Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

dewi, Ratih kartika

doi:10.21609/jiki.v12i2.729

Jurnal SINTA 2

Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

dewi, Ratih kartika^{University of Brawijaya}

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 12 No. 2 (2019) · pp. 85–90

DOI: 10.21609/jiki.v12i2.729
ISSN: 2502-9274, 2088-7051
Published: 2019-07-08
Publisher: Faculty of Computer Science - Universitas Indonesia
Language: eng

Abstract

This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendation

Keywords

Collaboration network of the authors

6 authors · 8 co-authorship links · 3 across different organisations

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University of Brawijaya
Institut Teknologi Sepuluh Nopember

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Same organisation
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Co-author	Organisation	Joint articles
Chastine Fatichah	Institut Teknologi Sepuluh Nopember	1
Yuita Arum Sari	University of Brawijaya	1
Raden Venantius Hari Ginardi	Institut Teknologi Sepuluh Nopember	1
Herman Tolle	University of Brawijaya	1
Annisah Amalia	University of Brawijaya	1

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">Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System</dc:title>
	<dc:creator>dewi, Ratih kartika</dc:creator>
	<dc:subject xml:lang="en-US">AHP-TOPSIS</dc:subject>
	<dc:subject xml:lang="en-US">Recommendation</dc:subject>
	<dc:subject xml:lang="en-US">Culinary</dc:subject>
	<dc:subject xml:lang="en-US">Group DSS</dc:subject>
	<dc:subject xml:lang="en-US">Decision Support System</dc:subject>
	<dc:description xml:lang="en-US">This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendation</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2019-07-08</dc:date>
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	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 12 No. 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 85-90</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v12i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/729/415</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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