Pendekatan Geographically Weighted Regression (GWR) untuk Menganalisis Hubungan PDRB Sektor Pertanian, Kehutanan, dan Perikanan dengan Faktor Pencemaran Lingkungan di Jawa Timur
Keywords:
GRDP, Spatial, Environment, Geographically Weighted Regression
Abstract
The Geographically Weighted Regression (GWR) method is a method used to analyze spatial heterogeneity, where the same independent variable gives unequal responses at different locations in a research area. The purpose of this study was to determine the environmental pollution factors that affect GRDP in the agricultural, forestry and fisheries sectors in East Java. The data used in this study are the GRDP of the Agriculture, Forestry and Fisheries sectors in East Java in 2020 along with the environmental pollution factors that are thought to influence it. The results of this study obtained a different model for each district/city. The GWR model shows better results than the multiple linear regression model, as seen from the smallest AIC value and the largest R2
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Caraka, R. E., & Yasin, H. (2017). Geographically Weighted Regression (GWR) ; Sebuah Pendekatan Regresi Geografis (Pertama, hal. 160). Mobius.
Dienelly, U., Bakri, S., & Santoso, T. (2017). Pengaruh Perubahan Tutupan Hutan Dan Lahan Terhadap Produk Domestik Regional Bruto (PDRB) Di Sektor Pertanian, Kehutanan Dan Industri : Studi Di Provinsi Lampung. Jurnal Sylva Lestari, 5(1), 61. https://doi.org/10.23960/jsl1561-70
Faris, N. A. (2022). Analisis Pengaruh PDRB Sektor Pertanian, Industri, Dan Transportasi Terhadap Emisi Karbondioksida Di Provinsi Jawa Timur. Jurnal Ilmiah Mahasiswa FEB. https://jimfeb.ub.ac.id/index.php/jimfeb/article/view/8176
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons, Inc.
Hocking, R. R. (2003). Methods and Applications of Linear Models (Kedua). John Wiley & Sons, Inc. https://doi.org/10.2307/1271138
Lee, J., & Wong, D. W. S. (2001). Statistical Analysis with ArcView GIS. John Willey & Sons, Inc.
Nasir, R., Annas, S., & Nusrang, M. (2020). Pemodelan dengan Spatial Autoregressive (SAR) pada Angka Putus Sekolah Bagi Anak Usia Wajib Belajar di Provinsi Sulawesi Selatan. VARIANSI: Journal of Statistics and Its application on Teaching and Research, 3(1), 44. https://doi.org/10.35580/variansiunm9358
Ndruru, E, M.Situmorang, G. T. (2014). Analisa Faktor-Faktor yang Mempengaruhi Hasil Produksi Padi di Deli Serdang. Saintia Matematika, 2(1), 71–83.
Suardin, M., Bustan, M. N., & Ahmar, A. S. (2020). Pemodelan Pertumbuhan Ekonomi Provinsi Sulawesi Selatan dengan Menggunakan Regresi Data Panel. VARIANSI: Journal of Statistics and Its application on Teaching and Research, 2(3), 158. https://doi.org/10.35580/variansiunm14637
Sugiyono. (2007). Statistika Untuk Penelitian. CV Alfabeta.
Tiro, M. A. (2010). Analisis Korelasi dan Regresi (Ketiga). Andira Publisher.
Trenggonowati, D. L., Kulsum, K., & Salma, F. A. (2020). Model Persamaan Regresi Pengoptimalan Waktu Pelayanan Bongkar Kargo Menggunakan Multiple Regression Analysis di PT. XYZ. Journal Industrial Servicess, 6(1), 13. https://doi.org/10.36055/jiss.v6i1.9484
Yasin, H., Warsito, B., & Hakim, A. R. (2020). Regresi Spasial (Aplikasi dengan R) (Pertama). WADE Publish.
Published
2024-04-30
How to Cite
Bakri, N. A., Annas, S., & Aidid, M. K. (2024). Pendekatan Geographically Weighted Regression (GWR) untuk Menganalisis Hubungan PDRB Sektor Pertanian, Kehutanan, dan Perikanan dengan Faktor Pencemaran Lingkungan di Jawa Timur. VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 6(01), 11-20. https://doi.org/10.35580/variansiunm194
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