PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017

Authors

  • Hardani Prisma Rizky Universitas PGRI Adi Buana Surabaya
  • Wara Pramesti Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas PGRI Adi Buana Surabaya, Indonesia
  • Gangga Anuraga g.anuraga@unipasby.ac.id

DOI:

https://doi.org/10.29244/ijsa.v4i1.563

Keywords:

moran's i, multiple linear regression, sarma, tuberculosis

Abstract

Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).

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References

Angisna, T. (2018). Aplikasi Regresi Spasial untuk Mengetahui Pengaruh Faktor-Faktor Persebaran TB Paru di Kabupaten Magetan [skripsi]. Surabaya (ID): Universitas Airlangga.

Anselin, L. (2005). Spatial regression analysis in R: a workbook. University of Illions, Urbana Champaign.

Anselin, L. (2013). Spatial econometrics: methods and models. Springer Science & Business Media.

[BPS] Badan Pusat Statistik. (2018). Provinsi Jawa Timur dalam Angka tahun 2018. Surabaya (ID): Badan Pusat Statistik Jawa Timur.

[DINKES] Dinas Kesehatan Jawa Timur. (2018). Profil Kesehatan Provinsi Jawa Timur Tahun 2017. Surabaya (ID): Dinas Kesehatan Jawa Timur.

LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. Boca Raton (US): CRC Press.

Suherni, N. A. D., & Maduratna, M. (2013). Analisis Pengelompokan kecamatan di kota surabaya berdasarkan faktor penyebab terjadinya penyakit tuberkulosis. Jurnal Sains Dan Seni ITS, 2(1): D13–D18.

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Published

2020-02-28

How to Cite

Rizky, H. P., Pramesti, W., & Anuraga, G. (2020). PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017. Indonesian Journal of Statistics and Its Applications, 4(1), 164–178. https://doi.org/10.29244/ijsa.v4i1.563

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