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Implementation of recurrent neural network for the forecasting of USD buy rate against IDR

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 13 No. 4 (2023)

DOI
10.11591/ijece.v13i4.pp4567-4581
ISSN
2722-2578, 2088-8708
Published
2023-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation function for the hidden layer, while the linear transfer function is the most appropriate activation function for the output layer. Based on the results of training and forecasting the USD against IDR currency, the Elman BPTT method is better than the Jordan BPTT method, with the best iteration being the 4000 th iteration for both. The lowest root mean square error (RMSE) values for training and forecasting produced by Elman BPTT were 0.073477 and 122.15 the following day, while the Jordan backpropagation RNN method yielded 0.130317 and 222.96 also the following day.

Collaboration network of the authors

19 authors · 34 co-authorship links · 6 across different organisations

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  • Bandung Institute of Technology
  • Politeknik Penerbangan Surabaya
  • Indonesia University of Education
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Masayu Leylia KhodraBandung Institute of Technology3
Dwi Hendratmo WidyantoroBandung Institute of Technology3
E. Aminudin AzizIndonesia University of Education3
Tjong Wan SenBandung Institute of Technology1
Arry Akhmad ArmanBandung Institute of Technology1
Rila MandalaBandung Institute of Technology1
Budi RahardjoBandung Institute of Technology1
Muhammad Nur Kholish AbdurrazaqBandung Institute of Technology1
Muhammad Adi Khairul AnsharyBandung Institute of Technology1
Saripudin SaripudinBandung Institute of Technology1
Modestus Oliver AsaliBandung Institute of Technology1
Toto IndriyantoBandung Institute of Technology1
Yudi Satria GondokaryonoBandung Institute of Technology1
Rina MardiatiBandung Institute of Technology1
Sony S WibowoAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Implementation of recurrent neural network for the forecasting of USD buy rate against IDR</dc:title><dc:creator>Moonlight, Lady Silk</dc:creator><dc:creator>Trilaksono, Bambang Riyanto</dc:creator><dc:creator>Harianto, Bambang Bagus</dc:creator><dc:creator>Faizah, Fiqqih</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span lang="EN-US"&amp;gt;This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation function for the hidden layer, while the linear transfer function is the most appropriate activation function for the output layer. Based on the results of training and forecasting the USD against IDR currency, the Elman BPTT method is better than the Jordan BPTT method, with the best iteration being the 4000&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; iteration for both. The lowest root mean square error (RMSE) values for training and forecasting produced by Elman BPTT were 0.073477 and 122.15 the following day, while the Jordan backpropagation RNN method yielded 0.130317 and 222.96 also the following day.&amp;lt;/span&amp;gt;&amp;lt;p&amp;gt; &amp;lt;/p&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2023-08-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v13i4.pp4567-4581</dc:identifier><dc:identifier>10.11591/ijece.v13i4.pp4567-4581</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 13 No 4 (2023); 4567</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/27297/16735</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>