Pangesti, Elsa Maulida (2025) Pemodelan Jumlah Kasus Kusta di Provinsi Jawa Timur Menggunakan Geographically Weighted Generalized Poisson Regression. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kusta merupakan salah satu penyakit yang menjadi masalah kesehatan serius di banyak wilayah endemis. Angka penemuan kasus baru kusta per 100.000 penduduk di Provinsi Jawa Timur mengalami peningkatan selama tiga tahun terakhir. Hal ini menunjukkan pentingnya penelitian lebih lanjut untuk mengetahui dan memahami faktor-faktor yang memengaruhi keberadaan kasus kusta di wilayah ini. Dalam penelitian ini, data jumlah kasus kusta memiliki nilai varians yang lebih besar daripada rata-rata yang menunjukkan adanya kasus overdispersi dan dalam pengujian dengan Breusch-Pagan Test didapatkan bahwa data memiliki heterogenitas spasial. Oleh karena itu, penelitian ini menggunakan metode Geographically Weighted Generalized Poisson Regression (GWGPR). Penelitian ini bertujuan untuk mendeskripsikan jumlah kasus kusta dan memodelkan jumlah kasus kusta beserta faktor-faktor yang diduga memengaruhinya dengan GWGPR. Pemodelan GWGPR dilakukan dengan menggunakan dua fungsi pembobot yang berbeda yaitu Adaptive Bisquare Kernel dan Adaptive Gaussian Kernel. Hasil penelitian menunjukkan bahwa berdasarkan nilai Akaike’s Information Criteria Corrected (AICc), model GWGPR dengan fungsi pembobot Adaptive Gaussian Kernel merupakan model terbaik untuk memodelkan jumlah kasus kusta di Provinsi Jawa Timur dengan AICc sebesar 314,91. Model GWGPR menggunakan fungsi pembobot Adaptive Gaussian Kernel menghasilkan lima kelompok kabupaten/kota dengan variabel yang berpengaruh signifikan pada beberapa wilayah di model ini adalah variabel kepadatan penduduk, rasio jumlah puskesmas terhadap 100.000 penduduk dan rasio jumlah tenaga kesehatan masyarakat terhadap 100.000 penduduk.
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Leprosy is a disease that is a serious health problem in many endemic areas. The rate of new leprosy cases per 100.000 population in East Java Province has increased over the last three years. This suggests the importance of further research to determine and understand the factors that influence the presence of leprosy cases in this region. Previous studies have shown that several factors influence the number of leprosy cases in East Java Province such as the ratio of health workers, access to proper sanitation and clean and healthy behavior. The leprosy case count data exhibit overdispersion, and the Breusch-Pagan test confirms the presence of spatial heterogeneity. To address these characteristics, this study applies the Geographically Weighted Generalized Poisson Regression (GWGPR) method. This study aims to describe the number of leprosy cases and modelling the number of leprosy cases and the factors that are thought to affect them with GWGPR. The modeling is performed using two different spatial weighting functions, which are the Adaptive Bisquare Kernel and the Adaptive Gaussian Kernel. Based on the Akaike’s Information Criterion Corrected (AICc), the GWGPR model with an Adaptive Gaussian Kernel weighting function is the best model for modeling the number of leprosy cases in East Java Province, with an AICc value of 314,91. This GWGPR model using the Adaptive Gaussian Kernel produces five groups of districts/cities. The variables that have a significant effect in several regions are population density, the ratio of puskesmas per 100.000 population, and the ratio of public health workers per 100.000 population.
Item Type: | Thesis (Other) |
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Uncontrolled Keywords: | Geographically Weighted Generalized Poisson Regression, Jawa Timur, Kusta, Overdispersi |
Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
Depositing User: | Elsa Maulida Pangesti |
Date Deposited: | 31 Jul 2025 02:57 |
Last Modified: | 31 Jul 2025 02:57 |
URI: | http://repository.its.ac.id/id/eprint/124516 |
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