Penerapan Metode Geographically Weighted Logistic Regression Untuk Memodelkan Pencemaran Air Sungai Mahakam Berdasarkan Data Dissolved Oxygen

Wiwit Widyaningsih; Suyitno Suyitno; Qonita Qurrota A'yun

doi:10.30872/eksponensial.v15i2.1365

Wiwit Widyaningsih Universitas Mulawarman
Suyitno Suyitno Universitas Mulawarman
Qonita Qurrota A'yun Universitas Mulawarman

DOI: https://doi.org/10.30872/eksponensial.v15i2.1365

Abstract

The Geographically Weighted Logistic Regression (GWLR) model is a local model of logistic regression applied to spatial heterogenity data. Parameter estimation of the GWLR model is conducted at each observation location using spatial weighting. The aim of this research is to obtain the GWLR model on the Dissolved Oxygen (DO) data of Mahakam River in 2022, and to identify the factors affecting the probability of Mahakam River water is polluted. The research data is secondary data obtained from Environmental Department of East Kalimantan Province. Spatial weight is calculated using the adaptive bisquare weighting function, and the optimal bandwidth is determined using the Generalized Cross Validation (GCV) criterion. Parameter estimation method is Maximum Likelihood Estimation (MLE), and Maximum Likelihood (ML) estimator was obtained using the iterative Newton-Raphson method. Based on the result of the GWLR model parameter testing, it was concluded that locally influential factors on the probability of Mahakam River water pollution are nitrate concentration and iron concentration, and globally influential factor is nitrate concentration.

Downloads

Download data is not yet available.

References

Aliu, M,A., Zubedi, F., Yahya, L., & Oroh, F,A. (2022). Perbandingan Fungsi Pembobot Kernel pada Geographically Weighted Logistic Regression dalam Memodelkan Kasus Kemiskinan di Indonesia. Jurnal Matematika, Statistika & Komputasi. 18(3), 362-384. https://journal.unhas.ac.id/index.php/jmsk/article/view/19567/8139.
Alwi, W., Ermawati, & Husain, S. (2018). Analisis Regresi Logistik Biner untuk Memprediksi Kepuasan Pengunjung pada Rumah Sakit Umum Daerah Majene. Jurnal MSA. 6(1), 20-26. https://journal.uin-alauddin.ac.id/index.php/msa/article/view/4783/pdf.
Anggraini. N., Simarmata, A.H., & Sihotang, C. (2014). Dissolved Oxygen Concentration from the Water Around the Floating Cage Fish Culture Area and From the Area with No Cage. In the DAM Site of the Koto Panjang Reservoir. Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan. 2(1), 1-7. https://jom.unri.ac.id/index.php/JOMFAPERIKA/article/view/4704/4586.
Chairina, P., Suyitno, & Siringoringo, M. (2020). Model-Model Regresi Weibull Univariat pada Indikator Pencemaran Air Dissolved Oxygen di Daerah Aliran Sungai Lingkungan Hutan Hujan Tropis Kalimantan Timur. Jurnal Eksponensial. 11(1), 19-28. https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/641/278.
Hidayanti, U., Latra, I.N., & Purhadi. (2015). Pemodelan dan Pemetaan Jumlah Kasus DBD di Kota Surabaya dengan Geographically Weighted Negative Binomial Regression (GWNBR) dan Flexibly Shaped Spatial Scan Statistic. Jurnal Sains dan Seni ITS. 4(2), 272-278. https://ejurnal.its.ac.id/index.php/sains_seni/article/view/11183/2426.
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), 17-26. https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/755/311.
Lestari, V.D., Suyitno, & 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), 37-46. https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/757/313.
Pemerintah Republik Indonesia. (2021). Peraturan Pemerintah Republik Indonesia Nomor 22 Tahun 2021 Tentang Penyelenggaraan Perlindungan dan Pengelolaan Lingkungan Hidup. Jakarta: Pemerintah Republik Indonesia.
Pradipa, Z. (2023). Analisis Potensi Pencemaran Air Sungai Mahakam menggunakan Pemodelan Regresi Weibull (Studi Kasus: Data Indikator Pencemaran Air DO Tahun 2022) (Skripsi). Universitas Mulawarman.
Pratiwi, N., Suyitno, & Siringoringo, M. (2020). Penerapan Model Geographically Weighted Logistic Regression pada Data Status Kesejahteraan Masyarakat di Kalimantan Timur Tahun 2017. Jurnal Eksponensial. 11(1), 83-92. https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/648/285.
Rahman, R.A., Amalia, A.R., Riandhis, J.A., Hidayah, H., & Mardiah. (2017). Peningkatan Kualitas Air Baku Sungai Mahkaam dengan Teknologi MOCI (Moringa Oleifera and Cellulose Installation). Prosiding Seminar Nasional Teknologi IV. e-ISSN: 2598-7429, 7-14. https://www.researchgate.net/publication/331645449_PENINGKATAN_KUALITAS_AIR_BAKU_SUNGAI_MAHAKAM_DENGAN_TEKNOLOGI_MOCI_MORINGA_OLEIFERA_AND_CELLULOSE_INSTALLATION.
Safitri, R.N., Suyitno, & Hayati, M.N. (2020). Penerapan Model Mixed Geographically Weighted Regression dengan Fugsi Pembobot Adaptive Tricube pada IPM 30 Kabupaten/Kota di Provinsi Kalimantan Timur. Kalimantan Tengah. dan Kalimantan Selatan Tahun 2016. Jurnal Eksponensial. 11(2), 107-116. https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/651/288.
Sembiring, A.P. (2019). Faktor-Faktor yang Memengaruhi Besar Klaim Asuransi Jiwa dengan Menggunakan Model Regresi Tobit. Bimaster. 8(4), 729-736. https://jurnal.untan.ac.id/index.php/jbmstr/article/view/35996/75676583139.
Sriningsih, M., Hatidja, D., & Prang, J.D. (2018). Penanganan Multikolinearitas Dengan Menggunakan Analisis Regresi Komponen Utama Pada Kasus Impor Beras di Provinsi Sulut. Jurnal Ilmiah Sains. 18(1), 18-24. https://ejournal.unsrat.ac.id/v3/index.php/JIS/article/view/19396.
Tizona, A.R., Goejantoro, R., & Wasono. (2017). Pemodelan Geographically Weighted Regression (GWR) dengan Fungsi Pembobot Adaptive Bisquare untuk Angka Kesakitan Demam Berdarah di Kalimantan Timur Tahun 2015. Jurnal Eksponensial. 8(1), 87-94. https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/81/50.