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Determining Factors that Influence Unmet Need For Family Planning Using Geographically Weighted Logistic Regression With LASSO:

Jurnal Matematika, Statistika dan Komputasi · Vol. 21 No. 2 (2025) · pp. 583–600

DOI
10.20956/j.v21i2.35081
ISSN
2614-8811, 1858-1382
Published
2025-01-12
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Binary logistic regression is a regression used for categorical response variables with two possibilities: success or failure. This regression is a global model, making it inappropriate for spatial data. Binary logistic regression was then developed into geographically weighted logistic regression (GWLR). GWLR considers location factors into the model through a weight function. Nevertheless, GWLR is unable to overcome multicollinearity issue. Multicollinearity can cause the estimated parameters to be insignificant, thus it needs to be solved. A method to deal with multicollinearity is least absolute shrinkage and selection operator (LASSO). LASSO is applicable to various areas, including health, namely in the case of unmet need for family planning (FP). Unmet need for FP refers to productive-age women who do not wish to have more children or wish to postpone having children without using contraceptive methods. This study aims to obtain GWLR model with LASSO and influential factors, and acquire the performance of GWLR model with LASSO on unmet need for FP in South Sulawesi. The AIC value of the GWLR with LASSO model, which is 31,918, is less than the AIC value of the GWLR without LASSO, which is 38,879. This implies that GWLR with LASSO method is able to model unmet need for FP better than GWLR model. In addition, it was obtained that the status of unmet need for FP in 22 districts/cities was affected by the percentage of women with junior high school education or equivalent or lower, number of high-fertility women, percentage of husbands/families who refuse family planning, and number of KB staffs, while there were 2 districts/cities where the status of unmet need for KB was determined by the number of high-fertility women, percentage of husbands/families who refuse family planning, and number of FP staffs.

Keywords

Collaboration network of the authors

40 authors · 133 co-authorship links · showing the 40 most connected

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  • Sultan Ageng Tirtayasa University
  • Affiliation unknown
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  • Same organisation
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  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Raupong RaupongAffiliation unknown5
Siswanto SiswantoAffiliation unknown4
Anisa AnisaSultan Ageng Tirtayasa University3
Muhammad Jayzul UsrahAffiliation unknown2
Anisa KalondengAffiliation unknown2
Ainun Utari BudistiharahAffiliation unknown2
Mamik UtamiAffiliation unknown2
Widya Nauli Amalia PuteriAffiliation unknown1
Zhazha Alifkhamulki RamdhaniAffiliation unknown1
Samsul ArifinAffiliation unknown1
Bunga ApriliaAffiliation unknown1
Nirwan IlyasAffiliation unknown1
Dewi Rahma EnteAffiliation unknown1
AnisaAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Novilia JaoAffiliation unknown1
Fitri FitriAffiliation unknown1
Hajratul Ashwad KAffiliation unknown1
Andi Kresna JayaAffiliation unknown1
Andi Ummi Melin Aicha AichaAffiliation unknown1
Muhammad FadilAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
A. Elisha WulandariAffiliation unknown1
Belia Nurul FajriAffiliation unknown1
Abdullah PannuAffiliation unknown1
Alfian Akbar VallarinoAffiliation unknown1
Anugrah Nur Isnaeni RahmanAffiliation unknown1
Nasrah SirajangAffiliation unknown1
Muhammad ZakirAffiliation unknown1
Ummi SariAffiliation unknown1
Fajar AffanAffiliation unknown1
Nurul RezkiAffiliation unknown1
Sri Indriani AmilAffiliation unknown1
Fadilah Amirul AdhelAffiliation unknown1
Erna Tri HerdianiAffiliation unknown1
Nuravia NuraviaAffiliation unknown1
Nurmiati Nurmiati NurdinAffiliation 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://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Determining Factors that Influence Unmet Need For Family Planning Using Geographically Weighted Logistic Regression With LASSO:  </dc:title>
	<dc:creator xml:lang="en">Rusdy, Dian Ayu Permata Sari</dc:creator>
	<dc:creator xml:lang="en">Thamrin, Sri Astuti</dc:creator>
	<dc:creator xml:lang="en">Islamiyati, Anna</dc:creator>
	<dc:subject xml:lang="en">Unmet need</dc:subject>
	<dc:subject xml:lang="en">keluarga berencana</dc:subject>
	<dc:subject xml:lang="en">regresi logistis biner</dc:subject>
	<dc:subject xml:lang="en">GWLR</dc:subject>
	<dc:subject xml:lang="en">multikolinearitas</dc:subject>
	<dc:subject xml:lang="en">LASSO</dc:subject>
	<dc:description xml:lang="en">Binary logistic regression is a regression used for categorical response variables with two possibilities: success or failure. This regression is a global model, making it inappropriate for spatial data. Binary logistic regression was then developed into geographically weighted logistic regression (GWLR). GWLR considers location factors into the model through a weight function. Nevertheless, GWLR is unable to overcome multicollinearity issue. Multicollinearity can cause the estimated parameters to be insignificant, thus it needs to be solved. A method to deal with multicollinearity is least absolute shrinkage and selection operator (LASSO). LASSO is applicable to various areas, including health, namely in the case of unmet need for family planning (FP). Unmet need for FP refers to productive-age women who do not wish to have more children or wish to postpone having children without using contraceptive methods. This study aims to obtain GWLR model with LASSO and influential factors, and acquire the performance of GWLR model with LASSO on unmet need for FP in South Sulawesi. The AIC value of the GWLR with LASSO model, which is 31,918, is less than the AIC value of the GWLR without LASSO, which is 38,879. This implies that GWLR with LASSO method is able to model unmet need for FP better than GWLR model. In addition, it was obtained that the status of unmet need for FP in 22 districts/cities was affected by the percentage of women with junior high school education or equivalent or lower, number of high-fertility women, percentage of husbands/families who refuse family planning, and number of KB staffs, while there were 2 districts/cities where the status of unmet need for KB was determined by the number of high-fertility women, percentage of husbands/families who refuse family planning, and number of FP staffs.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2025-01-12</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/35081</dc:identifier>
	<dc:identifier>10.20956/j.v21i2.35081</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 21 No. 2 (2025): JANUARY 2025; 583-600</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/35081/12454</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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