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Rule-Based Promotional Segmentation In Higher Education Using Apriori Algoritm

SemanTIK: Teknik Informasi · Vol. 12 No. 1 (2026) · pp. 167–180

DOI
10.55679/semantik.v12i1.238
ISSN
2502-8928, 2460-1446
Published
2026-06-25
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study aims to identify co-occurrence patterns among higher education promotional media and translate them into rule-based promotional recommendations using association rule mining. An exploratory quantitative design was applied to 118 anonymized promotional engagement transactions from Institut Teknologi dan Bisnis Muhammadiyah Wakatobi. Each transaction consisted of three promotional items from ten media and content categories. Data were processed through item-label standardization, transaction transformation, binary one-hot encoding, frequent itemset generation, and association rule extraction using support, confidence, and lift metrics in Google Colaboratory. The results show that Facebook information was the most frequent promotional item (support = 0.4322), followed by registration form (0.3814), study program video (0.3559), and Instagram campaign (0.3220). The strongest association rule was Social media → Facebook information, with confidence of 0.4839 and lift of 1.1195, indicating positive association beyond random co-occurrence. The findings were translated into a six-segment rule-based promotional framework. This study contributes to educational data mining by providing an interpretable and replicable approach for supporting data-driven promotional planning in higher education

Keywords

Collaboration network of the authors

9 authors · 15 co-authorship links

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  • Institut Teknologi dan Bisnis Kalbe
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Amin IrmawanAffiliation unknown1
JumuiAffiliation unknown1
RuliatiAffiliation unknown1
KariyaminInstitut Teknologi dan Bisnis Kalbe1
Egi Dio Bagus SudewoAffiliation unknown1
La Ode AlyandiInstitut Teknologi dan Bisnis Kalbe1

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://semantik.uho.ac.id/index.php/journal/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">Rule-Based Promotional Segmentation In Higher Education Using Apriori Algoritm</dc:title>
	<dc:creator>Hamka, Sry Faslia</dc:creator>
	<dc:creator>Irmawan, Yamin</dc:creator>
	<dc:creator>Sariani</dc:creator>
	<dc:subject xml:lang="en-US">Apriori algorithm</dc:subject>
	<dc:subject xml:lang="en-US">association rule mining</dc:subject>
	<dc:subject xml:lang="en-US">educational data mining</dc:subject>
	<dc:subject xml:lang="en-US">promotional segmentation</dc:subject>
	<dc:subject xml:lang="en-US">higher education promotion</dc:subject>
	<dc:description xml:lang="en-US"> This study aims to identify co-occurrence patterns among higher education promotional media and translate them into rule-based promotional recommendations using association rule mining. An exploratory quantitative design was applied to 118 anonymized promotional engagement transactions from Institut Teknologi dan Bisnis Muhammadiyah Wakatobi. Each transaction consisted of three promotional items from ten media and content categories. Data were processed through item-label standardization, transaction transformation, binary one-hot encoding, frequent itemset generation, and association rule extraction using support, confidence, and lift metrics in Google Colaboratory. The results show that Facebook information was the most frequent promotional item (support = 0.4322), followed by registration form (0.3814), study program video (0.3559), and Instagram campaign (0.3220). The strongest association rule was Social media → Facebook information, with confidence of 0.4839 and lift of 1.1195, indicating positive association beyond random co-occurrence. The findings were translated into a six-segment rule-based promotional framework. This study contributes to educational data mining by providing an interpretable and replicable approach for supporting data-driven promotional planning in higher education</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:date>2026-06-25</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://semantik.uho.ac.id/index.php/journal/article/view/238</dc:identifier>
	<dc:identifier>10.55679/semantik.v12i1.238</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol. 12 No. 1 (2026): Vol. 12 No. 1 (2026): SemanTIK Teknik Informasi; 167-180</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://semantik.uho.ac.id/index.php/journal/article/view/238/71</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Sry Faslia Hamka, Yamin Irmawan, Sariani</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>