GEOGRAPHICALLY WEIGHTED REGRESSION ANALYSIS WITH ADAPTIVE GAUSSIAN IN THE SOCIAL AND ECONOMIC FIELDS FOR TUBERCULOSIS IN SOUTH SUMATRA 2020

Dia Cahya Wati(1*), Ismi Rizqa Lina(2)


(1) Department of Data Science, University of Insan Cita Indonesia
(2) Department of Data Science, University of Insan Cita Indonesia
(*) Corresponding Author

Abstract


Most of the tuberculosis germs not only attack the lungs, but can also attack other organs. Low income, population density, education level, low public health knowledge, and sanitation in the home environment are sources of transmission for tuberculosis sufferers. In this case, the number of tuberculosis cases varies between districts/cities. This study aims to analyze the factors that influence tuberculosis in South Sumatra using the Geographically Weighted Regression (GWR) approach. GWR is a modification of a simple regression model into a weighted regression model that can better explain the relationship between response variables and predictors. Tuberculosis is spread in every sub-district so that it can be identified more deeply in each sub-district using the GWR with an adaptive gaussian weighting function. Based on the results of the study, the distribution of tuberculosis was affected into 5 groups. The dominant group with economic influence is the average expenditure per capita (RPP) in the areas of Ogan Komering Ilir Regency, Banyuasin Regency, Ogan Ilir Regency, Palembang City and Prabumulih City and the coefficient of determination is 96.10834%.

Keywords


Tuberculosis; Geographically Weighted Regression (GWR); Adaptive Gaussian; South Sumatera.

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References


Zaina, A. S. N., Pontoh, R. S., & Tantular, B. (2021, August). Pemodelan Dan Pemetaan Penyakit TB Paru di Kota Bandung Menggunakan Geographically Weighted Negative Binomial Regression: Studi Kasus Dinas Kesehatan Kota Bandung. In Prosiding Seminar Nasional Statistika Aktuaria| Departemen Statistika FMIPA Universitas Padjadjaran (Vol. 1, pp. 62-71).

Ramadhani, S. (2020). Analisis Spasial Penyebaran Penyakit Tuberkulosis di Sumatera Utara Menggunakan Indeks Moran dan Local Indicator of Spatial Association (LISA).

Azzahra, Z. (2017). Faktor-Faktor yang Mempengaruhi Kejadian Penyakit Tuberkulosis Paru di Wilayah Kerja Puskesmas Muliorejo Kecamatan Sunggal Kabupaten Deli Serdang Tahun 2017.

Larasati, Widya. (2015). TBC: Kurangnya Kesadaran Masyarakat Indonesia. https://www.kompasiana.com/widyalaras/552c0d216ea834e3388b4569/tbc-kurangnya-kesadaran-masyarakat-indonesia. Diakses pada 31 Mei 2022

Meutuah, S. M., Yasin, H., & Di Asih, I. M. (2017). Pemodelan Fixed Effect Geographically Weighted Panel Regression untuk Indeks Pembangunan Manusia di Jawa Tengah. Jurnal Gaussian, 6(2), 241-250.

Maulani, A., Herrhyanto, N., & Suherman, M. (2016). Aplikasi Model Geographically Weighted Regression (GWR) Untuk Menentukan Faktor-Faktor Yang Mempengaruhi Kasus Gizi Buruk Anak Balita Di Jawa Barat. Jurnal EurekaMatika, 4(1), 46-63.

Annabilah, Z. F., & Sutanto, H. T. (2019). Pemodelan Indeks Pembangunan Manusia di Jawa Timur Menggunakan Geographically Weighted Regression (GWR). Mathunesa: Jurnal Ilmiah Matematika, 7(1).

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons.

Lumaela, A. K., Otok, B. W., & Sutikno, S. (2013). Pemodelan Chemical Oxygen Demand (COD) Sungai di Surabaya dengan Metode Mixed Geographically Weighted Regression. Jurnal Sains dan Seni ITS, 2(1), D100-D105.

Leung, Y., Mei, C. L., & Zhang, W. X. (2000). Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model. Environment and Planning A, 32(1), 9-32.

Ramani, A. (2014). Hubungan Indeks Pembangunan Manusia Dengan Indikator Penyakit, Lingkungan, Dan Gizi Masyarakat (Analisis Data Sekunder Negara Anggota UNDP). Jurnal Ilmu Kesehatan Masyarakat, 10(1)

BPS. 2021. Sirusa Angka Harapan Hidup (AHH). Jakarta: BPS.

BPS. 2020. Kasus Penyakit menurut Kabupaten/ Kota. Sumatera Selatan: BPS.

Chasco, C., García, I., & Vicéns, J. (2007). Modeling Spatial Variations in Household Disposable Income with Geographically Weighted Regression.

Dinas Kesehatan Provinsi Sumatera Selatan. (2019). Profil Kesehatan Provinsi Sumatera Selatan. https://e-renggar.kemkes.go.id/file_performance/1-119013-2tahunan-255.pdf. Diakses pada 31 Mei 2022.

Media, Yulfira. 2011. Pengetahuan, Sikap dan Perilaku Masyarakat tentang Penyakit Tuberklosis (TB) Paru di Kecamatan Sungai Tarab, Kabupaten Tanah Datar Provinsi Sumatera Barat. Media Litbang Kesehatan,21(2):82-88.

Pierre De Bellfon, Marrie. Geographically Weighted Regression. file:///C:/Users/user/Downloads/imet131-m-chapitre-9.pdf. Diakses tanggal 31 Mei 2022, (Hal. 232-254).

Wikurendra, E. A. (2010). Faktor Faktor Yang Mempengaruhi Kejadian Tb Paru Dan Upaya Penanggulangannya. Jurnal Ekologi Kesehatan, 9(4), 1340-1346.


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DOI: https://doi.org/10.26714/jsunimus.11.2.2023.22-30

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