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TUBERCULOSIS CASE MODEL USING GCV AND UBR KNOT SELECTION METHODS IN TRUNCATED SPLINE NONPARAMETRIC REGRESSION

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 3 (2023) · pp. 1565–1574

DOI
10.30598/barekengvol17iss3pp1565-1574
ISSN
2615-3017, 1978-7227
Published
2023-09-30
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The nonparametric regression approach is used when the shape of the regression curve is not known. The advantage of nonparametric regression is that it has a high degree of flexibility. The truncated spline is a method in the nonparametric regression approach, which can overcome changing data patterns at certain sub-intervals with the help of knot points. The purpose of this research is to obtain the best truncated spline nonparametric regression model estimates based on the GCV and UBR knot point selection methodsThe data used in this study came from the publications of the Indonesian Ministry of Health and BPS Indonesia. The response variable used is the percentage of successful treatment of tuberculosis patients in Indonesia with predictor variables namely the percentage of people who smoke over the age of 15 years, the percentage of households that have access to proper sanitation, the percentage of poor people, the percentage of food processing establishments that meet the standard requirements, national health insurance membership coverage and percentage of accredited hospitals. The results showed that the best model came from the GCV method using three knots. This model produces an MSE value of 3.65 with value of 97.04. The value indicates that the predictor variable used in this study affects the response variable by 97.04% while the other 2.96% is influenced by other variables that are not included in this study.

Collaboration network of the authors

35 authors · 73 co-authorship links · 3 across different organisations

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

  • Mulawarman University
  • State University of Makassar
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Andrea Tri Rian DaniMulawarman University4
Surya PranggaMulawarman University3
Rizki Dwi FadlirhohimMulawarman University2
Zakiyah Mar’ahState University of Makassar2
Meirinda FauziyahMulawarman University2
Wasono WasonoMulawarman University2
Darnah DarnahAffiliation unknown1
Eka Nur AmaliahMulawarman University1
Ahmad Rizky KesumaMulawarman University1
Farikah Ayu RinandaMulawarman University1
Ilyas AstafiraMulawarman University1
Nur AfrianiMulawarman University1
Tri Septi Ayu LestariMulawarman University1
Tirta PurnaragaMulawarman University1
Rifka Nurfaiza ZarkasiMulawarman University1
Aprianti Boma PadatuanMulawarman University1
Muhammad Danil PasarellaMulawarman University1
Fidia Deny Tisna AmijayaMulawarman University1
Memi Nor HayatiMulawarman University1
Yunda Sasha ParadillaMulawarman University1
Mohammad Nurul HudaMulawarman University1
Ar Ruum Mia SariMulawarman University1
Frans Karta Sayoga SitohangMulawarman University1
Siti MahmudaMulawarman University1
Anggi Putri AnisarMulawarman University1
Marisa Nanda SaputriMulawarman University1
Itsar MangngiriMulawarman University1
Rahmania RahmaniaMulawarman University1
Asnita AsnitaMulawarman University1
Indria SaskaMulawarman University1
Suyitno SuyitnoMulawarman University1
Ghina FadhillaMulawarman University1

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">TUBERCULOSIS CASE MODEL USING GCV AND UBR KNOT SELECTION METHODS IN TRUNCATED SPLINE  NONPARAMETRIC REGRESSION</dc:title>
	<dc:creator>Anggraeni, Sitti</dc:creator>
	<dc:creator>Sifriyani, Sifriyani</dc:creator>
	<dc:creator>A'yun, Qonita Qurrota</dc:creator>
	<dc:description xml:lang="en-US">The nonparametric regression approach is used when the shape of the regression curve is not known. The advantage of nonparametric regression is that it has a high degree of flexibility. The truncated spline is a method in the nonparametric regression approach, which can overcome changing data patterns at certain sub-intervals with the help of knot points. The purpose of this research is to obtain the best truncated spline nonparametric regression model estimates based on the GCV and UBR knot point selection methodsThe data used in this study came from the publications of the Indonesian Ministry of Health and BPS Indonesia. The response variable used is the percentage of successful treatment of tuberculosis patients in Indonesia with predictor variables namely the percentage of people who smoke over the age of 15 years, the percentage of households that have access to proper sanitation, the percentage of poor people, the percentage of food processing establishments that meet the standard requirements , national health insurance membership coverage and percentage of accredited hospitals. The results showed that the best model came from the GCV method using three knots. This model produces an MSE value of 3.65 with &amp;nbsp;value of 97.04. The value indicates that the predictor variable used in this study affects the response variable by 97.04% while the other 2.96% is influenced by other variables that are not included in this study. </dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-09-30</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/8481</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss3pp1565-1574</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications; 1565-1574</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss3year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8481/6570</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Sitti Anggraeni, Sifriyani Sifriyani, Qonita Qurrota A'yun</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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