiment. Multi-Layer Perception is used to train the Data set to produce a model to make prediction of classifying .After preparing the Mushrooms data for training, only 8124 of dataset instances used to be train. Software used to mining data in this project is Neural Connection Version 2.0. This report, generally explaining the Classification, Multi-Layer Preceptor, Back propagation, Mushrooms, and details on the mining activity done to the selected datasets, to determine whether Mushroom's attribute is edible or Poison.