CLASSIFICATION OF STUNTING USING GEOGRAPHICALLY WEIGHTED REGRESSION-KRIGING CASE STUDY: STUNTING IN EAST JAVA
Abstract
Geographically Weighted Regression Kriging (GWRK) is a special case of Geographically Weighted Regression (GWR) model, which is modeling with the effect of spatial autocorrelation on the GWR model error. The purpose of this research is to obtain a GWRK model between the factors that affect stunting density for each site viewed from the district center point in East Java Province and to make a prediction map based on the GWRK modeling. The data used was obtained from Basic Health Research (RISKESDAS) and the East Java Health Profile Book for 2021. The units of observation in this study were 38 districts in East Java.. Based on the GWR modeling results, it was found that the GWR model error contained spatial autocorrelation so that GWR model can be formed. From the GWRK modeling using stunting prevalence data in East Java in 2021, it was found that the GWR model was better than the global regression. Through prediction and prediction mapping formed from the GWR-Kriging modeling, it could be seen that stunting in regencies in East Java was evenly distributed . The interpolation map showed that the stunting forecasting values using the Kriging GWR interpolation ranged from 27% to 46%.
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References
A. Ernawati, “Gambaran Penyebab Balita Stunting di Desa Lokus Stunting Kabupaten Pati,” J. Litbang Media Inf. Penelitian, Pengemb. Dan IPTEK, vol. 16, no. 2, pp. 77–94, 2020.
K. Rahmadhita, “Permasalahan Stunting dan Pencegahannya,” J. Ilm. Kesehat. Sandi Husada, vol. 9, no. 1, pp. 225–229, 2020.
R. Kim, I. Mejia-Guevara, D. J. Corsi, V. M. Aguayo, and S. V Subramanian, “Relative importance of 13 correlates of child stunting in South Asia: Insights from nationally representative data from Afghanistan, Bangladesh, India, Nepal, and Pakistan,” Soc. Sci. Med., vol. 187, pp. 144–154, 2017.
I. Budiastutik and S. A. Nugraheni, “Determinants of stunting in Indonesia: A review article,” Int. J. Heal. Res., vol. 1, no. 2, pp. 43–49, 2018.
T. Mulyaningsih, I. Mohanty, V. Widyaningsih, T. A. Gebremedhin, R. Miranti, and V. H. Wiyono, “Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia,” PLoS One, vol. 16, no. 11, p. e0260265, 2021.
D. Izwardy, “Studi Status Gizi Balita Terintegrasi Susenas 2019,” Balitbangkes Kemenkes RI, 2020.
T. Beal, A. Tumilowicz, A. Sutrisna, D. Izwardy, and L. M. Neufeld, “A review of child stunting determinants in Indonesia,” Matern. Child Nutr., vol. 14, no. 4, p. e12617, 2018.
L. Fitri, “Hubungan BBLR Dan Asi Ekslusif Dengan Kejadian Stunting Di Puskesmas Lima Puluh Pekanbaru,” J. Endur. Kaji. Ilm. Probl. Kesehat., vol. 3, no. 1, pp. 131–137, 2018.
C. R. Titaley, I. Ariawan, D. Hapsari, A. Muasyaroh, and M. J. Dibley, “Determinants of the stunting of children under two years old in Indonesia: a multilevel analysis of the 2013 Indonesia basic health survey,” Nutrients, vol. 11, no. 5, p. 1106, 2019.
H. Pramoedyo, M. Mudjiono, A. A. Fernandes, D. Ardianti, and K. Septiani, “Determination of Stunting Risk Factors Using Spatial Interpolation Geographically Weighted Regression Kriging in Malang,” Mutiara Med. J. Kedokt. dan Kesehat., vol. 20, no. 2, pp. 98–103, 2020.
A. Fadliana and P. P. Darajat, “Pemetaan Faktor Risiko Stunting Berbasis Sistem Informasi Geografis Menggunakan Metode Geographically Weighted Regression,” ikraith-informatika, vol. 5, no. 3, pp. 91–102, 2021.
H. Al Azies, F. Cholid, and D. Trishnanti, “Pemetaan Faktor-Faktor yang Mempengaruhi Stunting pada Balita dengan Geographically Weighted Regression (GWR),” semnaskes 2019, pp. 156–165, 2019.
W. Simeng, W. Dazhao, and H. Chang, “A comparative study of using ANUSPLIN and GWR models for downscaled GPM precipitation,” in 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 2019, pp. 1–5.
Q. Zhou, C. Wang, and S. Fang, “Application of geographically weighted regression (GWR) in the analysis of the cause of haze pollution in China,” Atmos. Pollut. Res., vol. 10, no. 3, pp. 835–846, 2019.
C. Zeng et al., “Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method,” Geoderma, vol. 281, pp. 69–82, 2016.
A. Y. K. Kartini and L. N. Ummah, “Pemodelan Kejadian Balita Stunting di Kabupaten Bojonegoro dengan Metode Geographically Weighted Regression dan Multivariate Adaptive Regression Splines,” J Stat. J. Ilm. Teor. dan Apl. Stat., vol. 15, no. 1, 2022.
Y. Sen Sun, W. F. Wang, and G. C. Li, “Spatial distribution of forest carbon storage in Maoershan region, Northeast China based on geographically weighted regression kriging model.,” Ying Yong Sheng tai xue bao= J. Appl. Ecol., vol. 30, no. 5, pp. 1642–1650, 2019.
S. Bahmani, S. R. Naganna, M. A. Ghorbani, M. Shahabi, E. Asadi, and S. Shahid, “Geographically weighted regression hybridized with Kriging model for delineation of drought-prone Areas,” Environ. Model. Assess., vol. 26, no. 5, pp. 803–821, 2021.
A. A. Rostami, M. Isazadeh, M. Shahabi, and H. Nozari, “Evaluation of geostatistical techniques and their hybrid in modelling of groundwater quality index in the Marand Plain in Iran,” Environ. Sci. Pollut. Res., vol. 26, no. 34, pp. 34993–35009, 2019.
A. S. Fotheringham, W. Yang, and W. Kang, “Multiscale geographically weighted regression (MGWR),” Ann. Am. Assoc. Geogr., vol. 107, no. 6, pp. 1247–1265, 2017.
D. A. I. M. Siti Maulina Meutuah, Hasbi Yasin, “Pemodelan Fixed Effect Geographically Weighted Panel,” J. Gaussian, vol. Vol 6 Nomo, pp. 241–250, 2017, [Online]. Available: https://media.neliti.com/media/publications/98983-ID-none.pdf.
A. LUTFI, “Identifikasi Autokorelasi Spasial Angka Partisipasi Sekolah di Provinsi Sulawesi Selatan Menggunakan Indeks Moran.” Universitas Negeri Makassar, 2019.
P. Harris, A. S. Fotheringham, R. Crespo, and M. Charlton, “The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets,” Math. Geosci., vol. 42, no. 6, pp. 657–680, 2010.
A. Muche, M. S. Melaku, E. T. Amsalu, and M. Adane, “Using geographically weighted regression analysis to cluster under-nutrition and its predictors among under-five children in Ethiopia: Evidence from demographic and health survey,” PLoS One, vol. 16, no. 5, p. e0248156, 2021.
M. M. Fischer and A. Getis, Handbook of applied spatial analysis: software tools, methods and applications. Springer, 2010.
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