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Jurnal SINTA 3

ANALISIS DISKRIMINAN, REGRESI LOGISTIK, NEURAL NETWORK DAN MARS PADA PENGKLASIFIKASIAN DATA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 1 No. 2 (2007) · pp. 8–13

DOI
10.30598/barekengvol1iss2pp8-13
ISSN
2615-3017, 1978-7227
Published
2007-12-01
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

The purpose of this research is to apply and compare the discriminant analysis, logistic regression, Neural Network (NN) and Multivariate Adaptive Regression Spline (MARS) at HBAT and IRIS data. Next will be the classification of fourth methods using the SPSS statistical software, MINITAB, MARS, and R. The results showed that the data HBAT predictor variables affected to the response variable which is the quality of the product (X6), Complaint resolution (X9) and Salesforce image (X12), whereas all predictor variables on the IRIS data affect the response variable. A more precise method used in HBAT data classification is NN and discriminant analysis because the value of the resulting classification accuracy is greater, especially for testing. While a more precise method used in the IRIS data classification is discriminant analysis because the value of the resulting classification accuracy is greater

Collaboration network of the authors

8 authors · 11 co-authorship links

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Co-authorOrganisationJoint articles
Lexy J. SinayAffiliation unknown2
RUDY W. MatakupanAffiliation unknown1
Freya N. WattimenaAffiliation unknown1
Yopi A. LesnussaAffiliation unknown1
Salmon N. AuleleAffiliation unknown1
Riana WattimenaAffiliation unknown1
D. AnakottaAffiliation unknown1

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">ANALISIS DISKRIMINAN, REGRESI LOGISTIK, NEURAL NETWORK DAN MARS PADA PENGKLASIFIKASIAN DATA</dc:title>
	<dc:creator>Pentury, Thomas</dc:creator>
	<dc:description xml:lang="en-US">The purpose of this research is to apply and compare the discriminant analysis, logistic regression, Neural Network (NN) and Multivariate Adaptive Regression Spline (MARS) at HBAT and IRIS data. Next will be the classification of fourth methods using the SPSS statistical software, MINITAB, MARS, and R. The results showed that the data HBAT predictor variables affected to the response variable which is the quality of the product (X6), Complaint resolution (X9) and Salesforce image (X12), whereas all predictor variables on the IRIS data affect the response variable. A more precise method used in HBAT data classification is NN and discriminant analysis because the value of the resulting classification accuracy is greater, especially for testing. While a more precise method used in the IRIS data classification is discriminant analysis because the value of the resulting classification accuracy is greater</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2007-12-01</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/228</dc:identifier>
	<dc:identifier>10.30598/barekengvol1iss2pp8-13</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 1 No 2 (2007): BAREKENG : Jurnal Ilmu Matematika dan Terapan; 8-13</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/228/192</dc:relation>
</oai_dc:dc>