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Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Articles

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

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  • Hanifa Sepriadi,
  • Atiek Iriany,
  • Solimun Solimun,
  • Adji Achmad Rinaldo Fernandes
Hanifa Sepriadi
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
Atiek Iriany
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
Solimun Solimun
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
Adji Achmad Rinaldo Fernandes
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
DOI: https://doi.org/10.30598/barekengvol19iss2pp1193-1202
Published April 1, 2025
Keywords
  • Cashless Society,
  • Cluster,
  • Heterogeneous,
  • Structural Equation Modeling
How to Cite
[1]
H. Sepriadi, A. Iriany, S. Solimun, and A. A. Rinaldo Fernandes, “DEVELOPMENT OF CLUSTER INTEGRATION WITH VARIAN BASED STRUCTURAL EQUATION MODELING TO MANAGE HETEROGENEOUS DATA”, BAREKENG: J. Math. & App., vol. 19, no. 2, pp. 1193-1202, Apr. 2025.

Copyright (c) 2025 Hanifa Sepriadi, Atiek Iriany, Solimun Solimun, Adji Achmad Rinaldo Fernandes

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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.

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