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Stock Return Prediction Using Voting Regressor Ensemble Learning

Recursive Journal of Informatics · Vol. 1 No. 2 (2023) · pp. 55–63

DOI
10.15294/rji.v1i2.68048
ISSN
2986-6588, 2963-5551
Published
2023-09-29
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

Abstract. The value of return on stock prices is often used in predicting profits in the process of buying and selling shares based on the calculation of the return on investment. The calculation of the value of return on stock prices can be predicted automatically at certain periods, both weekly and daily Purpose: The problem faced is determining a good algorithm for making predictions due to fluctuating data on stock prices making it difficult to predict. Methods: The stages carried out by the researcher include the data preprocessing stage and then proceed to the Exploratory Data Analysis (EDA) stage to get a pattern from the data, followed by the modeling stage on the data. This research was developed using the Python programming language where the models used to make predictions can be obtained in real-time. Result: The results obtained in this study show that the Voting Regressor has the best model with an error rate of 0.032523 using Root Mean Square Error (RMSE). The results of this study can be further developed to automatically predict stock return values in the future.

Collaboration network of the authors

40 authors · 103 co-authorship links · 19 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.

  • State University of Semarang
  • Institut Teknologi Kalimantan
  • Tun Hussein Onn University of Malaysia
  • Universitas PGRI Palembang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Endang SugihartiState University of Semarang5
Florentina Yuni AriniState University of Semarang3
Rochmad RochmadAffiliation unknown3
Amin SuyitnoAffiliation unknown3
Alamsyah AlamsyahInstitut Teknologi Kalimantan2
Anggyi Trisnawan PutraAffiliation unknown2
Novanka Agnes SekartajiAffiliation unknown2
Irfan Fajar MuttaqinAffiliation unknown2
Dwijanto DwijantoAffiliation unknown2
Putriaji HendikawatiAffiliation unknown2
Mashuri MashuriAffiliation unknown2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimTun Hussein Onn University of Malaysia1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Arif WidiyatmokoState University of Semarang1
Stephani Diah PamelasariState University of Semarang1
Arka YanitamaState University of Semarang1
Melissa Salma DarmawanState University of Semarang1
Aji SaputraUniversitas PGRI Palembang1
Desy Fitria AstutianingtyasAffiliation unknown1
Syafa Nabilah SyahputraAffiliation unknown1
Athoillah AthoillahAffiliation unknown1
Kholiq BudimanAffiliation unknown1
zaenal abidinAffiliation unknown1
Anggoro Sakti AnindhitoAffiliation unknown1
Fajar SujitoAffiliation unknown1
Anita Wahyu MelindaAffiliation unknown1
Isa AkhlisState University of Semarang1
Achmad Wahyu HidayatAffiliation unknown1
Dian Rahman SyahputraAffiliation unknown1
Safit FirmansyahAffiliation unknown1
Anggil AgustaAffiliation unknown1
Fachrizal Ahdy AndoyoAffiliation unknown1
Margareta Ayu RiantikAffiliation unknown1
Dandi Indra WijayaAffiliation unknown1
Hanif Nur CahyaniAffiliation unknown1
Andhika Seno TamtamaAffiliation 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.unnes.ac.id/sju/index.php/rji/oai, first harvested 6 Nov 2026, last seen 6 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">Stock Return Prediction Using Voting Regressor Ensemble Learning</dc:title>
	<dc:creator>Arrohman, Ramadhan Ridho</dc:creator>
	<dc:creator>Arifudin, Riza</dc:creator>
	<dc:description xml:lang="en-US">Abstract. The value of return on stock prices is often used in predicting profits in the process of buying and selling shares based on the calculation of the return on investment. The calculation of the value of return on stock prices can be predicted automatically at certain periods, both weekly and daily
Purpose: The problem faced is determining a good algorithm for making predictions due to fluctuating data on stock prices making it difficult to predict.
Methods: The stages carried out by the researcher include the data preprocessing stage and then proceed to the Exploratory Data Analysis (EDA) stage to get a pattern from the data, followed by the modeling stage on the data. This research was developed using the Python programming language where the models used to make predictions can be obtained in real-time.
Result: The results obtained in this study show that the Voting Regressor has the best model with an error rate of 0.032523 using Root Mean Square Error (RMSE). The results of this study can be further developed to automatically predict stock return values in the future.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2023-09-29</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.unnes.ac.id/sju/rji/article/view/68048</dc:identifier>
	<dc:identifier>10.15294/rji.v1i2.68048</dc:identifier>
	<dc:source xml:lang="en-US">Recursive Journal of Informatics; Vol 1 No 2 (2023): September 2023; 55-63</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/rji.v1i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/rji/article/view/68048/25388</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Recursive Journal of Informatics</dc:rights>
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