PENERAPAN QUICKPROPAGATION DAN ATTRIBUTE REDUCTION BERDASARKAN INFORMATION GAIN DAN DISCERNIBILITY MATRIX UNTUK PREDIKSI STROKE ULANGAN - , JAZILUL ATHOYA ([Yogyakarta] : Universitas Gadjah Mada, 2014)
The death can be caused by illness, one of them are because of reccurent stroke. Recurrent stroke can be predicted because there are some factors that raising the risk of occuring after having the first stroke. This reccurent stroke problem can be predicted thus to solving this problem a classifier that able to relate factors into right prediction is needed. Artificial neural network with quickpropagation is an improved and faster algorithm based on backpropagtaion. But there is one problem, function of an artificial neural netwrok will grows complex if it's component is not on suitable parameters including the dimension of input variables. There is a chance that a variables from acquired stroke data is not relevan nor on good distribution. Neural networks can be resulting with low accracy. To avoid this problem, taking relevant subset from all variables is used which this method is called attribute reduction. Using this method neural network can run better and get high accuracy on predictioning reccurent stroke problem. In order to predict reccurent stroke problem, sample data from this reccurent stroke is needed. Sample data is obtained from RSUP Dr. Sardjito. Sample data is processed beforehand so quickpropagation can do prediction. This research run some test from sample data to get accuracy. Result of this research shows that quickpropagation can predict reccurent stroke problem with 74% accuracy without attribute reduction. Using attribute reduction with high-entropy subset is resulting 74% accuracy and 76% accuracy with discernibilty matrix. Keyword : neural network, quickpropagation, attribute reduction, recurrent stroke