Fuzzy-Klassen Model for Development Disparities Analysis based on Gross Regional Domestic Product Sector of a Region

Analysis of regional development imbalances quadrant has a very important meaning in order to see the extent of achievement of the development of certain areas as well as the difference. Factors that could be used as a tool to measure the inequality of development is to look at the average growth and development contribution of each sector of Gross Regional Domestic Product (GRDP) based on the analyzed region and the reference region. This study discusses the development of a model to determine the regional development imbalances using fuzzy approach system, and the rules of typology Klassen. The model is then called fuzzy-Klassen. Implications Product Mamdani fuzzy system is used in the model as an inference engine to generate output after defuzzyfication process. Application of MATLAB is used as a tool of analysis in this study. The test a result of Kota Cilegon is shows that there are significant differences between traditional Klassen typology analyses with the results of the model developed. Fuzzy model-Klassen shows GRDP sector inequality Cilegon City is dominated by Quadrant I (K4), where status is the sector forward and grows exponentially. While the traditional Klassen typology, half of GRDP sector is dominated by Quadrant IV (K4) with a sector that is lagging relative status.

Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition

There are several training algorithms for backpropagation method in neural network. Not all of these algorithms have the same accuracy level demonstrated through the percentage level of suitability in recognizing patterns in the data. In this research tested 12 training algorithms specifically in recognize data patterns of test validity. The basic network parameters used are the maximum allowable epoch = 1000, target error = 10-3, and learning rate = 0.05. Of the twelve training algorithms each performed 20 times looping. The test results obtained that the percentage rate of the great match is trainlm algorithm with alpha 5% have adequate levels of suitability of 87.5% at the level of significance of 0.000. This means the most appropriate training algorithm in recognizing the the data pattern of test validity is the trainlm algorithm.