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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) · hlm. 669–683

DOI
10.20956/j.v21i3.42872
ISSN
2614-8811, 1858-1382
Terbit
2025-05-14
Penerbit
Department of Mathematics, Hasanuddin University
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

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.

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  • State University of Medan
  • Satya Wacana Christian University
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Nuzla Af’idatur RobbaniyyahAfiliasi tidak diketahui2
La Onu La OlaAfiliasi tidak diketahui1
Utama K. PangerangAfiliasi tidak diketahui1
Sitti Wahidah MasnaniAfiliasi tidak diketahui1
Tirsa AminahAfiliasi tidak diketahui1
Fauziah Khairani LubisState University of Medan1
Adi SetiawanSatya Wacana Christian University1
Khairul AnsharAfiliasi tidak diketahui1
Zainuddin GintingAfiliasi tidak diketahui1
Nirwanto NirwantoAfiliasi tidak diketahui1
Lisa HarsyiahAfiliasi tidak diketahui1
Lailia AwalushaumiAfiliasi tidak diketahui1
Bulqis Nebulla SyechahAfiliasi tidak diketahui1
Satriawan PradanaAfiliasi tidak diketahui1
Rohmatul UlyaAfiliasi tidak diketahui1
Ismi AsmawatiAfiliasi tidak diketahui1
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Al MusannaAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 5 Nov 2026.
Metadata asli (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>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<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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