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Penerapan Extreme Learning Machine Dalam Meramalkan Harga Minyak Sawit Mentah

KUBIK: Jurnal Publikasi Ilmiah Matematika · Vol. 7 No. 2 (2023) · pp. 97–105

DOI
10.15575/kubik.v7i2.20460
ISSN
2686-0341, 2338-0896
Published
2023-06-05
Publisher
Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung
Language
ind

Abstract

The need for crude palm oil has increased due to the large demand for vegetable oils in various parts of the world. Beginning in March 2022, the price of crude palm oil set a record high which caused international cooking oil prices to soar, especially for Indonesia. This study aims to predict the price of crude palm oil with test parameters, namely hidden neurons and activation functions. The method used is Extreme Learning Machine (ELM). This method is a development of the artificial neural network (ANN) method which can overcome weaknesses in the learning speed process. There are several stages in this study: (1) pre-processing the data by normalizing the data and dividing the data using the time series split method, (2) analyzing the data using the ELM method by testing parameters, namely hidden neurons and activation functions, (3) analyzing the results of the best parameter trials, (4) calculating forecasting data using the best parameters that have been obtained, and (5) analyzing the forecasting results that have been obtained. This study uses daily data on the price of crude palm oil from April 1 2021 to April 14 2022 obtained from the Investing website. The results of the research that has been carried out obtained MAPE and RMSE values of 0.0173 and 0.0308 with the best parameters namely the number of hidden neurons of 5 and the binary sigmoid activation function. Based on the results obtained, it is hoped that it will make it easier for the government to determine the price of crude palm oil in the future.

Keywords

Collaboration network of the authors

40 authors · 66 co-authorship links · 1 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.

  • UIN Sunan Ampel Surabaya
  • Politeknik Elektronika Negeri Surabaya
  • Hasanuddin University
  • University of Indonesia
  • State University of Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Moh. HafiyusholehUIN Sunan Ampel Surabaya2
Halimatus Sa’dyahPoliteknik Elektronika Negeri Surabaya1
Nilna AlmumtazahUIN Sunan Ampel Surabaya1
Muhammad Sahrul KiromiUIN Sunan Ampel Surabaya1
Yuniar FaridaUIN Sunan Ampel Surabaya1
Ida PurwantiUIN Sunan Ampel Surabaya1
Wika Dianita UtamiUIN Sunan Ampel Surabaya1
Shafa Fitria Aqilah KhansaUIN Sunan Ampel Surabaya1
Fery FirmansyahAffiliation unknown1
Hani KhaulasariUIN Sunan Ampel Surabaya1
Yanuwar ReinaldiUIN Sunan Ampel Surabaya1
Tuti BahfiartiHasanuddin University1
Alem Febri SonniAffiliation unknown1
Dinar WahyuningrumAffiliation unknown1
Puji HardatiAffiliation unknown1
Denny SetiawanUniversity of Indonesia1
Della Raymena JovankaAffiliation unknown1
Tarissa Putri HanifahAffiliation unknown1
Floribertus Bujana Adi PradanaAffiliation unknown1
Muhammad Fathan Zahran DikaAffiliation unknown1
Afra YasminAffiliation unknown1
Asep KadarohmanAffiliation unknown1
Asyti FeblizaAffiliation unknown1
Norazilawati AbdullahAffiliation unknown1
Afakhrul Masub BakhtiarAffiliation unknown1
Sugiyo SugiyoAffiliation unknown1
Sinta SaraswatiAffiliation unknown1
Saiful RidloState University of Semarang1
DIAN NISA ISTOFAAffiliation unknown1
Dion GinantoAffiliation unknown1
T. Rykard Yudha PratamaAffiliation unknown1
Aqwa Naser DaulayAffiliation unknown1
Habib Lauda NararyaAffiliation unknown1
Eddy Pahar HarahapAffiliation unknown1
Muthmainnah IsmailAffiliation unknown1
Lina AmeliaAffiliation unknown1
Findya PuspitasariAffiliation 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.uinsgd.ac.id/index.php/kubik/oai, first harvested 7 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id-ID">Penerapan Extreme Learning Machine Dalam Meramalkan Harga Minyak Sawit Mentah</dc:title>
	<dc:creator>Aisyah, Siti</dc:creator>
	<dc:creator>Ulinnuha, Nurissaidah</dc:creator>
	<dc:creator>Hamid, Abdulloh</dc:creator>
	<dc:subject xml:lang="id-ID">Crude Palm Oil</dc:subject>
	<dc:subject xml:lang="id-ID">Extreme Learning Machine</dc:subject>
	<dc:subject xml:lang="id-ID">Time Series Split</dc:subject>
	<dc:description xml:lang="id-ID">The need for crude palm oil has increased due to the large demand for vegetable oils in various parts of the world. Beginning in March 2022, the price of crude palm oil set a record high which caused international cooking oil prices to soar, especially for Indonesia. This study aims to predict the price of crude palm oil with test parameters, namely hidden neurons and activation functions. The method used is Extreme Learning Machine (ELM). This method is a development of the artificial neural network (ANN) method which can overcome weaknesses in the learning speed process. There are several stages in this study: (1) pre-processing the data by normalizing the data and dividing the data using the time series split method, (2) analyzing the data using the ELM method by testing parameters, namely hidden neurons and activation functions, (3) analyzing the results of the best parameter trials, (4) calculating forecasting data using the best parameters that have been obtained, and (5) analyzing the forecasting results that have been obtained. This study uses daily data on the price of crude palm oil from April 1 2021 to April 14 2022 obtained from the Investing website. The results of the research that has been carried out obtained MAPE and RMSE values of 0.0173 and 0.0308 with the best parameters namely the number of hidden neurons of 5 and the binary sigmoid activation function. Based on the results obtained, it is hoped that it will make it easier for the government to determine the price of crude palm oil in the future.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung</dc:publisher>
	<dc:date>2023-06-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.uinsgd.ac.id/index.php/kubik/article/view/20460</dc:identifier>
	<dc:identifier>10.15575/kubik.v7i2.20460</dc:identifier>
	<dc:source xml:lang="en-US">KUBIK: Jurnal Publikasi Ilmiah Matematika; Vol. 7 No. 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika; 97-105</dc:source>
	<dc:source xml:lang="id-ID">KUBIK; Vol 7 No 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika; 97-105</dc:source>
	<dc:source>2686-0341</dc:source>
	<dc:source>2338-0896</dc:source>
	<dc:source>10.15575/kubik.v7i2</dc:source>
	<dc:language>ind</dc:language>
	<dc:relation>https://journal.uinsgd.ac.id/index.php/kubik/article/view/20460/9058</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Siti Aisyah, Nurissaidah Ulinnuha, Abdulloh Hamid</dc:rights>
</oai_dc:dc>