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Optimization of Long Short Term Memory Model for Gold Price Prediction Using Adaptive Moment Estimation

Jurnal Matematika, Statistika dan Komputasi · Vol. 21 No. 3 (2025) · pp. 669–683

DOI
10.20956/j.v21i3.42872
ISSN
2614-8811, 1858-1382
Published
2025-05-14
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

The era of globalization and rapidly evolving economic dynamics place the financial sector at the center of attention for market participants and investors. Financial instruments such as gold play a crucial role as hedging tools and portfolio diversification, yet face significant challenges due to complex and unpredictable price fluctuations. Artificial intelligence technology, particularly Long Short Term Memory (LSTM) models and Adaptive Moment Estimation (ADAM), offers relevant solutions for predicting financial asset prices with strong temporal fluctuations, such as gold prices. This research aims to optimize the LSTM model using the ADAM technique to enhance the accuracy of gold price predictions. The research findings indicate that the LSTM model optimized with ADAM can provide highly accurate gold price predictions with low error rates. The LSTM model used has 3 layers with 128, 64, and 32 units, and uses 100 epochs in the model training process. At the 100th epoch, the final loss obtained was 0,000336. Model evaluation results showed a MAPE of around 0,0108 or 1,08% an accuracy rate of about 98,92%, and a low loss value of 0,00025.

Keywords

Collaboration network of the authors

40 authors · 105 co-authorship links · showing the 40 most connected

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

  • State University of Medan
  • Satya Wacana Christian University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Musrini MuisAffiliation unknown2
Nuzla Af’idatur RobbaniyyahAffiliation unknown2
La Onu La OlaAffiliation unknown1
Utama K. PangerangAffiliation unknown1
Karina NurwijayantiAffiliation unknown1
Sitti Wahidah MasnaniAffiliation unknown1
Tirsa AminahAffiliation unknown1
Fauziah Khairani LubisState University of Medan1
Adi SetiawanSatya Wacana Christian University1
Khairul AnsharAffiliation unknown1
Zainuddin GintingAffiliation unknown1
Nirwanto NirwantoAffiliation unknown1
Lisa HarsyiahAffiliation unknown1
Lailia AwalushaumiAffiliation unknown1
Bulqis Nebulla SyechahAffiliation unknown1
Satriawan PradanaAffiliation unknown1
Rohmatul UlyaAffiliation unknown1
Ismi AsmawatiAffiliation unknown1
Andi Lukman IrwanAffiliation unknown1
Nurlinah NurlinahAffiliation unknown1
Al MusannaAffiliation unknown1
Andi SantiAffiliation unknown1
Hamidin RasuluAffiliation unknown1
Janiah HusenAffiliation unknown1
Angela WulansariAffiliation unknown1
Ikrima M. Mustafa MustafaAffiliation unknown1
Asep NurhikmatAffiliation unknown1
Nancy KiayAffiliation unknown1
Nurjanna AlbaarAffiliation unknown1
Mila FatmawatiAffiliation unknown1
Nabel Ahmed A MansourAffiliation unknown1
Tirta WiryaAffiliation unknown1
Gando SariAffiliation unknown1
Nursama HeruAffiliation unknown1
Resa Wantika UtamiAffiliation unknown1
Burhan DjamaluddinAffiliation unknown1
Nurlailah NurlailahAffiliation 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.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Optimization of Long Short Term Memory Model for Gold Price Prediction Using Adaptive Moment Estimation</dc:title>
	<dc:creator xml:lang="en">amryliana, septia</dc:creator>
	<dc:creator xml:lang="en">Bahri, Syamsul</dc:creator>
	<dc:creator xml:lang="en">Alfian, Muhammad Rijal</dc:creator>
	<dc:subject xml:lang="en">Gold Price Prediction</dc:subject>
	<dc:subject xml:lang="en">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en">Long Short Term Memory</dc:subject>
	<dc:subject xml:lang="en">Adaptive Moment Estimation</dc:subject>
	<dc:description xml:lang="en">The era of globalization and rapidly evolving economic dynamics place the financial sector at the center of attention for market participants and investors. Financial instruments such as gold play a crucial role as hedging tools and portfolio diversification, yet face significant challenges due to complex and unpredictable price fluctuations. Artificial intelligence technology, particularly Long Short Term Memory (LSTM) models and Adaptive Moment Estimation (ADAM), offers relevant solutions for predicting financial asset prices with strong temporal fluctuations, such as gold prices. This research aims to optimize the LSTM model using the ADAM technique to enhance the accuracy of gold price predictions. The research findings indicate that the LSTM model optimized with ADAM can provide highly accurate gold price predictions with low error rates. The LSTM model used has 3 layers with 128, 64, and 32 units, and uses 100 epochs in the model training process. At the 100th epoch, the final loss obtained was 0,000336. Model evaluation results showed a MAPE of around 0,0108 or 1,08%  an accuracy rate of about 98,92%, and a low loss value of 0,00025.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2025-05-14</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/42872</dc:identifier>
	<dc:identifier>10.20956/j.v21i3.42872</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 21 No. 3 (2025): MAY 2025; 669-683</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/42872/12798</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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