Modelling the Probability of River Water Pollution Using Geographically Weighted Logistic Regression Model (Case Study: River Water DO Data in East Kalimantan)
DOI:
https://doi.org/10.20956/j.v21i2.40346Keywords:
AIC, DO, GWLR, MLE, river water pollutionAbstract
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
References
[1] Agresti, A., 2013. Categorical Data Analysis Third Edition. New Jersey: John Wiley & Sons, Inc.
[2] Berliana, S. M., Purhadi, Sutikno & Rahayu, S. P., 2020. Parameter estimation and hypothesis testing of geographically weighted multivariate generalized poisson regression. mathematics. 8 (1523), pp. 1-14.
https://doi.org/10.3390/math8091523
[3] BPS, 2023. Statistik Air Bersih Provinsi Kalimantan Timur 2022. Kalimantan Timur: Badan Pusat Statistik.
[4] Caraka, R. E. & Yasin, H., 2017. Geographically Weighted Regression: Sebuah Pendekatan Regresi Geografis. Yogyakarta: MOBIUS.
[5] Fotheringham, A. S., Brundson, C. & Charlton, M. E., 2002. Geographically Weighted Logistic Regression: The Analysis of Spatially Varying Relationship. England: John Wiley & Sons.
[6] Gujarati, D. A., 2003. Basic Econometric Fourth Edition. New York: McGraw-Hill Inc.
[7] Haq, I., Aidi, M. N., Kurnia, A. & Efriwati, 2023. A Comparison of Logistics Regression and Geographically Weighted Logistics Regression (GWLR) on COVID-19 Data in West Sumatra. Barekeng: Journal of Mathematics and Its Applications. 17 (3), pp. 1749-1760.
https://doi.org/10.30598/barekengvol17iss3pp1749-1760
[8] Harlan, J., 2018. Analisis Regresi Logistik. Depok: Gunadarma
[9] Hosmer, D. W. & Lemeshow, S., 2000. Applied Logistic Regression, Second Edition. New York: John Willey & Sons.
[10] Inayah, U. R., Suyitno & Siringoringo, M., 2021. Upaya Pencegahan Pencemaran Air Sungai Mahakam melalui Pemodelan Geographically Weighted Logistic Regression pada Data BOD. Jurnal Eksponensial, 12 (1), pp. 17-26.
https://doi.org/10.30872/eksponensial.v12i1.755
[11] Kementerian Lingkungan Hidup dan Kehutanan, 2021. Peraturan Menteri Lingkungan Hidup dan Kehutanan Republik Indonesia Nomor 5 Tahun 2021 Tentang Tata Cara Penerbitan Persetujuan Teknis dan Surat Kelayakan Operasional Bidang Pengendalian Pencemaran Lingkungan. Jakarta: Menteri Lingkungan Hidup dan Kehutanan Republik Indonesia.
[12] Lestari, V. D., Suyitno, S. & Siringoringo, M., 2021. Analisis Faktor-Faktor Yang Berpengaruh Terhadap Pencemaran Air Sungai Mahakam Menggunakan Pemodelan Geographically Weighted Logistic Regression Pada Data Dissolved Oxygen. Jurnal Eksponensial, 12 (1), pp. 37-46.
https://doi.org/10.30872/eksponensial.v12i1.757
[13] Pemerintah Republik Indonesia, 2022. Lampiran VI, Peraturan Pemerintah Republik Indonesia Nomor 22 Tahun 2021, Tentang Penyelenggaraan Perlindungan dan Pengelolaan Lingkungan Hidup. Jakarta: Pemerintah Republik Indonesia.
https://jdih.setkab.go.id/PUUdoc/176367/Lampiran_VI_Salinan_PP_Nomor_22_Tahun_2021.pdf
[14] Pemerintah Republik Indonesia, 2022. Peraturan Pemerintah Republik Indonesia Nomor 22 Tahun 2021, Tentang Penyelenggaraan Perlindungan dan Pengelolaan Lingkungan Hidup. Jakarta: Pemerintah Republik Indonesia.
https://jdih.setkab.go.id/PUUdoc/176367/PP_Nomor_22_Tahun_2021.pdf
[15] Perdana, H., Satyahadewi, N. & Arsy, F. M., 2023. Determining Student Graduation Based on School Location Using Geographically Weighted Logistics Regression. Barekeng: Journal of Mathematics and Its Applications. 17 (4), pp. 2273-2280.
https://doi.org/10.30598/barekengvol17iss4pp2273-2280
[16] Purwanti, S. I., Sutikno & Purhadi, 2021. Parameter and hypothesis testing of geographically and temporally weighted bivariate generalized Poisson Regression. IOP Conference Series: Earth and Environmental Science. 880 (2021) 012043, pp. 1-11.
https://iopscience.iop.org/article/10.1088/1755-1315/880/1/012043
[17] Rifada, M., Chamidah, N., Ningrum, R. A. & Muniroh, L., (2023) Stunting Determinants Among Toddlers in Probolinggo of Indonesia Using Parametric and Nonparametric Ordinal Logistic Regression Models. Commun. Math. Biol. Neurosci. 2023 (2023), 8, pp. 1-14.
http://www.scik.org/index.php/cmbn/article/view/6690
[18] Salsavira, S., Afifah, J., Mahendra, F. T. & Dzakiyah, L., 2021. Analisis Spasial Prevalensi Perkawinan Dini dan IPM di Indonesia. Jurnal Matematika, Statistika Dan Komputasi, 18 (1), pp. 31-41.
https://doi.org/10.20956/j.v18i1.13975
[19] Scabra, A. R., Afriadin, A. & Marzuki, M., 2022. Efektivitas Peningkatan Oksigen Terlarut Menggunakan Perangkat Microbubble Terhadap Produktivitas Ikan Nila. Jurnal Perikanan Unram, 12 (1), pp. 13–21. https://jperairan.unram.ac.id/index.php/JP/article/view/269
[20] Sifriyani, S., Rasjid, M., Rosadi. D., Anwar, S., Wahyuni. R. D. & Jalaluddin, S., 2022. Spatial-Temporal Epidemiology of Covid-19 Using a Geographically and Temporally Weighted Regression Model. Symmetry, 14 (4),742, pp. 1-8.
https://www.mdpi.com/2073-8994/14/4/742
[21] Suyitno, Purhadi, Sutikno, & Irhamah, 2017. Multivariate Weibull regression model. Far East Journal of Mathematical Sciences, 101 (9), pp. 1977-1992.
https://doi.org/10.17654/MS101091977
[22] Suyitno, Purhadi, Sutikno, and Irhamah, 2016. Parameter estimation of geographically weighted trivariate Weibull regression model,” Applied Mathematical Sciences, 10 (18), 861–878. https://doi.org/10.12988/ams.2016.6129.
[23] Wardhani, Q. S., Handajani, S. S. & Susanto, I., 2022. Pemodelan Indeks Pembangunan Kesehatan Masyarakat dengan Metode Geographically Weighted Logistic Regression. Jurnal Aplikasi Statistika & Komputasi Statistik, 14 (1), 1-12.
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