Model Generalized Poisson Regression (GPR) pada Faktor-Faktor yang Mempengaruhi Jumlah Kasus Stunting di Kabupaten Kupang, Provinsi Nusa Tenggara Timur (NTT)
DOI:
https://doi.org/10.20956/ejsa.v7i1.44106Keywords:
Generalized Poisson Regression (GPR), Modeling, Overdispersion, Risk Factors, StuntingAbstract
Stunting is a condition of failure to thrive due to chronic malnutrition, recurrent infections, and poor sanitation. East Nusa Tenggara (NTT) Province is the highest contributor of stunting cases in Indonesia and Kupang Regency is also the third highest contributor of stunting cases in NTT Province. This study aims to identify factors that influence the number of stunting cases using the Generalized Poisson Regression (GPR) model which is able to overcome overdispersion in count data. Secondary data for 2023 was obtained from the Kupang District Health Office and BPS. Independent variables included LBW, complete basic immunization (IDL), exclusive breastfeeding, nutritional status of children under five, access to sanitation and safe drinking water, vitamin A administration, number of health centers, and health workers. The results of the analysis show that the percentage of IDL toddlers, the percentage of neighborhoods with access to safe drinking water, the number of infants receiving Vitamin A, exclusive breastfeeding, the number of health centers, and the number of community health workers have a significant effect on the number of stunting cases in Kupang district. These findings can inform the formulation of more effective health intervention policies in the region.
References
Frongillo, E. A. Introduction. The Journal of Nutrition, 129(2), 529S–530S, 1999.
UNICEF, WHO, & World Bank Group. Levels and Trends in Child Malnutrition: Joint Child Malnutrition Estimates – 2023 Edition. UNICEF, 2023.
Badan Pusat Statistik Provinsi Nusa Tenggara Timur. Jumlah dan Persentase Balita Stunting Menurut Kabupaten/Kota (Jiwa), 2021–2024. 2025.
Lais, M. F., Atti, A., Pangaribuan, R. M., & Guntur, R. D. Model Generalized Poisson Regression pada Kasus Stunting di Provinsi Nusa Tenggara Timur. Jurnal Diferensial, 5(2), 68–75, 2023.
Zubedi, F., Aliu, M. A., Rahim, Y., & Oroh, F. A. Analisis Faktor-Faktor yang Mempengaruhi Stunting pada Balita di Kota Gorontalo Menggunakan Regresi Binomial Negatif. Jambura Journal of Probability and Statistics, 2(1), 48–55, 2021.
Putri, F. R. D. Pemodelan Generalized Poisson Regression terhadap Kejadian Stunting pada Balita di Kabupaten Bondowoso. Skripsi, Universitas Islam Negeri Maulana Malik Ibrahim Malang, 2023.
Maxwell, O., Mayowa, B. A., Chinedu, I. U., & Peace, A. E. Modelling Count Data: A Generalized Linear Model Framework. American Journal of Mathematics and Statistics, 8(6), 179–183, 2018.
Zubedi, F., Oroh, F. A., & Aliu, M. A. Pemodelan Stunting dan Gizi Kurang di Kabupaten Bone Bolango Menggunakan Regresi Poisson Generalized. JMPM: Jurnal Matematika dan Pendidikan Matematika, 6(2), 113–128, 2021.
Durmuş, B., & Güneri, Ö. An Application of the Generalized Poisson Model for Overdispersion Data on the Number of Strikes Between 1984 and 2017. Alphanumeric Journal, 8(2), 249–260, 2020.
Guntur, R. D., & Njudang, C. C. I. A. Pemodelan Generalized Poisson Regression (GPR) untuk Mengatasi Pelanggaran Equidispersi pada Regresi Poisson Kasus Pneumonia di Provinsi NTT. MATHunesa: Jurnal Ilmiah Matematika, 13(1), 2025.
Molli, W. Statistik Deskriptif untuk Penelitian: Olah Data Manual dan SPSS Versi 25. Yogyakarta: Bintang Pustaka Madani, 2020.
Tiara, Y., Aidi, M. N., Erfiani, R., & Rachmawati, R. Overdispersion Handling in Poisson Regression Model by Applying Negative Binomial Regression. BAREKENG: Journal of Mathematics and Its Applications, 17(1), 417–426, 2023.
Bili Dappa, J. S., & Dole Guntur, R. Pemodelan Generalized Poisson Regression pada Jumlah Kasus AIDS di Provinsi Nusa Tenggara Timur. VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 6(2), 95–104, 2024.
Tendriyawati. Pemodelan Regresi Poisson terhadap Faktor-Faktor yang Mempengaruhi Terjadinya Hipertensi di Kota Kendari. Jurnal Matematika, Komputasi dan Statistika, 3(1), 255–262, 2023.
Marita, V. E. Pemodelan terhadap Faktor-Faktor yang Mempengaruhi Jumlah Penduduk Miskin di Jawa Timur Menggunakan Generalized Poisson Regression. Skripsi, Institut Teknologi Sepuluh Nopember, Surabaya, 2015.
Fitrial, N. H., & Fatikhurrizqi, A. Pemodelan Jumlah Kasus COVID-19 di Indonesia dengan Pendekatan Regresi Poisson dan Regresi Binomial Negatif. Prosiding Seminar Nasional Official Statistics, 2020(1), 65–72, 2021.
Yunardi, D. A., Maiyastri, M., & Yozza, H. Pemodelan Penderita Stroke dan Diabetes Melitus di Kota Padang dengan Model Regresi Logistik Biner Bivariat. Jurnal Matematika UNAND, 9(4), 270–277, 2021.
Guntur, R. D., & Da Rato, M. R. Generalized Poisson Regression Modeling on the Number of Infant Deaths in East Nusa Tenggara Province in 2022. J-Stat: Jurnal Ilmiah Teori dan Aplikasi Statistika, 17(2), 779–788, 2024.
Girik Allo, C. B., Otok, B. W., & Purhadi. Estimation Parameter of Generalized Poisson Regression Model Using Generalized Method of Moments and Its Application. IOP Conference Series: Materials Science and Engineering, 546(5), 052050, 2019.
Darsyah, M. Y., & Ramadhan, M. N. Pemodelan Jumlah Kasus Penyakit Kusta di Provinsi Sulawesi Tenggara Menggunakan Metode Regresi Poisson Invers Gaussian. Jurnal Litbang Edusaintech, 3(1), 11–24, 2022
Downloads
Published
Issue
Section
License
Copyright
It is the author's responsibility to ensure that his or her submitted work does not infringe any existing copyright. Authors should obtain permission to reproduce or adapt copyrighted material and provide evidence of approval upon submitting the final version of a manuscript.
