Interconnection Learning between Economic Indicators in Indonesia Optimized by Genetic Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 339)

Abstract

Economic is important issue in a country since it is conducted by many sectors. Knowing stability of economic condition can be looked at by predicting the economic indicator. Unfortunately, the prediction that had been done to each of economic indicator did not concern about interconnection of them while predicting it. Because of that, learning about dependability of economic indicator still need further research about it. In other side, economic as knowledge that complex and chaos need differential dynamic to face the problem inside. Based on those reasons, this research not only observed about interconnection between indicators economic while predicting, but also needed differential dynamic which had been optimized by genetic algorithm. System got 20% until 80% for the accuracy system. The reason of why accuracy 80% was gotten because of using the same characteristics of economic indicators, ex. when system observed GDP and GNI together. Using similar data trend influenced the fitness function in GA able to optimized differential dynamic while doing prediction of economic indicators. Whereas, the decrease accuracy around 20% until 40% was came by using different characteristic of economic indicator. It can be found when learning dependable of GDP and Inflation while predict times series for GDP. Based on this research, it can be concluded that GA is able in optimizing learning the dependability of economic indicator’s Indonesia. Moreover, it can be said that using the same characteristic indicator economic give better result for GA to learning the dependability of economic indicator than not. It can be said indirectly that government should concern about value of indicators economic that have the same characteristics when monitoring economic condition.

Keywords

Economic Indicator’s Indonesia Interconnection Learning Differential Dynamic Genetic Algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Telkom School oof ComputingTelkom UniversityBandungIndonesia

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