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Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 2 (2021) · pp. 141–152

DOI
10.22146/ijccs.63676
ISSN
2460-7258, 1978-1520
Published
2021-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Sunspot is an area on photosphere layer which is dark-colored. Sunspot is very important to be researched because sunspot is affected by sunspot numbers, which present the level of solar activity. This research was conducted to make prediction on sunspot numbers using Gated Recurrent Unit (GRU) algorithm. The work principle of GRU is similar to Long short-term Memory (LSTM) method: the information from the previous memory is processed through two gates, that is update gate and reset gate, then the output generated will be input for the next process. The purpose of predicting sunspot numbers was to find out the information of sunspot numbers in the future, so that if there is a significant increase in sunspot numbers, it can inform other physical consequences that may be caused. The data used was the data of monthly sunspot numbers obtained from SILSO website. The data division and parameters used were based on the results of the trials resulted in the smallest MAPE value. The smallest MAPE value obtained from the prediction was 7.171% with 70% training data, 30% testing data, 150 hidden layer, 32 batch size, 100 learning rate drop.

Keywords

Collaboration network of the authors

40 authors · 127 co-authorship links · 26 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
  • Badan Pusat Statistik
  • Meteorological, Climatological, And Geophysical Agency
  • Institut Teknologi Sepuluh Nopember
  • National Taiwan University of Science and Technology
  • Universiti Teknologi MARA
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Abdulloh HamidUIN Sunan Ampel Surabaya9
Hani KhaulasariUIN Sunan Ampel Surabaya9
Fajar SetiawanMeteorological, Climatological, And Geophysical Agency3
Putroue Keumala IntanUIN Sunan Ampel Surabaya3
Nurissaidah UlinnuhaUIN Sunan Ampel Surabaya3
Yuniar FaridaUIN Sunan Ampel Surabaya3
Felia Dria YuliawantiUIN Sunan Ampel Surabaya3
Ilmiatul MardiyahUIN Sunan Ampel Surabaya3
Dewi SulistiyawatiBadan Pusat Statistik3
Diah Devi PramestiUIN Sunan Ampel Surabaya2
Aris FananiUIN Sunan Ampel Surabaya2
Alvin Nuralif RamadantiUIN Sunan Ampel Surabaya2
Indra Ariyanto WijayaAffiliation unknown2
Victory Tyas Pambudi SwindiartoAffiliation unknown2
Achmad Fachril Yusuf AbabilUIN Sunan Ampel Surabaya2
Achmad Teguh WibowoUIN Sunan Ampel Surabaya1
Chalawatul AisUIN Sunan Ampel Surabaya1
Eka Alifia KusnantiInstitut Teknologi Sepuluh Nopember1
Mohammad HafiyusholehAffiliation unknown1
Ghaluh Indah Permata SariNational Taiwan University of Science and Technology1
Siti Nur FadilahUIN Sunan Ampel Surabaya1
Muhammad ImronUIN Sunan Ampel Surabaya1
Firman ArmunantoBadan Pusat Statistik1
Ahmad Naufal DzakyUIN Sunan Ampel Surabaya1
Lutfi HakimUIN Sunan Ampel Surabaya1
Risma Madurahma PutriUIN Sunan Ampel Surabaya1
Ahmad Hanif AsyharUIN Sunan Ampel Surabaya1
Firda Yunita SariUIN Sunan Ampel Surabaya1
Dina Zatusiva HaqUIN Sunan Ampel Surabaya1
Shafa Fitria Aqilah KhansaUIN Sunan Ampel Surabaya1
Shukor Sanim Mohd FauziUniversiti Teknologi MARA1
Maunah SetyawatiUIN Sunan Ampel Surabaya1
Jeneiro MaulanaAffiliation unknown1
Fery FirmansyahAffiliation unknown1
Yahya Vigo Tri SaputraUIN Sunan Ampel Surabaya1

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.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm</dc:title>
	<dc:creator>Arfianti, Unix Izyah</dc:creator>
	<dc:creator>Novitasari, Dian Candra Rini</dc:creator>
	<dc:creator>Widodo, Nanang</dc:creator>
	<dc:creator>Hafiyusholeh, Moh.</dc:creator>
	<dc:creator>Utami, Wika Dianita</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">prediction; sunspot numbers; time series; GRU; LSTM</dc:subject>
	<dc:description xml:lang="en-US">Sunspot is an area on photosphere layer which is dark-colored. Sunspot is very important to be researched because sunspot is affected by sunspot numbers, which present the level of solar activity. This research was conducted to make prediction on sunspot numbers using Gated Recurrent Unit (GRU) algorithm. The work principle of GRU is similar to Long short-term Memory (LSTM) method: the information from the previous memory is processed through two gates, that is update gate and reset gate, then the output generated will be input for the next process. The purpose of predicting sunspot numbers was to find out the information of sunspot numbers in the future, so that if there is a significant increase in sunspot numbers, it can inform other physical consequences that may be caused. The data used was the data of monthly sunspot numbers obtained from SILSO website. The data division and parameters used were based on the results of the trials resulted in the smallest MAPE value. The smallest MAPE value obtained from the prediction was 7.171% with 70% training data, 30% testing data, 150 hidden layer, 32 batch size, 100 learning rate drop. </dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/63676</dc:identifier>
	<dc:identifier>10.22146/ijccs.63676</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 2 (2021): April; 141-152</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/63676/31154</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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