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NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 1 (2024) · pp. 0373–0382

DOI
10.30598/barekengvol18iss1pp0373-0382
ISSN
2615-3017, 1978-7227
Published
2024-03-01
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study aims to obtain the main component score of the ability to pay latent variable, determine the strongest indicators forming the ability to pay on a mixed scale based on defined indicators, and model the ability to pay on time mediated by fear of paying using path analysis. The data used in this study is secondary data from mortgage-paying customers with a sample size of 100. The method used is nonlinear principal component analysis with path analysis modeling. The results of this study indicate that the eleven variables formed by PC1 or X1 are able to store diversity or information by 32.50%, while 67.50% of diversity or other information is not stored (wasted). The credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgages has a significant effect on payments by mediating the fear of paying late with a coefficient of determination of 80.40%.

Collaboration network of the authors

40 authors · 93 co-authorship links · 20 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.

  • Universitas Halu Oleo
  • University of Brawijaya
  • Institut Agama Islam Negeri Kendari
  • Universitas Muhammadiyah Parepare
  • Jenderal Soedirman University
  • Universitas Islam Negeri Mahmud Yunus Batusangkar
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Adji Achmad Rinaldo FernandesUniversity of Brawijaya8
Jopang JopangUniversitas Halu Oleo3
Akhyar AbdullahUniversitas Halu Oleo3
Atiek IrianyUniversity of Brawijaya3
Ni Wayan Surya WardhaniUniversity of Brawijaya3
Makmur KambolongUniversitas Halu Oleo2
Mustakim MustakimUniversitas Muhammadiyah Parepare2
Erni QomariyahAffiliation unknown2
Achmad EfendiAffiliation unknown2
Suci AstutikUniversity of Brawijaya2
Agus Fachrur RozyUniversity of Brawijaya1
Agus SusiloAffiliation unknown1
Galuh Wilujeng PuspaAffiliation unknown1
Miftakhul CahyatiAffiliation unknown1
Dodyk PranowoAffiliation unknown1
Sumardona SumardonaInstitut Agama Islam Negeri Kendari1
Sarlinda SarlindaInstitut Agama Islam Negeri Kendari1
Epiyana EpiyanaInstitut Agama Islam Negeri Kendari1
Sarnianti SarniantiInstitut Agama Islam Negeri Kendari1
Kamrin KamrinUniversitas Halu Oleo1
Ruslan MajidUniversitas Halu Oleo1
Megasari SidulaAffiliation unknown1
Nooraini AsrudinAffiliation unknown1
Suriyani Bunga BataraAffiliation unknown1
Siti NurhayatiJenderal Soedirman University1
Nyoman Ningsih UtamiAffiliation unknown1
Candra Puspita NingtyasAffiliation unknown1
Aprilia WulandariAffiliation unknown1
Santi M Santi MAffiliation unknown1
isran museAffiliation unknown1
Aminuddin AminuddinInstitut Agama Islam Negeri Kendari1
Dewi DewiAffiliation unknown1
Muh AmirUniversitas Halu Oleo1
muslima kurniatiAffiliation unknown1
Abdul KadirUniversitas Halu Oleo1
Mirab MirabAffiliation unknown1
Irwan IrwanUniversitas Islam Negeri Mahmud Yunus Batusangkar1

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 5 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA</dc:title>
	<dc:creator>Hardianti, Rindu</dc:creator>
	<dc:creator>Solimun, Solimun</dc:creator>
	<dc:creator>Nurjannah, Nurjannah</dc:creator>
	<dc:description xml:lang="en-US">This study aims to obtain the main component score of the ability to pay latent variable, determine the strongest indicators forming the ability to pay on a mixed scale based on defined indicators, and model the ability to pay on time mediated by fear of paying using path analysis. The data used in this study is secondary data from mortgage-paying customers with a sample size of 100. The method used is nonlinear principal component analysis with path analysis modeling. The results of this study indicate that the eleven variables formed by PC1 or X1 are able to store diversity or information by 32.50%, while 67.50% of diversity or other information is not stored (wasted). The credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgages has a significant effect on payments by mediating the fear of paying late with a coefficient of determination of 80.40%.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-03-01</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/10410</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss1pp0373-0382</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application; 0373-0382</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss1year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10410/7594</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Rindu Hardianti, Solimun Solimun, Nurjannah Nurjannah</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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