Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Indikator Utama Ekonomi Dunia

Alan Boy Sandy Damanik, Agung Bimantoro
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引用次数: 1

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.
模拟神经网络的应用来预测世界经济的关键指标
经济学是世界上最重要的方面之一。经济在很大程度上决定着一个国家的进步和发展。然而,仍有许多国家经济水平较低。因此,本研究的目的是预测和确定世界经济主要指标的水平,作为进一步提高国家经济水平的预期步骤之一。使用的世界经济指标数据来自彭博社和印尼银行。为了找出进一步的发展,有必要研究现有的数据。使用的算法是反向传播神经网络。采用Matlab R2011b软件,采用人工神经网络方法进行数据分析。该研究使用了5种建筑模型。得到的最佳网络结构为3-43-1,准确率为86%,均方误差(MSE)为1.336593。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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