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Damanik, Alan Boy Sandy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Indikator Utama Ekonomi Dunia Damanik, Alan Boy Sandy; Bimantoro, Agung
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 2, No 2 (2018): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (738.708 KB) | DOI: 10.30645/j-sakti.v2i2.80

Abstract

Economics is one of the most important aspects in the world. Economics greatly determines the progress and development of a country. However, there are still many countries with low economic levels. Therefore the aim of this study is to predict and determine the level of the main indicators of the world economy as one of the anticipatory steps to further increase the level of the country's economy. World Economic Indicator Data to be used is sourced from Bloomberg and Bank Indonesia. To find out further developments, it is necessary to research the existing data. The algorithm used is Backpropagatian Neural Network. Data analysis was carried out using artificial neural network method using Matlab R2011b software. The study uses 5 architectural models. The best network architecture produced is 3-43-1 with an accuracy rate of 86% and the Mean Squared Error (MSE) value is 1.336593.
Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Indikator Utama Ekonomi Dunia Damanik, Alan Boy Sandy; Bimantoro, Agung
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 2, No 2 (2018): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v2i2.80

Abstract

Economics is one of the most important aspects in the world. Economics greatly determines the progress and development of a country. However, there are still many countries with low economic levels. Therefore the aim of this study is to predict and determine the level of the main indicators of the world economy as one of the anticipatory steps to further increase the level of the country's economy. World Economic Indicator Data to be used is sourced from Bloomberg and Bank Indonesia. To find out further developments, it is necessary to research the existing data. The algorithm used is Backpropagatian Neural Network. Data analysis was carried out using artificial neural network method using Matlab R2011b software. The study uses 5 architectural models. The best network architecture produced is 3-43-1 with an accuracy rate of 86% and the Mean Squared Error (MSE) value is 1.336593.
Co-Authors Bimantoro, Agung