Advance Search

Pemodelan Faktor-Faktor yang Mempengaruhi Kasus Stunting di Sulawesi Selatan Menggunakan Geographically Weighted Regression

DOI: https://doi.org/10.20956/ejsa.v5i2.30142
Article History

Submited : September 14, 2023
Published : July 27, 2024

Keywords:
One of the prevalent nutritional issues affecting toddlers worldwide is stunting. Several studies on stunting cases have been conducted in Indonesia. However, modeling using the Geographically Weighted Regression (GWR) method in South Sulawesi has not been carried out. This study aims to identify the variables that affect the incidence of stunting in each district in South Sulawesi based on spatial modeling using the GWR method. Data on the number of stunting cases, the pproportion of low-birth-weight infants, the percentage of under-five who are malnourished, the percentage of proper drinking water, and the percentage of poor people in South Sulawesi in 2020 were used. The results show that the GWR model has an  value of 86.64%, which is higher than that of the global regression model. The factors that influence the percentage of stunting based on the GWR modeling results are the percentage of under-five who are malnourished and the percentage of proper drinking water. The findings of this study are anticipated to help the government address the issue of stunting in South Sulawesi. Early prevention may then be implemented.

References

  1. Komalasari, K., Supriati, E., Sanjaya, & Ifayanti, H. Faktor-Faktor Penyebab Kejadian Stunting Pada Balita. Maj. Kesehat. Indones., 1(2), pp. 51–56, 2020, doi: 10.47679/makein.202010.
  2. Kemenkes R. I. Hasil Riset Kesehatan Dasar Tahun 2018. Kementrian Kesehat. RI, 53(9), pp. 1689–1699, 2018.
  3. Kemenkes RI, Profil Kesehatan Indonesia 2020. Indonesia, 2020. doi: 10.1524/itit.2006.48.1.6.
  4. Dinas Kesehatan Sulawesi Selatan, Profil Kesehatan Provinsi Sulawesi Selatan 2021. Dinas Kesehat. Provinsi Sulawesi Selatan. Makassar: Dinas Kesehatan Provinsi Sulawesi Selatan, 2021.
  5. Griffith, D. A., & Anselin, L. Spatial Econometrics: Methods and Models, 65(2). 1989. doi: 10.2307/143780.
  6. Qiu, Q., Sung, J., Davis, W., & Tchernis, R. Using spatial factor analysis to measure human development. J. Dev. Econ., 132, 130–149, 2018, doi: https://doi.org/10.1016/j.jdeveco.2017.12.007.
  7. Ma, Y., & Gopal, S. Geographicallyweighted regression models in estimating median home prices in towns of Massachusetts based on an urban sustainability framework. Sustain., 10(4), 2018, doi: 10.3390/su10041026.
  8. Beal, T., Tumilowicz, A., Sutrisna, A., Izwardy, D., & Neufeld, L. M. A review of child stunting determinants in Indonesia. Matern. Child Nutr., 14(4), 1–10, 2018, doi: 10.1111/mcn.12617.
  9. Aswi, A., Sukarna, S., & Nurhilaliyah. Pemetaan Risiko Relatif Kasus Stunting di Provinsi Sulawesi Selatan. Sainsmat, 11(1), 11–20, 2022.
  10. Cholid, F., Trishnanti, D., & Azies, H. A. Pemetaan Faktor-Faktor yang Mempengaruhi Stunting pada Balita dengan Geographically Weighted Regression (GWR). Semnakes, 2019, 156–165, 2019.
  11. Anjas, M., Sukarsa, A. I. K. G., & Kencana, I. P. E. N. Penerapan Metode Geographically Weighted Regression (Gwr) Pada Kasus Penyakit Pneumonia Di Provinsi Jawa Timur. E-Jurnal Mat., 8(1), 27, 2019, doi: 10.24843/mtk.2019.v08.i01.p231.
  12. Raza, O., Mansournia, M. A., Foroushani, A. R., & Holakouie-Naieni, K. Geographically weighted regression analysis: A statistical method to account for spatial heterogeneity. Arch. Iran. Med., 22(3), 155–160, 2019.
  13. Caraka, R. E., & Yasin, H. Geographically Weighted Regression (GWR) : Sebuah Pendekatan Regresi Geografis, Edisi Pert. Indonesia: Mobius, 2017.

Downloads

Download data is not yet available.
Fulltext
pdf
statcounter