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BINARY LOGISTICS REGRESSION MODEL TO IDENTIFY FACTORS ASSOCIATED WITH LOW BIRTH WEIGHT (LBW) (CASE STUDY: BABY DATA AT DR. M. HAULUSSY HOSPITAL AMBON)

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 3 (2022) · pp. 985–994

DOI
10.30598/barekengvol16iss3pp985-994
ISSN
2615-3017, 1978-7227
Published
2022-09-01
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Low birth weight (LBW) is one of the risk factors for increasing baby mortality. LBW is characterized by a baby's birth weight of fewer than 2500 grams which is weighed within the first hour after birth. The case of LBW is of special concern because it can have a serious impact on the quality of future generations, which will slow down the growth and development of children and affect the decline in intelligence. In this study, identification was carried out to determine the factors that influence the status of BBL in RSUD Dr. M. Haulussy Ambon in 2020, the data used in this study is medical record data from RSUD Dr. M. Haulussy Ambon in 2020 with a total sample of 183 respondents with predictor variables covering nine variables and one response variable. The analysis used is a binary logistic regression method with the response variables of BBL status which are categorized as normal and low. The results of this study obtained a binary logistic regression model in which the factors that influence the case of low birth weight are maternal gestational age and parity with a classification accuracy of 91.8.

Collaboration network of the authors

40 authors · 103 co-authorship links

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  • Universitas Halu Oleo
  • Universitas Pembangunan Nasional Veteran Yogyakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Venn Yan Ishak IlwaruAffiliation unknown3
Mozart W. TalakuaAffiliation unknown2
Zeth Arthur LeleuryAffiliation unknown2
Olumuyiwa James PeterAffiliation unknown2
Henry W.M. PattyAffiliation unknown2
Abraham Z. WattimenaAffiliation unknown1
Susantri SaputriAffiliation unknown1
Venn Y. I. IlwaruAffiliation unknown1
Zeth A. LeleuryAffiliation unknown1
Tesa SumahAffiliation unknown1
H. AbrahamsAffiliation unknown1
Lexy J. SinayAffiliation unknown1
Mirawati I. P. HetharieAffiliation unknown1
Dorteus L. RahakbauwAffiliation unknown1
Rethalina WaasAffiliation unknown1
Salmon Noce AuleleAffiliation unknown1
Natalia KelbulanAffiliation unknown1
Sasmita HayotoAffiliation unknown1
Ronald John DjamiAffiliation unknown1
Elvinus R. PersulessyAffiliation unknown1
Jemsry E. LakotanyAffiliation unknown1
Jesica TentuaAffiliation unknown1
Charlitta FhilyaAffiliation unknown1
Berny Pebo TomasouwAffiliation unknown1
Fredrylo Alberth Noel Joddy ApituleyAffiliation unknown1
Nurhidayah NurhidayahUniversitas Halu Oleo1
Nona Tjie SapuletteAffiliation unknown1
Monalissa E RijolyAffiliation unknown1
Sylvert Prian TahaleaAffiliation unknown1
Swine Enggelina LarubunAffiliation unknown1
Maria Marlein WarongAffiliation unknown1
Monalisa E. RijolyAffiliation unknown1
Anita PapaliaAffiliation unknown1
Vynska Amalia PermadiUniversitas Pembangunan Nasional Veteran Yogyakarta1
Maria Engeline SahusilawaneAffiliation unknown1
Lazarus Kalvein BeayAffiliation unknown1
Mayowa Micheal OjoAffiliation 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">BINARY LOGISTICS REGRESSION MODEL TO IDENTIFY FACTORS ASSOCIATED WITH LOW BIRTH WEIGHT (LBW) (CASE STUDY: BABY DATA AT DR. M. HAULUSSY HOSPITAL AMBON)</dc:title>
	<dc:creator>Sari, Yunita Puspita</dc:creator>
	<dc:creator>Noya Van Delsen, Marlon S.</dc:creator>
	<dc:creator>Lesnussa, Yopi Andry</dc:creator>
	<dc:description xml:lang="en-US">Low birth weight (LBW) is one of the risk factors for increasing baby mortality. LBW is characterized by a baby's birth weight of fewer than 2500 grams which is weighed within the first hour after birth. The case of LBW is of special concern because it can have a serious impact on the quality of future generations, which will slow down the growth and development of children and affect the decline in intelligence. In this study, identification was carried out to determine the factors that influence the status of BBL in RSUD Dr. M. Haulussy Ambon in 2020, the data used in this study is medical record data from RSUD Dr. M. Haulussy Ambon in 2020 with a total sample of 183 respondents with predictor variables covering nine variables and one response variable. The analysis used is a binary logistic regression method with the response variables of BBL status which are categorized as normal and low. The results of this study obtained a binary logistic regression model in which the factors that influence the case of low birth weight are maternal gestational age and parity with a classification accuracy of 91.8.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-09-01</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/5805</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss3pp985-994</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications; 985-994</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss3year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5805/4622</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Yunita Puspita Sari, Marlon S. Noya Van Delsen, Yopi Andry Lesnussa</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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