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COMPARING GAUSSIAN KERNEL AND QUADRATIC SPLINE OF NONPARAMETRIC REGRESSION IN MODELING INFECTIOUS DISEASES

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 4 (2023) · pp. 2135–2146

DOI
10.30598/barekengvol17iss4pp2135-2146
ISSN
2615-3017, 1978-7227
Published
2023-12-19
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The regression curve for nonparametric regression is assumed to belong to some infinite-dimensional collection of functions, which allows great flexibility in the form of the curve. This research intends to compare the Gaussian Kernel and Quadratic Spline regressions in four infectious diseases in Indonesia by 2021. The data used is secondary data from the Central Bureau of Statistics and the Ministry of Health, Indonesia, and the sample consists of four infectious diseases in Indonesia by 2021 (Tuberculosis, Diarrhoeal, Pneumonia, and COVID-19). Considering the correlation value, it was found that the independent and dependent variables of the four infectious diseases are all highly correlated (r values are more than 0.7). Furthermore, the scatter plots for four infectious diseases do not follow a particular pattern; due to this, parametric regression cannot be used to analyze the data. Therefore, nonparametric regression was applied in this research. According to the analysis, the Gaussian Kernel is the best regression technique for modeling four infectious diseases in Indonesia by 2021, which its R2 values are 99.85% (Tuberculosis), 100% (Diarrhoeal), 99.91% (Pneumonia), and 99.99% (COVID-19).

Collaboration network of the authors

40 authors · 129 co-authorship links · 23 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.

  • State University of Gorontalo
  • Universitas Islam Negeri Imam Bonjol Padang
  • Universitas Halu Oleo
  • Sultan Ageng Tirtayasa University
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Universitas Pamulang
  • Universitas Dharma Andalas
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Resmawan ResmawanState University of Gorontalo7
Agusyarif Rezka NuhaState University of Gorontalo5
Nurwan NurwanState University of Gorontalo4
Nisky Imansyah YahyaState University of Gorontalo3
Franky Alfrits OrohState University of Gorontalo2
Sri Lestari MahmudState University of Gorontalo2
Asriadi AsriadiState University of Gorontalo2
Annisa Maharani BrahimState University of Gorontalo2
Dewi Rahmawaty IsaState University of Gorontalo2
Hasan S. PanigoroState University of Gorontalo2
Zulkifli AlamtahaAffiliation unknown1
Mohamad Rivaldi KoniAffiliation unknown1
Isran R OliiState University of Gorontalo1
Rahmayunda UsaliState University of Gorontalo1
Nur Safitri AbdulState University of Gorontalo1
Gusti Arviana RahmanUniversitas Halu Oleo1
Novianita AchmadState University of Gorontalo1
Syarif AbdullahSultan Ageng Tirtayasa University1
Sri Istiyarti Uswatun ChasanahSunan Kalijaga State Islamic University Yogyakarta1
Nina ValentikaUniversitas Pamulang1
La Ode SabranUniversitas Islam Negeri Imam Bonjol Padang1
Ilham Dangu RianjayaUniversitas Dharma Andalas1
Lilis Harianti HasibuanUniversitas Islam Negeri Imam Bonjol Padang1
Nazrilla Hasan LaitaState University of Gorontalo1
Irsan K. HasanState University of Gorontalo1
Sri Endang SalehState University of Gorontalo1
Debyyansa PakayaState University of Gorontalo1
Djihad WungguliState University of Gorontalo1
Salmun K. NasibState University of Gorontalo1
Jefri N. IsaState University of Gorontalo1
Stella JunusState University of Gorontalo1
Muftih Alwi AliuState University of Gorontalo1
Fahrezal ZubediState University of Gorontalo1

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">COMPARING GAUSSIAN KERNEL AND QUADRATIC SPLINE OF NONPARAMETRIC REGRESSION IN MODELING INFECTIOUS DISEASES</dc:title>
	<dc:creator>Adityaningrum, Amanda</dc:creator>
	<dc:creator>Ladjali, Sri Indriani</dc:creator>
	<dc:creator>Djakaria, Ismail</dc:creator>
	<dc:creator>Yahya, Lailany</dc:creator>
	<dc:creator>Payu, Muhammad Rezky Friesta</dc:creator>
	<dc:creator>Nashar, La Ode</dc:creator>
	<dc:creator>Jusuf, Herlina</dc:creator>
	<dc:description xml:lang="en-US">The regression curve for nonparametric regression is assumed to belong to some infinite-dimensional collection of functions, which allows great flexibility in the form of the curve. This research intends to compare the Gaussian Kernel and Quadratic Spline regressions in four infectious diseases in Indonesia by 2021. The data used is secondary data from the Central Bureau of Statistics and the Ministry of Health, Indonesia, and the sample consists of four infectious diseases in Indonesia by 2021 (Tuberculosis, Diarrhoeal, Pneumonia, and COVID-19). Considering the correlation value, it was found that the independent and dependent variables of the four infectious diseases are all highly correlated (r values are more than 0.7). Furthermore, the scatter plots for four infectious diseases do not follow a particular pattern; due to this, parametric regression cannot be used to analyze the data. Therefore, nonparametric regression was applied in this research . According to the analysis, the Gaussian Kernel is the best regression technique for modeling four infectious diseases in Indonesia by 2021, which its R2 values are 99.85% (Tuberculosis), 100% (Diarrhoeal), 99.91% (Pneumonia), and 99.99% (COVID-19).</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-12-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9721</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss4pp2135-2146</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications; 2135-2146</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss4year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9721/7198</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Amanda Adityaningrum, Sri Indriani Ladjali, Ismail Djakaria, Lailany Yahya, Muhammad Rezky Friesta Payu, La Ode Nashar, Herlina Jusuf</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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