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Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 9 No. 1 (2016) · pp. 1–8

DOI
10.21609/jiki.v9i1.287
ISSN
2502-9274, 2088-7051
Published
2016-02-15
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

The largest region that produces oil palm in Indonesia has an important role in improving the welfare of society and economy. Oil palm has increased significantly in Riau Province in every period, to determine the production development for the next few years with the functions and benefits of oil palm carried prediction production results that were seen from time series data last 8 years (2005-2013). In its prediction implementation, it was done by comparing the performance of Support Vector Regression (SVR) method and Artificial Neural Network (ANN). From the experiment, SVR produced the best model compared with ANN. It is indicated by the correlation coefficient of 95% and 6% for MSE in the kernel Radial Basis Function (RBF), whereas ANN produced only 74% for R2 and 9% for MSE on the 8th experiment with hiden neuron 20 and learning rate 0,1. SVR model generates predictions for next 3 years which increased between 3% - 6% from actual data and RBF model predictions.

Keywords

Collaboration network of the authors

24 authors · 40 co-authorship links · 7 across different organisations

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

  • IPB University
  • Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Universitas Kristen Wira Wacana Sumba
  • Pakuan University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yani NurhadryaniIPB University2
IrmansyahAffiliation unknown1
Dhany Nugraha RamdhanyAffiliation unknown1
Aziz KustiyoAffiliation unknown1
Ekowati HandharyaniIPB University1
Arini Aha PekuwaliUniversitas Kristen Wira Wacana Sumba1
Wisnu Ananta KusumaIPB University1
Risma RustiyanUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Siti RaehanIPB University1
Medria Kusuma Dewi HardhienataAffiliation unknown1
Kudang Boro SeminarIPB University1
Melva Linda AritonangIPB University1
Nugraha Edhi SuyatmaIPB University1
Sri NurdiatiIPB University1
Fajar Delli WihartikoPakuan University1
Edi SantosaIPB University1
I Nyoman Rai Widartha KesumaIPB University1
Addini YusmarIPB University1
Imas Sukaesih SitanggangIPB University1
Kahfi Heryandi SuradiradjaIPB University1
Luki AbdullahIPB 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://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production</dc:title>
	<dc:creator>Mustakim, Mustakim</dc:creator>
	<dc:creator>Buono, Agus</dc:creator>
	<dc:creator>Hermadi, Irman</dc:creator>
	<dc:subject xml:lang="en-US">Artificial Neural Network (ANN)</dc:subject>
	<dc:subject xml:lang="en-US">Palm Oil</dc:subject>
	<dc:subject xml:lang="en-US">Prediction</dc:subject>
	<dc:subject xml:lang="en-US">Radial Basis Function (RBF)</dc:subject>
	<dc:subject xml:lang="en-US">Support Vector Regression (SVR)</dc:subject>
	<dc:description xml:lang="en-US">The largest region that produces oil palm in Indonesia has an important role in improving the welfare of society and economy. Oil palm has increased significantly in Riau Province in every period, to determine the production development for the next few years with the functions and benefits of oil palm carried prediction production results that were seen from time series data last 8 years (2005-2013). In its prediction implementation, it was done by comparing the performance of Support Vector Regression (SVR) method and Artificial Neural Network (ANN). From the experiment, SVR produced the best model compared with ANN. It is indicated by the correlation coefficient of 95% and 6% for MSE in the kernel Radial Basis Function (RBF), whereas ANN produced only 74% for R2 and 9% for MSE on the 8th experiment with hiden neuron 20 and learning rate 0,1. SVR model generates predictions for next 3 years which increased between 3% - 6% from actual data and RBF model predictions.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2016-02-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/287</dc:identifier>
	<dc:identifier>10.21609/jiki.v9i1.287</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 9 No. 1 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 1-8</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v9i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/287/232</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2016 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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