Geographically Weighted Negative Binomial Regression Untuk Pemodelan Faktor-Faktor Yang Memengaruhi Kasus Balita Underweight Di Kota Surabaya

Isyana, Astafiaulia Putri (2026) Geographically Weighted Negative Binomial Regression Untuk Pemodelan Faktor-Faktor Yang Memengaruhi Kasus Balita Underweight Di Kota Surabaya. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Underweight merupakan salah satu masalah kekurangan gizi pada balita yang ditandai oleh berat badan menurut umur berada di bawah standar pertumbuhan. Penelitian ini bertujuan memodelkan faktor-faktor yang memengaruhi jumlah kasus balita underweight pada 63 wilayah kerja puskesmas di Kota Surabaya tahun 2024 menggunakan Geographically Weighted Negative Binomial Regression (GWNBR). Variabel respons berupa jumlah balita underweight, sedangkan jumlah balita yang ditimbang digunakan sebagai variabel exposure. Variabel prediktor meliputi persentase ibu hamil anemia, persentase balita diare, persentase bayi BBLR, persentase bayi yang diberi ASI eksklusif, dan persentase balita yang mendapatkan vitamin A. Data menunjukkan karakteristik data cacah, mengalami overdispersi, serta memiliki heterogenitas spasial. Pemodelan dilakukan menggunakan pembobot adaptive dan fixed dari bisquare kernel dan tricube kernel. Hasil penelitian menunjukkan bahwa model GWNBR dengan pembobot adaptive bisquare kernel memiliki nilai AICc terkecil, yaitu 599,2621, sehingga dipilih sebagai model terbaik. Pada model tersebut, persentase balita diare berpengaruh signifikan secara lokal pada 18 puskesmas, persentase bayi BBLR pada 8 puskesmas, dan persentase balita yang mendapatkan vitamin A pada 24 puskesmas. Hasil ini menunjukkan adanya variasi lokal faktor-faktor yang memengaruhi underweight antarwilayah puskesmas. Temuan tersebut dapat mendukung penyusunan intervensi gizi yang lebih spesifik sesuai karakteristik masing-masing wilayah kerja puskesmas.
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Underweight is a form of undernutrition among children under five, characterized by weight-for-age below the established growth standard. This study aims to model factors affecting the number of underweight cases across 63 community health center catchment areas in Surabaya in 2024 using Geographically Weighted Negative Binomial Regression (GWNBR). The response variable was the number of underweight children, while the number of children weighed was used as the exposure variable. The predictor variables included the percentage of pregnant women with anemia, children under five with diarrhea, low-birth-weight infants, exclusively breastfed infants, and children under five receiving vitamin A supplementation. The data were count data exhibiting overdispersion and spatial heterogeneity. Modeling was conducted using adaptive and fixed from bisquare and tricube kernel weighting functions. The results showed that the GWNBR model with the adaptive bisquare kernel produced the smallest AICc value of 599.2621 and was therefore selected as the best-fitting model. In this model, the percentage of children with diarrhea was locally significant in 18 community health centers, low-birth-weight infants in 8, and children receiving vitamin A supplementation in 24. These findings indicate local variation in the factors associated with underweight and may support more targeted nutrition interventions.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Underweight, Overdispersi, Heterogenitas, Geographically Weighted Negative Binomial Regression (GWNBR), Underweight, Overdispersion, Spatial Heterogeneity, Geographically Weighted Negative Binomial Regression (GWNBR)
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Astafiaulia Putri Isyana
Date Deposited:	05 Aug 2026 01:37
Last Modified:	05 Aug 2026 01:37
URI:	http://repository.its.ac.id/id/eprint/143546

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