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DEVELOPMENT OF CLUSTER INTEGRATION WITH VARIAN BASED STRUCTURAL EQUATION MODELING TO MANAGE HETEROGENEOUS DATA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 19 No. 2 (2025) · pp. 1193–1202

DOI
10.30598/barekengvol19iss2pp1193-1202
ISSN
2615-3017, 1978-7227
Published
2025-04-01
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In the application of SEM to multivariate data, the individuals collected not only come from the same population but also from several groups (clusters). This data is heterogeneous. When SEM is applied to heterogeneous data, there will be a risk of bias in estimating equations in the measurement and structural models because there are differences between groups in the data. The purpose of this study is to overcome heterogeneous data in modeling cashless behavior with cluster using a dummy approach. This study used primary data from a survey in Bekasi City using a questionnaire with 100 respondents. Based on the study's results, it is known that using clustering in SEM can overcome heterogeneous data, which is indicated by the high coefficient of determination of 96.12%. Banks can use the results of this study to design products and services that are more in line with customer needs and preferences while encouraging financial inclusion in the digital era.

Collaboration network of the authors

40 authors · 129 co-authorship links · 9 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.

  • University of Brawijaya
  • Universitas Negeri Surabaya
  • Universiti Malaysia Sarawak
  • Université d’Antsiranana
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ni Wayan Surya WardhaniUniversity of Brawijaya5
Nurjannah NurjannahUniversity of Brawijaya4
SolimunAffiliation unknown4
Dea Saraswati PramaningrumUniversity of Brawijaya4
Suci AstutikUniversity of Brawijaya4
Wigbertus NgabuUniversity of Brawijaya3
Fachira Haneinanda JuniantoUniversity of Brawijaya3
Hanifa SepriadiAffiliation unknown3
Adji Achmad Rinaldo FernandesAffiliation unknown3
Cindy Rudiat SekarsariUniversity of Brawijaya3
Devi Veda AmandaUniversity of Brawijaya3
Ulfah MaisarohUniversity of Brawijaya2
Luthfia Hanun Yuli AriniUniversity of Brawijaya2
Achmad EfendiUniversity of Brawijaya2
Yossy Maynaldi PratamaUniversity of Brawijaya2
Rosita HamdanUniversiti Malaysia Sarawak2
Rahma FitrianiUniversity of Brawijaya2
Usriatur RohmaUniversity of Brawijaya2
Achmad EfendiUniversity of Brawijaya2
Shalsa Amalia YuliantoUniversity of Brawijaya2
Endang ArisoesilaningsihAffiliation unknown2
Agus Fachrur RozyUniversity of Brawijaya1
Adji FernandesAffiliation unknown1
Ani Budi AstutiUniversity of Brawijaya1
Eni SumarminingsihUniversity of Brawijaya1
Nabila Ayunda SoviaAffiliation unknown1
Danang AriantoUniversity of Brawijaya1
Arditama PutraUniversity of Brawijaya1
M. Fikar PapaliaUniversity of Brawijaya1
Rindu HardiantiUniversity of Brawijaya1
Danang AriyantoUniversitas Negeri Surabaya1
Petrus Kanisius OlaUniversity of Brawijaya1
Armando Jacquis Federal ZamelinaUniversity of Brawijaya1
Lucius RamifidisoaUniversité d’Antsiranana1
Hafizh Syihabuddin Al JauharUniversity of Brawijaya1
Maulidya MaghfiroUniversity of Brawijaya1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">DEVELOPMENT OF CLUSTER INTEGRATION WITH VARIAN BASED STRUCTURAL EQUATION MODELING TO MANAGE HETEROGENEOUS DATA</dc:title>
	<dc:creator>Sepriadi, Hanifa</dc:creator>
	<dc:creator>Iriany, Atiek</dc:creator>
	<dc:creator>Solimun, Solimun</dc:creator>
	<dc:creator>Rinaldo Fernandes, Adji Achmad</dc:creator>
	<dc:description xml:lang="en-US">In the application of SEM to multivariate data, the individuals collected not only come from the same population but also from several groups (clusters). This data is heterogeneous. When SEM is applied to heterogeneous data, there will be a risk of bias in estimating equations in the measurement and structural models because there are differences between groups in the data. The purpose of this study is to overcome heterogeneous data in modeling cashless behavior with cluster using a dummy approach. This study used primary data from a survey in Bekasi City using a questionnaire with 100 respondents. Based on the study's results, it is known that using clustering in SEM can overcome heterogeneous data, which is indicated by the high coefficient of determination of 96.12%. Banks can use the results of this study to design products and services that are more in line with customer needs and preferences while encouraging financial inclusion in the digital era.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2025-04-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15464</dc:identifier>
	<dc:identifier>10.30598/barekengvol19iss2pp1193-1202</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application; 1193-1202</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol19iss2year2025</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15464/9967</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Hanifa Sepriadi, Atiek Iriany, Solimun Solimun, Adji Achmad Rinaldo Fernandes</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>