Assessment of the Average Level of TOEFL Score by using SOM (Self Organizing Map) and K-Mean Clustering Techniques

Abstract

Economic growth as measured by GDP growth rates and economic growth set as an increase in GDP strongly helps government predictions about the economic situation and the formation of economic development strategies. This measurement is done by combining mathematical and computer technology to make qualitative and quantitative predictions scientifically and appropriately for economic growth trends. It is a good practical sense to use scientific and proven methods to predict future GDP development trends of a particular economy. In some cases, machine learning methods have proven to be better forecasting results than statistical methods. A Deep Neural Network (DNN) is one type of ANN (Artificial Neural network) architecture based on deep MLP (Multi Layer Perceptron), which uses Deep Learning training techniques. This study proposes the use of DNN to predict the percentage of GDP distribution at current prices by industry sector. In this case, the DNN used will have multiple outputs as many industry sectors. The aim of this study is how to predict for the next period with the smallest possible prediction errors by using DNN.