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COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 2 (2023) · pp. 0757–0766

DOI
10.30598/barekengvol17iss2pp0757-0766
ISSN
2615-3017, 1978-7227
Published
2023-06-11
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The performance of machine learning in analyzing time series data is being widely discussed. A new ensemble method Double Random Forest (DRF), which considers supervised learning currently developed. This method has been claimed to be able to improve the performance of Random Forest (RF) if the data is under-fitting. Another machine learning method, Long Short-Term Memory Networks (LSTMs) have capability to analyze nonlinear data. Since the study compare both methods has not been existed in literature, it is interesting to compare the performance of both methods using Indonesian data, especially economic indicator data which have been found to be under-fitting, non-underfitting, and nonlinear data. The indicators used in this study are Export, Import, Official Reserves Asset, and Exchange Rate data. The results showed that overall, the LSTMs method outperforms DRF method in analyzing the data.

Collaboration network of the authors

40 authors · 117 co-authorship links · 27 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.

  • IPB University
  • Bandung Islamic University
  • State University of Jakarta
  • Badan Pusat Statistik
  • Universitas Sulawesi Barat
  • Universitas Islam Negeri Alauddin Makassar
  • Universitas Prasetiya Mulya
  • Other organisations
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Bagus SartonoIPB University10
Yenni AngrainiIPB University9
Laily Nissa Atul MualifahIPB University7
Anang KurniaIPB University2
ERFIANI ERFIANIIPB University2
Utami Dyah SyafitriIPB University2
Muhammad Nur AidiIPB University2
Khalilah NurfadilahUniversitas Islam Negeri Alauddin Makassar2
Teguh PrasetyoIPB University1
Rizki Alifah PutriIPB University1
Dini RamadhaniIPB University1
Dwi Agustin Nuriani SirodjBandung Islamic University1
Vera Maya SantiState University of Jakarta1
INDAHWATI INDAHWATIIPB University1
Abdullah PannuIPB University1
Hari WijayantoIPB University1
Hafizah IlmaIPB University1
Ratna Nur Mustika SanusiIPB University1
Asep SaefuddinIPB University1
Satria June AdwendiBadan Pusat Statistik1
Kusman SadikIPB University1
Rahmatin Nur AmaliaIPB University1
Asrirawan AsrirawanUniversitas Sulawesi Barat1
Vivin Nur AzizaIPB University1
Fatma Hilali Moh’dIPB University1
Firda Aulia MaghfirohIPB University1
Muhammad Azis SuprayogiIPB University1
I Made SumertajayaIPB University1
Agus Sofian Eka HidayatPresident University1
Dian KusumaningrumUniversitas Prasetiya Mulya1
Arie PurwantoUniversitas Mercu Buana Yogyakarta1
Indra Rivaldi SiregarIPB University1
Adhiyatma NugrahaIPB University1
Riska YuliantiIPB University1
Nabila Tri AmandaIPB University1
Aleytha Ilahnugrah KurnadipareIPB University1
Sri AmaliyaIPB University1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA</dc:title>
	<dc:creator>Ratnasari, Andika Putri</dc:creator>
	<dc:creator>Susetyo, Budi</dc:creator>
	<dc:creator>Notodiputro, Khairil Anwar</dc:creator>
	<dc:description xml:lang="en-US">The performance of machine learning in analyzing time series data is being widely discussed. A new ensemble method Double Random Forest (DRF), which considers supervised learning currently developed. This method has been claimed to be able to improve the performance of Random Forest (RF) if the data is under-fitting. Another machine learning method, Long Short-Term Memory Networks (LSTMs) have capability to analyze nonlinear data. Since the study compare both methods has not been existed in literature, it is interesting to compare the performance of both methods using Indonesian data, especially economic indicator data which have been found to be under-fitting, non-underfitting, and nonlinear data. The indicators used in this study are Export, Import, Official Reserves Asset, and Exchange Rate data. The results showed that overall, the LSTMs method outperforms DRF method in analyzing the data. </dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-06-11</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7738</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss2pp0757-0766</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0757-0766</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss2year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7738/5937</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Andika Putri Ratnasari, Budi Susetyo, Khairil Anwar Notodiputro</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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