Bahasa Indonesia Modeling Geographically Weighted Negative Binomial Regression (GWNBR) on Stunting Incidence in Malang Regency

Bahasa Indonesia


  • Elok Pratiwi Universitas Brawijaya
  • Henny Pramoedyo Universitas Brawijaya
  • Suci Astutik Universitas Brawijaya
  • Fahimah Fauwziyah Universitas Brawijaya



GWNBR, Stunting, Malang Regency


Discrete data on the response variable can be analyzed using poisson regression. The assumption of equidispersion in poisson regression must be fulfilled, but in practice there are many problems of overdispersion. The negative binomial regression model is used to overcome the problem of overdispersion, but this model is global while in some cases each location has different characteristics. Therefore, a method that considers the effects of spatial heterogeneity is needed. If the response variable is discrete data that is overdispersed and includes spatial effects, a model called Geographically Weighted Negative Binomial Regression (GWNBR) is developed. The GWNBR method can be applied in the health sector, such as in stunting. The prevalence of stunting in Malang Regency is still quite high, there is 25.7%. By conducting the GWNBR test, 385 models were obtained, one of them is Tulungrejo Village with factors influencing the incidence of stunting, namely access to permanent healthy latrines, access to posyandu, exclusive breastfeeding, population density and community empowerment. From three weights used, namely the Adaptive Gaussian Kernel, Adaptive Bisquare Kernel and Adaptive Tricube Kernel, the best model was obtained from the Adaptive Bisquare Kernel weighting with the smallest AIC is -211.3763.


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Afri, L. E. (2017). Perbandingan Regresi Binomial Negatif dan Regresi Conway-Maxwell-Poisson dalam Mengatasi Overdispersi pada Regresi Poisson. Jurnal Gantang, 2(1). 79–87.

Anjas, A. M., Sukarsa, I. K. G., & Kencana, I. P. E. N. (2019). Penerapan Metode Geographically Weighted Regression (GWR) pada Kasus Penyakit Pneumonia Di Provinsi Jawa Timur. E-Jurnal Matematika, 8(1). 27 – 34.

Anselin, L. (1988). Spatial Econometrics: Methods and Models. Springer- Science+Bussiness Media. Journal of the American Statistical Assosiation, 85(411). 905 – 906.

Bertalina, & R., A. P. (2018). Hubungan Asupan Gizi, Pemberian Asi Eksklusif, dan Pengetahuan Ibu dengan Status Gizi (Tb/U) Balita 6-59 Bulan. Jurnal Kesehatan, 9(1). 117 – 125.

Cameron, A. C., & Trivedi, P. K. (2013). Regression Analysis of Count Data, Second Edition. Cambridge:Cambridge University Press.

da Silva, A. R., & Rodrigues, T. C. V., 2013. Geographically Weighted Negative Binomial Regression-Incorporating Overdispersion. Statistics and Computing, 24. 769 – 783.

Delvia, N., Mustafid, & Yasih, H., 2021. Geographically Weighted Negative Binomial Regression Untuk Menangani Overdispersi Pada Jumlah Penduduk Miskin. Jurnal Gaussian, 10(4). 532-543.

Kementerian Kesehatan RI, 2020. Buletin Jendela Data dan Informasi Kesehatan: Situasi Stunting di Indonesia.

Ulfa, Y. A., Soleh, A. M., & Sartono, B., 2021. Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java. Indonesian Journal of Statistics and Its Applications, 5(1). 1–13.

Rahayu, H. K., Kandarina, I, & Wahab, A., 2019. Antenatal care visit frequency of short stature mother as risk factor of stunting among children aged 6 - 23 months in Indonesia (IFLS 5 Study Analysis). Indonesian Journal of Nutrition and Dietetics, 7(03). 107-113.

Soekatri, M. Y. E., Sandjaja, S., & Syauqy, A. (2020). Stunting Was Associated with Reported Morbidity, Parental Education and Socioeconomic Status in 0.5–12-Year-Old Indonesian Children. International Journal Of Environmental Research and Public Health, 17,6204. 1-9.

Ulhaq, H. Y. D., Wasono, R., & Nur, I. M., 2020. Geographically Weighted Logistic Regression (GWLR) with Gaussian Adaptive Kernel Weighting Function, Bisquare, and Tricube In Case Of Malnutrition Of Toddlers In Indonesia. Jurnal Litbang Edusaintech, 1,1. 5 – 15.

Yuhan, R. J., & Sitorus, J. R. H., 2017. Metode Geographically Weighted Regression Pada Karakteristik Penduduk Hampir Miskin di Kabupaten/Kota Pulau Jawa. Jurnal Ilmiah Widya Eksakta. 41 – 47.

Zhang, L., Cheng, J., & Jin, C., 2019. Spatial Interaction Modeling of OD Flow Data: Comparing Geographically Weighted Negative Binomial Regression (GWNBR) and OLS (GWOLSR). International Journal of Geo-Information, 8, 220. 1 – 18.

Zulkarnain, M., Sitorus, R. J., Appulembang, Y. A., Jasmine, A. B., & Lutfi, A., 2022. The Relationship between Stunting and Tooth Eruption of Primary School Children at Tuah Negeri Sub-district, Musi Rawas District, South Sumatera Province, Indonesia. Journal of Medical Sciences, 10(E). 238-242.




How to Cite

Pratiwi, E. ., Pramoedyo, H. ., Astutik, S. ., & Fauwziyah, F. . (2022). Bahasa Indonesia Modeling Geographically Weighted Negative Binomial Regression (GWNBR) on Stunting Incidence in Malang Regency: Bahasa Indonesia. Jurnal Matematika, Statistika Dan Komputasi, 19(1), 163-171.



Research Articles