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Adaptive Moment Estimation On Deep Belief Network For Rupiah Currency Forecasting

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 1 (2019) · pp. 31–42

DOI
10.22146/ijccs.39071
ISSN
2460-7258, 1978-1520
Published
2019-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

One approach that is often used in forecasting is artificial neural networks (ANN), but ANNs have problems in determining the initial weight value between connections, a long time to reach convergent, and minimum local problems.Deep Belief Network (DBN) model is proposed to improve ANN's ability to forecast exchange rates. DBN is composed of a Restricted Boltzmann Machine (RBM) stack. The DBN structure is optimally determined through experiments. The Adam method is applied to accelerate learning in DBN because it is able to achieve good results quickly compared to other stochastic optimization methods such as Stochastic Gradient Descent (SGD) by maintaining the level of learning for each parameter.Tests are carried out on USD / IDR daily exchange rate data and four evaluation criteria are adopted to evaluate the performance of the proposed method. The DBN-Adam model produces RMSE 59.0635004, MAE 46.406739, MAPE 0.34652. DBN-Adam is also able to reach the point of convergence quickly, where this result is able to outperform the DBN-SGD model.

Keywords

Collaboration network of the authors

31 authors · 40 co-authorship links · 9 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Muhammadiyah University of Surakarta
  • Universitas Maritim Raja Ali Haji
  • Malikussaleh University
  • Universitas Teknokrat Indonesia
  • Sebelas Maret University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yohanes SuyantoUniversitas Gadjah Mada2
Yayang Eluis Bali MawartikaUniversitas Gadjah Mada2
Harliana HarlianaAffiliation unknown1
Bhakti SusiloAffiliation unknown1
Mutammimul UlaMalikussaleh University1
Harry Ganda NugrahaUniversitas Gadjah Mada1
Izmy Alwiah MusdarUniversitas Gadjah Mada1
Devid Haryalesmana WahidUniversitas Gadjah Mada1
Victor PaskalathisUniversitas Gadjah Mada1
Pradita Eko Prasetyo UtomoUniversitas Gadjah Mada1
Azizah FatmawatiMuhammadiyah University of Surakarta1
Nisa RNAMuhammadiyah University of Surakarta1
Muhammad FahrurroziUniversitas Gadjah Mada1
Ferdi ChahyadiUniversitas Maritim Raja Ali Haji1
Hendra KurniawanUniversitas Maritim Raja Ali Haji1
Sigit PriyantaUniversitas Gadjah Mada1
Winda YulitaUniversitas Gadjah Mada1
Muh Amin NurrohmatUniversitas Gadjah Mada1
Muhammad Asaduddin HazaziUniversitas Gadjah Mada1
Auliya Rahman IsnainUniversitas Teknokrat Indonesia1
Taufan Bagus Dwi Putra AditamaUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Ainil MardiahUniversitas Gadjah Mada1
Fauzan IshlakhuddinUniversitas Gadjah Mada1
Annisaa UtamiUniversitas Gadjah Mada1
Astrid Noviana ParadhitaSebelas Maret University1
Anny Kartika SariUniversitas Gadjah Mada1
Dian EkapratiwiUniversitas Gadjah Mada1

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.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Adaptive  Moment Estimation  On Deep Belief Network For Rupiah Currency Forecasting</dc:title>
	<dc:creator>Prabowo, Abram Setyo</dc:creator>
	<dc:creator>Sihabuddin, Agus</dc:creator>
	<dc:creator>SN, Azhari</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">DBN;Deep Belief Network;Adam;Gradient Descent Optimazation;Forecasting</dc:subject>
	<dc:description xml:lang="en-US">One approach that is often used in forecasting is artificial neural networks (ANN), but ANNs have problems in determining the initial weight value between connections, a long time to reach convergent, and minimum local problems.Deep Belief Network (DBN) model is proposed to improve ANN's ability to forecast exchange rates. DBN is composed of a Restricted Boltzmann Machine (RBM) stack. The DBN structure is optimally determined through experiments. The Adam method is applied to accelerate learning in DBN because it is able to achieve good results quickly compared to other stochastic optimization methods such as Stochastic Gradient Descent (SGD) by maintaining the level of learning for each parameter.Tests are carried out on USD / IDR daily exchange rate data and four evaluation criteria are adopted to evaluate the performance of the proposed method. The DBN-Adam model produces RMSE 59.0635004, MAE 46.406739, MAPE 0.34652. DBN-Adam is also able to reach the point of convergence quickly, where this result is able to outperform the DBN-SGD model.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/39071</dc:identifier>
	<dc:identifier>10.22146/ijccs.39071</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 1 (2019): January; 31-42</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/39071/23717</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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