Geographically Weighted Regression Modeling with Adaptive Gaussian Kernel Weighting on GRDP in Indonesia
DOI:
https://doi.org/10.35799/jis.v24i2.50366Keywords:
Adaptive gaussian kernel, gross regional domestic product, GWRAbstract
The purpose of this study is to determine the variables that affect Gross Regional Domestic Product (GRDP) in Indonesia in 2022 using Geographically Weighted Regression (GWR) with Adaptive Gaussian kernel weighting function. The data used in this study uses secondary data taken from the website of the Central Bureau of Statistics. The variables used are gross regional domestic product of 34 provinces in Indonesia (Y, in billion rupiah), labor force participation rate (X1, in %), foreign investment (X2, in million dollars), open unemployment rate (X3, in %) and human development index (X4, in %). Data were analyzed using GWR with adaptive gaussian kernel weighting function. GRDP in all provinces on the island of Sumatra (11 provinces), DKI Jakarta province, Banten province, and West Kalimantan province are influenced by foreign investment (X2) and human development index (X4). Meanwhile, GRDP in the other 19 provinces is only influenced by foreign investment (X2). GWR model with adaptive gaussian kernel weighting function is formed differently for each province in Indonesia.
Keywords: Adaptive gaussian kernel; GWR; gross regional domestic product
References
Agustina, M.F., Wasono, R., & Darsyah, M.Y. (2015). Pemodelan Geographically Weighted Regression (GWR) Pada Tingkat Kemiskinan di Provinsi Jawa Tengah. Jurnal Statistika, 3(2), 67–74.
Chaerany, I. (2023). Perbandingan Fungsi Pembobot Kernel Pada Model Geographically Weighted Regression Dalam Mengetahui Faktor-Faktor Yang Mempengaruhi Indeks Pembangunan Manusia di Sulawesi Selatan Tahun 2021 [Skripsi]. FMIPA UNN, Makassar.
Fathurahman, M., Purhadi, Sutikno, & Ratnasari, V. (2020). Geographically Weighted Multivariate Logistic Regression Model and Its Application. Abstract and Applied Analysis, 2020, 1–10. https://doi.org/10.1155/2020/8353481
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Apatially Varying Relationships (UK). John Wiley & Sons, New York.
Herbiansyah, T., Yahya, I., Baharuddin, Agusrawati, Ruslan, & Laome, L. (2022). Pemodelan Produk Domestik Bruto Indonesia Menggunakan Geographically Weighted Regression. Prosiding Seminar Nasional Sains Dan Terapan (SINTA), VI, 23–33.
Lutfiani, N., Sugiman, & Mariani, S. (2019). Pemodelan Geographically Weighted Regression (GWR) dengan Fungsi Pembobot Kernel Gaussian dan Bi-Square. UNNES Journal of Mathematics, 8(1), 82–91.
Mamarimbing, F. (2022). Model Spasial Produksi Padi di Kecamatan Ranoyapo Menggunakan Geographically Weighted Regression [Skripsi]. FMIPA UNSRAT, Manado.
Maulani, A., Herrhyanto, N., & Suherman, M. (2016). Aplikasi Model Geographically Weighted Regression (GWR) Untuk Menentukan Faktor-Faktor Yang Mempengaruhi Kasus Gizi Buruk Anak Balita di Jawa Barat. EurekaMatika, 4(1), 46–63. https://doi.org/https://doi.org/10.17509/jem.v4i1.10454
Nadya, M., Rahayu, W., & Santi, V.M. (2017). Analisis Geographically Weighted Regression (GWR) Pada Kasus Pneumonia Balita di Provinsi Jawa Barat. Statistika Dan Aplikasinya, 1(1), 23–32. https://doi.org/https://doi.org/10.21009/JSA.01103
O’Sullivan, D. (2003). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (review). Geographical Analysis, 35(3), 272–275. https://doi.org/10.1353/geo.2003.0008.
Ramadayani, M.R., Indiyah, F.H., & Hadi, I. (2022). Pemodelan Geographically Weighted Regression Menggunakan Pembobot Kernel Fixed dan Adaptive pada Kasus Tingkat Pengangguran Terbuka di Indonesia. JMT : Jurnal Matematika Dan Terapan, 4(1), 51–62. https://doi.org/10.21009/jmt.4.1.5.
Tanadjaja, A., Zain, I., & Wibowo, W. (2017). Pemodelan Angka Harapan Hidup di Papua dengan pendekatan Geographically Weighted Regression. Jurnal Sains Dan Seni ITS, 6(1), 1–7.
Vebiriyana, M., Darsyah, Y.M., & Nur, I.M. (2015). Pemodelan Geographically Weighted Regression Dengan Fungsi Kernel Bisquare Terhadap Faktor-Faktor Yang Mempengaruhi Tingkat Kemiskinan Di Kabupaten Demak. Jurnal Statistika, 3(1), 34–39.
Weku, W., Pramoedyo, H., Widodo, A., & Fitriani, R. (2022). Optimal Bandwidth for Geographically Weighted Regression to Model the Spatial Dependency of Land Prices in Manado, North Sulawesi Province, Indonesia. Geography, Environment, Sustainability, 15(2), 84–90. https://doi.org/10.24057/2071-9388-2019-154
Widyaningsi, Y., & Fitrianingrum, M.R. (2022). Pemodelan Spasial pada Data Produk Domestik Regional Bruto di Pulau Jawa Sebelum dan Ketika Pandemi. Jurnal Statistika dan Aplikasinya, 6(2), 12–25.
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