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Abstract
Central Java province is one of the provinces with the highest number of poor people on the island of Java, with the number of poor people in 2020 increasing by 0.44 million people from the previous year. Poverty is caused by several factors, one of which is the Human Development Index (HDI) and the Total Population level. Each region has different characteristics from other regions. These differences in characteristics cause more specific spatial effects, namely spatial heterogeneity. Geographically Weighted Regression (GWR) is a statistical method that can analyze spatial heterogeneity by assigning different weights and models to each observation location. This study aims to determine whether the HDI variable and percentage of total population significantly impact the number of poor people in Central Java Province in 2020 without eliminating the spatial effect. There are three groupings of variables that affect the Number of Poor People for GWR with the Adaptive Kernel Bisquare weighting function and four groups for the Adaptive Kernel Tricube weighting function. The Key Performance Indicators (KPI) used are Mean , Akaike Information Criterion (AIC), Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Based on these KPIs, the GWR model with the Adaptive Kernel Bisquare weighting function provides better results when compared to the OLS model.
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References
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References
Badan Pusat Statistik, “Kemiskinan dan ketimpangagn,” 2021. [Online]. Available: https://www.bps.go.id/subject/23/kemiskinan-dan-ketimpangan.html
Badan Pusat Statistik Jawa Tengah, “Data dan informasi kemiskinan provinsi Jawa Tengah 2016-2020,” 2021.
R. Fitriani and A. Efendi, Ekonometrika Spasial Terapan dengan R. Malang: UB Press, 2019.
H. Yasin, B. Waryanto, and A. R. Hakim, Regresi Spasial: Aplikasi dengan R. Ponorogo: WADE Group, 2020.
R. E. Caraka and H. Yasin, Geographically weighted regression (GWR) sebuah pendekatan regresi geografis. MOBIUS, 2017.
D. N. Gujarati, Ekonomi dasar. Jakarta: Erlangga, 1999.
A. Efendi, N. W. S. Wardhani, R. Fitriani, and E. Sumarminingsih, Analisis regresi: teori dan aplikasi dengan R. Malang: UB Press, 2020.
N. Hanifah, N. Herrhyanto, and F. Agustina, “Penerapan metode weigthed least square untuk mengatasi heteroskedastisitas pada analisis regresi linear,” J. EurekaMatika, vol. 3, no. 1, pp. 105–114, 2015.
A. L. Andrietya, A. Pujiati, and A. Setyadharma, “Determinants of Poverty in Central Java Province 2013-2018,” J. Econ. Educ., vol. 9, no. 1, pp. 81–88, 2020, doi: 10.15294/jeec.v9i1.38671.
S. Haryanto and G. A. Andriani, “Pemodelan jumlah penduduk miskin di Jawa Tengah menggunakan Geographically Weighted Regression (GWR),” J. Litbang Sukowati Media Penelit. dan Pengemb., vol. 4, no. 2, p. 10, 2019, doi: 10.32630/sukowati.v4i2.122.
Suyono, Analisis regresi untuk penelitian. Yogyakarta: Deepublish, 2018.
E. Dwiningsih, “Seemingly Unrelated Regression (SUR),” Universitas Negeri Yogyakarta, 2012.
N. Sari, H. Yasin, and A. Prahutama, “Geographically weighted regression principal component analysis (GWRPCA) Pada Pemodelan Pendapatan Asli Daerah Di Jawa Tengah,” J. Gaussian, vol. 5, no. 4, pp. 717–726, 2016.
M. Vebiriyana, M. Y. Darsyah, and I. M. Nur, “Pemodelan geographically weighted regression dengan fungsi kernel bisquare terhadap faktor-faktor yang mempengaruhi tingkat kemiskinan di kabupaten Demak,” Statistika, vol. Vol 3, no. 1, pp. 34–39, 2015.
N. Lutfiani and S. Mariani, “Pemodelan geographically weighted regression ( GWR ),” UNNES J. Math., vol. 5, no. 1, pp. 82–91, 2017.
W. Jetz, C. Rahbek, and J. W. Lichstein, “Local and global approaches to spatial data analysis in ecology,” Glob. Ecol. Biogeogr., vol. 14, no. 1, pp. 97–98, 2005, doi: 10.1111/j.1466-822X.2004.00129.x.
A. S. Fotheringham, C. Brunsdon, and M. Charlton, “Geographically weighted regression: the analysis of spatially varying relationships,” vol. 3, no. 2. John Wiley & Sons Ltd, pp. 54–67, 2002.
T. Wuryandari, A. Hoyyi, D. S. Kusumawardani, and D. Rahmawati, “Identifikasi autokorelasi spasial pada jumlah pengangguran di Jawa Tengah menggunakan indeks moran,” Media Stat., vol. 7, no. 1, pp. 1–10, 2014, doi: 10.14710/medstat.7.1.1-10.
D. S. Ningtyas, “Pemodelan Geographically Weighted Regression (GWR) dengan fungsi pembobot adaptive gaussian kernel, adaptive bisquare kernel dan adaptive tricube kernel,” 2019.
W. Nurpadilah, I. M. Sumertajaya, and M. N. Aidi, “Geographically Weighted Regression with Kernel Weighted Function on Poverty Cases in West Java Province: Regresi Terboboti Geografis dengan Fungsi Pembobot Kernel pada Data Kemiskinan di Provinsi Jawa Barat,” Indones. J. Stat. Its Appl., vol. 5, no. 1, pp. 173–181, 2021, doi: 10.29244/ijsa.v5i1p173-181.
T. Hendrawati, A. H. Wigena, I. M. Sumertajaya, and B. Sartono, “Performance Evaluation of AIC and BIC in Time Series Clustering with Piccolo Method,” in The 1st International Conference on Statistics and Analytics, 2020, no. January, doi: 10.4108/eai.2-8-2019.2290340.
C. Sammut and G. I. Webb, Eds., “Mean Absolute Error,” in Encyclopedia of Machine Learning, Boston, MA: Springer US, 2010, p. 652.
C. Sammut and G. I. Webb, Eds., “Mean Squared Error,” in Encyclopedia of Machine Learning, Boston, MA: Springer US, 2010, p. 653.
P. M. Swamidass, Ed., “MAPE (mean absolute percentage error)MEAN ABSOLUTE PERCENTAGE ERROR (MAPE),” in Encyclopedia of Production and Manufacturing Management, Boston, MA: Springer US, 2000, p. 462.