ANALISIS KEMISKINAN EKSTREM PROVINSI BENGKULU MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DENGAN PEMBOBOT ADAPTIVE GAUSSIAN KERNEL DAN ADAPTIVE BI-SQUARE
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Badan Pusat Statistik (BPS) (2022). Data dan Informasi Kemiskinan Kabupaten/Kota di Indonesia. Jakarta: BPS
Brunsdon Ch, Fotheringham S, and Charlton M. 1996. Geographically Weighted Regression: A Method For Exploring Spatial Nonstationarity. Geogr. Anal. 28, 281-298.
Diantoro, Galuh (2015). Analisis Spasial Kemiskinan di Indonesia Tahun 2013 menggunakan Metode Geographically Weighted Regression (GWR) [Skripsi]. Jakarta: STIS
Dwinata, A. 2012. Model Regresi Logistik Terboboti Geografis (studi kasus :pemodelan kemiskinan di Provinsi Jawa Timur). Tesis. Institut Pertanian Bogor
Fotheringham, A.S.,C. Brunsdon, dan M. Charlton. (2002). Geographically Weighted Regression. Chichester, UK: John Wiley & Sons Ltd
Farida, Ira (2016). Model Geographically Weighted Regression (GWR) dengan pembobot kernel Bisquare. Bandung: UPI
Fatikhurrizqi. A, Kurniawan B.D (2020). Peran Bantuan Sosial dalam Pengentasan Kemiskinan Ekstrem di Jawa Timur Tahun 2020. Seminar Nasional Official Statistics 2022:1027-1036
Hartoyo, G dkk (2010). Modul Pelatihan Sistem Informasi Geografis (SIG) Tingkat Dasar. Bogor: Tropenbos International Indonesia Programme
Lutfiani, Nurul (2017). Geographically Weighted Regression (GWR) Dengan Fungsi Pembobot Kernel Gaussian dan Bi-Square [Skripsi]. Semarang: UNS.
Permai S.D, Tanty H, dan Rahayu, A. 2016. Geographically Weighted Regression Analysis for Human Development Index. Departement of Mathematics and Statistics, School of Computer Science, Bina Nusantara University, Jakarta. Published by the American Institute of Phisics
Putri A, Salamah M. 2013. Pemodelan Kasus Balita Gizi Buruk di Kabupaten Bojonegoro dengan Geographically Weighted Regression. FMIPA, Institut Teknologi Sepuluh Nopember (ITS). Jurnal Sains dan Seni Pomits Vol. 2, No.1
Rahmawati, R. dan Djuraidah, A. (2013). Analisis Geographically Weighted Regression (GWR) dengan Pembobot Kernel Gaussian untuk Data Kemiskinan. Prosiding Seminar Nasional Statistika. Semarang: Universitas Diponegoro
Saefuddin, A., N. A. Setiabudi, dan N. A. Achsani. (2011). On Comparisson between Ordinary Linear Regression and Geographically Weighted Regression: With Application to Indonesian Poverty Data. European Journal of Scientific Research, Vol. 57 No. 2 (2011), pp 275-285
United Nations. (1996). Konsep dan definisi Kemiskinan Ekstrem. Diakses pada tanggal 2 Desember 2022 melalui https://pendampingdesa.com/
Wang, C. 2016. The Impact of car ownership and public transport usage on cancer screening coverage: Empirical evidence using a spatial analysis in England. Journal of transport geography. University of London, page 15-22
Wheeler, DC, Paez, A. (2010). Regresi Berbobot Geografis. Dalam: Fischer, M., Getis, A. (eds) Handbook of Applied Spatial Analysis. Springer, Berlin, Heidelberg
DOI: http://dx.doi.org/10.24036/unpjomath.v8i2.14914