Estimasi Parameter Model Geographically Weighted Ridge Regression pada Indikator Pengukuran Penanganan Stunting di Indonesia
Keywords:
GWRR, Spatial Dependencies, Local Multicollinearity, Stunting, IKPSAbstract
Regression that is implemented on cross-sectional data and weighted by geographic space coordinates is called Geographically Weighted Regression (GWR). When local multicollinearity problem is detected in GWR model, Geographically Weighted Ridge Regression (GWRR) can include this problem. One of the government's main agendas is to accelerate the reduction of stunting in children under five.. The prevalence of stunting among children under five shows a decrease between 2020 and 2021. GWRR performs very well in handling local multicollinearity problems by looking at the almost perfect coefficient of determination ( ) of 99.99815%. From all provinces in Indonesia, the biggest factors that influence IKPS are KPS/KKS or Food Aid Recipients, Education Dimension, and Immunization.
References
Alinti, N. R. (2023). Pemodelan Geographically Weighted Ridge Regression Pada Kejadian Malaria Di Indonesia Tahun 2018 [Skripsi]. Universitas Tadulako Palu.
Arthayanti, Y., Srinadi, I. G. A. M., & Gandhiadi, G. K. (2017). Geographically Weighted Ridge Regression dalam Kasus Multikolinearitas Pada Indeks Pembangunan Manusia di Kabupaten/Kota Provinsi Jawa Timur. Jurnal Matematika, 7(2), 124. https://doi.org/10.24843/JMAT.2017.v07.i02.p89
Bivand, R. (2023). Geographically Weighted Regression. CRAN R.
BPS. (2022). Laporan Indeks Khusus Penanganan Stunting Kabupaten/Kota 2020-2021. Badan Pusat Statistik RI.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
Chen, S., Xiong, L., Ma, Q., Kim, J.-S., Chen, J., & Xu, C.-Y. (2020). Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method. Journal of Hydrology, 589, 125156. https://doi.org/10.1016/j.jhydrol.2020.125156
Crawford, T. W. (2009). Scale Analytical. In International Encyclopedia of Human Geography (pp. 29–36). Elvesier.
Fadliana, A., Pramoedyo, H., & Fitriani, R. (2019). Parameter Estimation of Locally Compensated Ridge-Geographically Weighted Regression Model. IOP Conference Series: Materials Science and Engineering, 546(5), 052022. https://doi.org/10.1088/1757-899X/546/5/052022
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons.
Haining, R. P. (2001). Spatial Sampling. International Encyclopedia of Social & Behavioral Sciences, 14822–14827.
Paradis, E. (2023). Analyses of Phylogenetics and Evolution. CRAN R.
Pebesma, E., & Bivand, R. (2023). Spatial Data Science With Applications in R. Routledge Taylor & Francis Group.
Percival, J. E. H., Tsutsumida, N., Murakami, D., Yoshida, T., & Nakaya, T. (2022). Exploratory Spatial Data Analysis with gwpcorMapper: An Interactive Mapping Tool for Geographically Weighted Correlation and Partial Correlation. Journal of Geovisualization and Spatial Analysis, 6(1), 17. https://doi.org/10.1007/s41651-022-00111-3
Percival, J., & Tsutsumida, N. (2017). Geographically Weighted Partial Correlation for Spatial Analysis. GI_Forum, 1, 36–43. https://doi.org/10.1553/giscience2017_01_s36
Pourmohammadi, P., Strager, M. P., Dougherty, M. J., & Adjeroh, D. A. (2021). Analysis of Land Development Drivers Using Geographically Weighted Ridge Regression. Remote Sensing, 13(7), 1307. https://doi.org/10.3390/rs13071307
Qur’ani, A. Y. (2014). Pemodelan Geographically Weighed Regression Panel (GWRPanel) sebagai Pendekatan Geographically Weighted Regression (GWR) dengan Menggunakan Fixed Effect Model Time Trend [Skripsi]. Universitas Brawijaya Malang.
Qur’ani, A. Y. (2023). Pemodelan Principal Component Regression Analysis dari Faktor Penanganan Stunting saat Pandemi Covid-19 di Indonesia. Ulil Albab, 2(8), 3922–3931.
Qur’ani, A. Y., & Subanar. (2021). A Spatial Nonhomogeneous Poisson Process Model Using Bayesian Approach on a Space-Time Geostatistical Data. African Journal of Mathematics and Statistics Studies, 4(3), 186–198. https://doi.org/10.52589/AJMSS-C4L7KHUC
Riznawati, A. (2023). Wilayah Prioritas Penanganan Stunting di Jakarta Timur Tahun 2021. Jurnal Penelitian Kesehatan Suara Forikes, 14(1), 123–128.
Salima, B. A., & Bellefon, M.-P. D. (2018). 3. Spatial Autocorrelation Indices. In Handbook of Spatial Analysis Theory and Application with R (pp. 51–70). INSEE.
Setyorini, R. H., & Andriyani, A. (2023). Peningkatan Pengetahuan Tentang Stunting Sebagai Upaya Pencegahan Terjadinya Stunting. JURNAL PENGABDIAN KEPADA MASYARAKAT, 3(2), 61–68.
Tsutsumida, N. (2021). Geographically Weighted Partial Correlation Coefficient. CRAN R.
Wheeler, D. (2022). Fits Geographically Weighted Regression Models with Diagnostic Tools. CRAN R.
Yoantika, A. F. & Susiswo. (2021). Comparing the Principal Regression Analysis Method with Ridge Regression Analysis in Overcoming Multicollinearity on Human Development Index (HDI) Data in Regency/City of East Java in 2018. Journal of Physics: Conference Series, 1872(1), 012024. https://doi.org/10.1088/1742-6596/1872/1/012024