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Abstract
Spatial data are data containing information on the location or geography of a region on the representation of objects on earth. Geographically Weighted Regression (GWR) is a development of the Ordinary Least Square (OLS) theory into a weighted regression model that considers spatial effects, resulting in a parameter estimation that can only be used to predict each location where the data are observed. The Human Development Index (HDI) is an essential indicator for measuring success in efforts to build human quality of life. HDI data regencies/cities in Central Java are interconnected, so it is said to be spatial data and there are spatial effects in it. Therefore, the GWR method was applied to obtain faculties affecting HDI in Central Java Province. The data used were secondary data in 2020. The determination coefficients of the GWR model ranged between 76.09% and 87.16%. If the variable values of population density and Gross Regional Domestic Product (GRDP) increase by one unit in each district/city in Central Java Province, the HDI variable value increases. These results were visualized on a dashboard providing information about the characteristics of HDI and independent variables, GWR parameter estimates, and the significance of independent variables in each regency/city.
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References
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References
D.S. Susanti, A.S. Lestia, and Y. Sukmawaty, “Pemodelan Tingkat Kesejahteraan Peduduk Propinsi Kalimantan Selatan dengan Pendekatan Geographically Weighted Regression (GWR),” in Prosiding Seminar Nasional MIPA 2016, Oct. 2016, pp. 184¬–192.
D.T. Hapsery Alfisyahrina, “Aplikasi Geographically Weighted Regression (GWR) untuk Pemetaan Faktor yang Mempengaruhi Indeks Aktivitas Literasi Membaca Di Indonesia,” Jurnal Riset dan Aplikasi Matematika, vol. 5, no. 2, pp. 80–90, Oct. 2021, doi: 10.26740/jram.v5n2.p80-91.
M. Anjas A, I.K.G. Sukarsa, and I.P.E.N. Kencana, “Penerapan Metode Geographically Weighted Regression (GWR) pada Kasus Penyakit Pneumonia di Provinsi Jawa Timur,” E-Jurnal Matematika, vol. 8, no. 1, pp. 27–34, Feb. 2019, doi: 10.24843/MTK.2019.v08.i01.p231.
I.M. Hida, A. Sukmono, and H.S. Firdaus, “Analisis Kerentanan Sosial dan Pengaruhnya terhadap Masalah Sosial dengan Geographically Weighted Regression (GWR) (Studi Kasus: Kota Semarang),” Jurnal Geodesi Undip, vol. 9, no. 1, pp. 237–246, Dec. 2019, doi: 10.14710/jgundip.2020.26168.
A. Maulani, N. Herrhyanto, and M. Suherman, “Aplikasi Model Geographically Weighted Regression (GWR) untuk Menentukan Faktor-Faktor yang Mempengaruhi Kasus Gizi Buruk Anak Balita di Jawa Barat,” Jurnal EurekaMatika, pp. Vol. 4, No. 1, pp. 46–63, May 2016, doi: 10.17509/jem.v4i1.10454.
A.F.J.P. Dao and Kartiko, “Analisis Geographically Weighted Regression Menggunakan Pembobot Adaptive Bisquare dan Near Neighbourhood Kernel (Studi Kasus: Kemiskinan di Provinsi Sulawesi Tenggara tahun 2017),” Jurnal Statistika Industri dan Komputasi, vol. 4, no. 2, pp. 75 –93, Jul. 2019, doi: 10.34151/statistika.v4i2.1950.