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dc.contributor.authorGaffar, Emmilya Umma Aziza
dc.contributor.authorGani, Irwan
dc.contributor.authorHaviluddin, Haviluddin
dc.contributor.authorGaffar, Achmad Fanany Onnlita
dc.contributor.authorAlfred, Rayner
dc.date.accessioned2020-03-10T05:00:03Z
dc.date.available2020-03-10T05:00:03Z
dc.date.issued2019-03-25
dc.identifier.isbn978-1-5386-5280-0
dc.identifier.urihttp://repository.unmul.ac.id/handle/123456789/3903
dc.description.abstractIn a country, economic growth indicators are measured by GDP growth rates. In order to predict GDP growth then, economic situation and economic development strategy have always been used. In this study, GDP contribution at the current price by the industrial sector has been predicted by using heuristic network method. There were nine GDP growth variables including (1) Agriculture, Livestock, Forestry, Fishery, (2) Mining & Quarrying, (3) Manufacturing Industry, (4) Electricity, Gas, Water supply, (5) Construction, (6) Trade, Hotel, Restaurant, (7) Transport& Communication, (8) Finance, Real Estate & Business Services, and (9) Services in the period 2001-2016 have been analyzed. Experimental results show that the error rate forecasting in 2017 is less than 10%. The results show that intelligent computing method (heuristic network) can be an alternative method for predicting the contribution of GDP. This method predicts fairly quickly, significantly and produces an acceptable error prediction.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectEconomic indicators; Training; Data models; Time series analysis; Agriculture; Informaticsen_US
dc.titleA Heuristic Network for Predicting the Percentage of Gross Domestic Product Distributionen_US
dc.typeArticleen_US


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