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Open access Jurnal SINTA 2

Determination of Temporal Association Rules Pattern Using Apriori Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 2 (2020) · pp. 159–168

DOI
10.22146/ijccs.51747
ISSN
2460-7258, 1978-1520
Published
2020-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.

Keywords

Collaboration network of the authors

19 authors · 24 co-authorship links · 6 across different organisations

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

  • Universitas Gadjah Mada
  • Universitas Stikubank
  • Universitas Trunodjoyo Madura
  • Sekolah Tinggi Multi Media "MMTC" Yogyakarta
  • Universitas Teknokrat Indonesia
  • Institut Teknologi Telkom Purwokerto
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Retantyo WardoyoUniversitas Gadjah Mada4
Edi WinarkoUniversitas Gadjah Mada4
Edhy SutantaUniversitas Gadjah Mada4
Wiwien HadikurniawatiUniversitas Stikubank2
Oesman Hendra KelanaUniversitas Gadjah Mada1
Anie Rose IrawatiUniversitas Gadjah Mada1
Wikranta ArsaUniversitas Gadjah Mada1
Firmansyah AdiputraUniversitas Trunodjoyo Madura1
Erma SusantiUniversitas Gadjah Mada1
Teguh SusyantoAffiliation unknown1
David KristiadiSekolah Tinggi Multi Media "MMTC" Yogyakarta1
Sunu Pinasthika FajarUniversitas Gadjah Mada1
Styawati StyawatiUniversitas Teknokrat Indonesia1
Sigit PriyantaUniversitas Gadjah Mada1
Amalia Nur AnggraeniUniversitas Gadjah Mada1
Tenia WahyuningrumInstitut Teknologi Telkom Purwokerto1
Ridowati GunawanUniversitas Gadjah Mada1

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 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Determination of Temporal Association Rules Pattern Using Apriori Algorithm</dc:title>
	<dc:creator>Bilqisth, Shona Chayy</dc:creator>
	<dc:creator>Mustofa, Khabib</dc:creator>
	<dc:subject xml:lang="en-US">Coputer Science</dc:subject>
	<dc:subject xml:lang="en-US">sales; temporal association rules; apriori; data mining</dc:subject>
	<dc:description xml:lang="en-US">A supermarket must have  good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules  functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.</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>2020-04-30</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/51747</dc:identifier>
	<dc:identifier>10.22146/ijccs.51747</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 2 (2020): April; 159-168</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/51747/27883</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 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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