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Recommendation method for selecting the rice seeds based on group decision support system

IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · pp. 2656–2665

DOI
10.11591/ijai.v13.i3.pp2656-2665
ISSN
2252-8938, 2089-4872
Published
2024-09-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In this paper, we provide group recommendations based on each decision makers (DMs) in choosing the best type of rice for replanting. This group decision support system (GDSS) aims to guide stakeholders who have a role in selecting rice types. In this method, we propose using technique for order preference by similarity to ideal solution (TOPSIS) to rank each DM, Borda to rank in groups, and then test it using Spearman's rank correlation to measure the relationship between system results and the method applied. The results of this study show that DM1 ranks highest in selecting Gelagai rice seeds with a preference of 0.7786. Then DM2 ranked highest with Ekor Payau rice seeds in preference 0.6529. Meanwhile, DM3 ranked highest in Gelagai rice seeds with a selection of 0.7728. The final group voting system uses Borda, where Gelagai rice seeds occupy the highest rank with the most accumulated votes from each DMs. The best option or the highest rating based on the assessment of the three DMs, DM1 as a farmer is the first rank A10 Gelagai with a score of Borda 26 in the decision group selection of superior rice seeds.

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.

  • Mulawarman University
  • STMIK Banjarbaru
  • Musamus Merauke University
  • Universitas Respati Yogyakarta
  • STMIK El Rahma Yogyakarta
  • Hasanuddin University
  • Universitas Islam Negeri Antasari Banjarmasin
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anindita SeptiariniMulawarman University7
Budi RahmaniSTMIK Banjarbaru5
Heru IsmantoMusamus Merauke University5
Novianti PuspitasariMulawarman University5
Agus Qomaruddin MunirUniversitas Respati Yogyakarta4
Andri SyafriantoSTMIK El Rahma Yogyakarta4
Ali Basrah PulunganAffiliation unknown3
Ummul HairahMulawarman University3
Dyna Marisa KhairinaMulawarman University2
Tarina DashelaAffiliation unknown2
Bistari BistariAffiliation unknown2
Rustam RustamHasanuddin University2
Veven VevenAffiliation unknown2
Aghnia NurhidayahAffiliation unknown2
Anandiaz Agung PradanaAffiliation unknown2
Muhammad Rifan FathoniAffiliation unknown2
Hugo ApriliantoSTMIK Banjarbaru1
Andi TejawatiMulawarman University1
Surya Eka PriyatnaUniversitas Islam Negeri Antasari Banjarmasin1
Wulan SariAffiliation unknown1
Ari SanjayaAffiliation unknown1
Rudianto RudiantoAffiliation unknown1
Rudy Agung NugrohoAffiliation unknown1
Retno AryaniAffiliation unknown1
Hetty ManurungMulawarman University1
Muhammad Fauzi ArifAffiliation unknown1
Iyut Jaya ToimsarAffiliation unknown1
Yuriadi IlmiAffiliation unknown1
Sadik IkhsanAffiliation unknown1
Silvia MeilinaAffiliation unknown1
Melda YenniAffiliation unknown1
Khairinal KhairinalAffiliation unknown1
Emosda EmosdaAffiliation 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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Recommendation method for selecting the rice seeds based on group decision support system</dc:title>
	<dc:creator>Hamdani, Hamdani</dc:creator>
	<dc:creator>Wati, Masna</dc:creator>
	<dc:creator>Suprihanto, Didit</dc:creator>
	<dc:creator>Salsabila, Nur Maya</dc:creator>
	<dc:creator>Septiarini, Anindita</dc:creator>
	<dc:creator>Nurmadewi, Dita</dc:creator>
	<dc:creator>Mawardi, Viny Christanti</dc:creator>
	<dc:subject xml:lang="en-US">GDSS</dc:subject>
	<dc:subject xml:lang="en-US">Borda; Group decision; Recommendation; Rice seeds; TOPSIS</dc:subject>
	<dc:description xml:lang="en-US">In this paper, we provide group recommendations based on each decision makers (DMs) in choosing the best type of rice for replanting. This group decision support system (GDSS) aims to guide stakeholders who have a role in selecting rice types. In this method, we propose using technique for order preference by similarity to ideal solution (TOPSIS) to rank each DM, Borda to rank in groups, and then test it using Spearman's rank correlation to measure the relationship between system results and the method applied. The results of this study show that DM1 ranks highest in selecting Gelagai rice seeds with a preference of 0.7786. Then DM2 ranked highest with Ekor Payau rice seeds in preference 0.6529. Meanwhile, DM3 ranked highest in Gelagai rice seeds with a selection of 0.7728. The final group voting system uses Borda, where Gelagai rice seeds occupy the highest rank with the most accumulated votes from each DMs. The best option or the highest rating based on the assessment of the three DMs, DM1 as a farmer is the first rank A10 Gelagai with a score of Borda 26 in the decision group selection of superior rice seeds.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universitas Mulawarman</dc:contributor>
	<dc:date>2024-09-01</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"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23897</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i3.pp2656-2665</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 2656-2665</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23897/14074</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>