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Modelling the Probability of River Water Pollution Using Geographically Weighted Logistic Regression Model (Case Study: River Water DO Data in East Kalimantan)

Pemodelan Peluang Pencemaran Air Sungai Menggunakan Model Geographically Weighted Logistic Regression (Studi Kasus: Data DO Air Sungai di Kalimantan Timur)

Jurnal Matematika, Statistika dan Komputasi · Vol. 21 No. 2 (2025) · pp. 408–430

DOI
10.20956/j.v21i2.40346
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

English

Geographically Weighted Logistic Regression (GWLR) is a local binary logistic regression model, and it’s applied to the spatial heterogeneity data. The parameter estimation of GWLR model in this study uses Maximum Likelihood Estimation (MLE) method, and it’s conducted at each observation location with spatial weighting. The spatial weight in this study was calculated using the adaptive tricube function. The spatial weighting function depends on distance between observation location and bandwidth, where the determination of optimal bandwidth uses the Akaike Information Criterion (AIC). The aim of this research is to identify the factors influencing the probability of river water pollution in East Kalimantan Province through GWLR modelling to Dissolved Oxygen (DO) data 2022, and to interpret it based on the best model. The research data is secondary data provided by Life Environment Department of East Kalimantan Province. Research concludes that the GWLR was fit model based on the results of similarity testing of the GWLR model and global model, as well as simultaneous parameter testing, with the model fitting measure was a McFadden R-Squared value of 61,1%, and an AIC value of 29,629. Based on partial parameter testing, local factors influencing chance of river water pollution in East Kalimantan can be identified, namely nitrate concentration and water color degree. Based on the GWLR modelling to DO data 2022, it can be interpreted that increasing nitrate concentration and water colour degree respectively will increase the probability of river water pollution

Bahasa Indonesia

Model Geographically Weighted Logistic Regression (GWLR) adalah model regresi logistik biner lokal, dan diterapkan pada data heterogenitas spasial (spasial tipe titik). Penaksiran parameter model GWLR dalam penelitian ini menggunakan metode Maximum Likelihood Estimation (MLE), dan dilakukan di setiap titik lokasi pengamatan dengan pembobot spasial. Pembobot spasial pada penelitian ini dihitung menggunakan fungsi pembobot adaptive tricube. Fungsi Pembobot spasial tergantung pada jarak antar lokasi pengamatan dan bandwidth, dimana penentuan bandwidth optimum menggunakan kriteria Akaike Information Criterion (AIC). Tujuan penelitian ini adalah mengidentifikasi faktor-faktor yang berpengaruh terhadap peluang pencemaran air sungai di Provinsi Kalimantan Timur melalui pemodelan GWLR pada data Dissolved Oxygen (DO) Tahun 2022, dan mendapatkan interpretasi model GWLR. Data penelitian adalah data sekunder yang disediakan oleh Dinas Lingkungan Hidup (DLH) Provinsi Kalimantan Timur. Penelitian ini menyimpulkan bahwa faktor-faktor lokal yang memengaruhi peluang air sungai di Kalimantan Timur tercemar adalah konsentrasi nitrat dan derajat warna air. Berdasarkan pemodelan GWLR pada data DO diinterpretasikan bahwa setiap kenaikan konsentrasi nitrat dan derajat warna air masing-masing akan meningkatkan peluang pencemaran air sungai.

