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Implementasi Model Long Short Term Memory (LSTM) Pada Proyeksi Harga Saham (Studi Kasus: PT. Pertamina Geothermal Energy (Persero))

ESTIMASI: Journal of Statistics and Its Application · Vol. 6 No. 2 (2025) · pp. 226–234

DOI
10.20956/ejsa.v6i2.44963
ISSN
2721-379X, 2721-3803
Published
2025-08-04
Publisher
Hasanuddin University
Language
en

Abstract

This research presents a comprehensive analysis of the Long Short Term Memory (LSTM) method in projecting the stock price of PT. Pertamina Geothermal Energy (Persero). Utilizing daily stock price data, the LSTM model achieves a high level of accuracy with a Mean Absolute Percentage Error (MAPE) value of 0.84%. The LSTM's gate mechanism (input, forget, output) enables it to store long-term information, controlling the flow of information to update memory, delete irrelevant data, and generate predictions. Optimized with backpropagation through time (BPTT) and activation functions, the LSTM model proves effective in investment decision making, providing valuable insights for investors and market players to anticipate stock price fluctuations. This research demonstrates the great potential of machine learning in financial analysis, particularly in stock price projection and time series analysis. The results indicate that LSTM can be a valuable tool for investors and financial analysts, enhancing their ability to make informed decisions.

Keywords

Collaboration network of the authors

40 authors · 157 co-authorship links · 2 across different organisations · showing the 40 most connected

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

  • Universitas Halu Oleo
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Lilis LaomeAffiliation unknown12
Ruslan RuslanAffiliation unknown9
Bahriddin AbapihiAffiliation unknown7
Baharuddin SaidAffiliation unknown6
Iskandar PattihAffiliation unknown5
Lestari DaswanInstitut Agama Islam Negeri Kendari4
Andi Tenri AmpaInstitut Agama Islam Negeri Kendari4
Mukhsar MukhsarAffiliation unknown4
Gusti N.A. WibawaAffiliation unknown4
Fahinu FahinuAffiliation unknown3
Andi Husni SitepuAffiliation unknown3
Lambertus LambertusAffiliation unknown3
Rahmaliah SahupalaAffiliation unknown3
Safira Nur AdhanUniversitas Halu Oleo3
Ida UsmanAffiliation unknown3
Kodirun KodirunUniversitas Halu Oleo2
Syerly KlaraAffiliation unknown2
Busnawir BusnawirUniversitas Halu Oleo2
Andi TenriawaruUniversitas Halu Oleo2
Andi PatombongiAffiliation unknown1
Baharuddin BaharuddinAffiliation unknown1
La Ode SaidiInstitut Agama Islam Negeri Kendari1
Amirullah AmirullahAffiliation unknown1
Hartini HartiniAffiliation unknown1
M SetiawanInstitut Agama Islam Negeri Kendari1
Nina ApriyantiInstitut Agama Islam Negeri Kendari1
Idul SetiawanInstitut Agama Islam Negeri Kendari1
Indiyanti IndiyantiAffiliation unknown1
Amiruddin BAffiliation unknown1
Nana SumarnaUniversitas Halu Oleo1
Cici ImayantiAffiliation unknown1
Sitti Kurnia Purnama AksanAffiliation unknown1
Deti Sri RahayuAffiliation 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/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Implementasi Model Long Short Term Memory (LSTM) Pada Proyeksi Harga Saham (Studi Kasus: PT. Pertamina Geothermal Energy (Persero))</dc:title>
	<dc:creator xml:lang="en">Arisona, Dian Christien</dc:creator>
	<dc:creator xml:lang="en">Agusrawati, Agusrawati</dc:creator>
	<dc:creator xml:lang="en">Makkulau, Makkulau</dc:creator>
	<dc:creator xml:lang="en">Yahya, Irma</dc:creator>
	<dc:creator xml:lang="en">Wibawa, Gusti Ngurah Adhi</dc:creator>
	<dc:creator xml:lang="en">Baharuddin, Baharuddin</dc:creator>
	<dc:creator xml:lang="en">Fahyuni, Putri Riski</dc:creator>
	<dc:subject xml:lang="en">Stock Price Forecasting</dc:subject>
	<dc:subject xml:lang="en">Time Series</dc:subject>
	<dc:subject xml:lang="en">LSTM</dc:subject>
	<dc:subject xml:lang="en">Financial Analysis</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:description xml:lang="en">This research presents a comprehensive analysis of the Long Short Term Memory (LSTM) method in projecting the stock price of PT. Pertamina Geothermal Energy (Persero). Utilizing daily stock price data, the LSTM model achieves a high level of accuracy with a Mean Absolute Percentage Error (MAPE) value of 0.84%. The LSTM's gate mechanism (input, forget, output) enables it to store long-term information, controlling the flow of information to update memory, delete irrelevant data, and generate predictions. Optimized with backpropagation through time (BPTT) and activation functions, the LSTM model proves effective in investment decision making, providing valuable insights for investors and market players to anticipate stock price fluctuations. This research demonstrates the great potential of machine learning in financial analysis, particularly in stock price projection and time series analysis. The results indicate that LSTM can be a valuable tool for investors and financial analysts, enhancing their ability to make informed decisions. </dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2025-08-04</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/ESTIMASI/article/view/44963</dc:identifier>
	<dc:identifier>10.20956/ejsa.v6i2.44963</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 6, No. 2, Juli, 2025 : Estimasi; 226-234</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/44963/13224</dc:relation>
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