Pemodelan Kasus Pneumonia pada Balita di Kabupaten Tuban Menggunakan Metode Geographically Weighted Generalized Poisson Regression

Wardani, Sulistya Ningrum Ayu (2025) Pemodelan Kasus Pneumonia pada Balita di Kabupaten Tuban Menggunakan Metode Geographically Weighted Generalized Poisson Regression. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pneumonia merupakan salah satu penyakit menular yang masih menjadi penyebab utama kematian balita. Kabupaten Tuban termasuk dalam wilayah dengan kasus pneumonia yang cukup tinggi, sehingga diperlukan analisis mendalam untuk mengetahui faktor-faktor yang memengaruhi penyeberannya. Penelitian ini bertujuan untuk memodelkan jumlah kasus pneumonia pada balita dengan mempertimbangkan faktor-faktor risiko dan pengaruh spasial antar wilayah desa/kelurahan. Data yang digunakan merupakan data jumlah kasus pneumonia pada balita tahun 2023 di Kabupaten Tuban beserta enam variabel prediktor dan jumlah balita sebagai exposure. Pemodelan dilakukan dengan pendekatan Geographically Weighted Generalized Poisson Regression (GWGPR) dengan exposure. Hasil analisis menunjukkan bahwa terdapat perbedaan pengaruh antar lokasi dan variabel yang berpengaruh signifikan terhadap jumlah kasus pneumonia pada balita. Model GWGPR dengan exposure memberikan hasil yang lebih baik berdasarkan nilai AICc terkecil, dibandingkan model global karena mampu menangkap variasi spasial antar wilayah. Variabel yang signifikan secara global maupun lokal adalah persentase pemberian ASI eksklusif. Pemodelan GWGPR dengan exposure menghasilkan 2 (dua) kelompok desa/kelurahan berdasarkan variabel-variabel yang berpengaruh signifikan.

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Pneumonia is one of the infectious diseases that remains a leading cause of death among children under five. Tuban Regency is among the regions with a relatively high number of pneumonia cases, thus requiring an in-depth analysis to identify the factors influencing its spread. This study aims to model the number of pneumonia cases in children under five by considering risk factors and spatial effects between villages/sub-districts. The data used include the number of pneumonia cases in 2023 in Tuban Regency, six predictor variables, and the number of children under five as an exposure variable. The modeling approach employed is Geographically Weighted Generalized Poisson Regression (GWGPR) with exposure. The analysis results show that there are varying effects across locations and several variables significantly influence the number of pneumonia cases. The GWGPR model with exposure provides better results based on the lowest AICc value compared to the global model, as it captures spatial variation across regions. The variable that is globally and locally significant is the percentage of exclusive breastfeeding. The GWGPR model with exposure also classifies the villages/sub-districts into two groups based on the significant influencing variables.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Balita, Efek Spasial, Geographically Weighted Generalized Poisson Regression, Overdispersi, Pneumonia, Children Under Five, Spatial Effects, Geographically Weighted Generalized Poisson Regression, Overdispersion, Pneumonia
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:	Sulistya Ningrum Ayu Wardani
Date Deposited:	04 Aug 2025 08:47
Last Modified:	04 Aug 2025 08:47
URI:	http://repository.its.ac.id/id/eprint/127187

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