Keywords

Collaboration network of the authors

19 authors · 35 co-authorship links

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Co-authorOrganisationJoint articles
Sifriyani SifriyaniAffiliation unknown3
Zakiyah Mar'ahAffiliation unknown1
Andrea Tri Rian DaniAffiliation unknown1
Asnita AsnitaAffiliation unknown1
Ghina Fadhilla MumtazAffiliation unknown1
Memi Nor HayatiAffiliation unknown1
Ineu SintiaAffiliation unknown1
Siti MahmudaAffiliation unknown1
Siti Fatimah KhairunnisaAffiliation unknown1
Meiliyani SiringoringoAffiliation unknown1
Ni Made Shantia AnandaAffiliation unknown1
SuyitnoAffiliation unknown1
Andrea Tri DaniAffiliation unknown1
Hardina SandaririaAffiliation unknown1
Darnah DarnahAffiliation unknown1
Azkadiaannzumi AzkadiaannzumiAffiliation 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">Modelling the Probability of River Water Pollution  Using Geographically Weighted Logistic Regression Model (Case Study: River Water DO Data in East Kalimantan)</dc:title>
	<dc:title xml:lang="id">Pemodelan Peluang Pencemaran Air Sungai  Menggunakan Model Geographically Weighted Logistic Regression (Studi Kasus: Data DO Air Sungai di Kalimantan Timur)</dc:title>
	<dc:creator xml:lang="en">Miranda, Adelia</dc:creator>
	<dc:creator xml:lang="en">Suyitno, Suyitno</dc:creator>
	<dc:creator xml:lang="en">Fauziyah, Meirinda</dc:creator>
	<dc:subject xml:lang="en">AIC</dc:subject>
	<dc:subject xml:lang="en">DO</dc:subject>
	<dc:subject xml:lang="en">GWLR</dc:subject>
	<dc:subject xml:lang="en">MLE</dc:subject>
	<dc:subject xml:lang="en">river water pollution</dc:subject>
	<dc:description xml:lang="en">Geographically Weighted Logistic Regression (GWLR) is a local binary logistic regression model, and it’s applied to the spatial heterogeneity data. The parameter estimation of GWLR model in this study uses Maximum Likelihood Estimation (MLE) method, and it’s conducted at each observation location with spatial weighting. The spatial weight in this study was calculated using the adaptive tricube function. The spatial weighting function depends on distance between observation location and bandwidth, where the determination of optimal bandwidth uses the Akaike Information Criterion (AIC). The aim of this research is to identify the factors influencing the probability of river water pollution in East Kalimantan Province through GWLR modelling to Dissolved Oxygen (DO) data 2022, and to interpret it based on the best model. The research data is secondary data provided by Life Environment Department of East Kalimantan Province. Research concludes that the GWLR was fit model based on the results of similarity testing of the GWLR model and global model, as well as simultaneous parameter testing, with the model fitting measure was a McFadden R-Squared value of 61,1%, and an AIC value of 29,629. Based on partial parameter testing, local factors influencing chance of river water pollution in East Kalimantan can be identified, namely nitrate concentration and water color degree. Based on the GWLR modelling to DO data 2022, it can be interpreted that increasing nitrate concentration and water colour degree respectively will increase the probability of river water pollution</dc:description>
	<dc:description xml:lang="id">Model Geographically Weighted Logistic Regression (GWLR) adalah model regresi logistik biner lokal, dan diterapkan pada data heterogenitas spasial (spasial tipe titik). Penaksiran parameter model GWLR dalam penelitian ini menggunakan metode Maximum Likelihood Estimation (MLE), dan dilakukan di setiap titik lokasi pengamatan dengan pembobot spasial. Pembobot spasial pada penelitian ini dihitung menggunakan fungsi pembobot adaptive tricube. Fungsi Pembobot spasial tergantung pada jarak antar lokasi pengamatan dan bandwidth, dimana penentuan bandwidth optimum menggunakan kriteria Akaike Information Criterion (AIC). Tujuan penelitian ini adalah mengidentifikasi faktor-faktor yang berpengaruh terhadap peluang pencemaran air sungai di Provinsi Kalimantan Timur melalui pemodelan GWLR pada data Dissolved Oxygen (DO) Tahun 2022, dan mendapatkan interpretasi model GWLR. Data penelitian adalah data sekunder yang disediakan oleh Dinas Lingkungan Hidup (DLH) Provinsi Kalimantan Timur. Penelitian ini menyimpulkan bahwa faktor-faktor lokal yang memengaruhi peluang air sungai di Kalimantan Timur tercemar adalah konsentrasi nitrat dan derajat warna air. Berdasarkan pemodelan GWLR pada data DO diinterpretasikan bahwa setiap kenaikan konsentrasi nitrat dan derajat warna air masing-masing akan meningkatkan peluang pencemaran air sungai.</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>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="id">Naskah artikel</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/40346</dc:identifier>
	<dc:identifier>10.20956/j.v21i2.40346</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 21 No. 2 (2025): JANUARY 2025; 408-430</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/40346/12442</dc:relation>
	<dc:coverage xml:lang="id">Sungai-sungai di Kalimantan Timur, Indonesia</dc:coverage>
	<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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