NONPARAMETRIC TRUNCATED SPLINE REGRESSION MODELING ON POVERTY RATES IN NORTH SUMATRA

Hasrat Ifolala Zebua

Abstract

This study aims to investigate the relationship between the Human Development Index (HDI) and poverty levels in North Sumatra. Using data from Central Bureau of Statistics (BPS), the study employs a nonparametric truncated spline regression model to analyze the relationship. The findings reveal that HDI significantly impacts poverty levels, with higher HDI associated with lower poverty rates. The model used in this study offers a robust approach to understanding the dynamics between HDI and poverty, and the results underscore the importance of improving HDI to reduce poverty. The research highlights an R-Squared value of 82.35%, indicating a strong correlation between HDI and poverty in the region.

Keywords

Poverty Rate; HDI; Trunated Spline; GCV

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References

Ardiansyah. (2019). Pemodelan Faktor yang Mempengaruhi Kemiskinan di Provinsi Sulawesi Selatan dengan Regresi Nonparametri Spline [Universitas Islam Negeri Alauddin Makassar]. https://repositori.uin-alauddin.ac.id/14909/

Ardianto, T. (2016). Analisis Faktor-Faktor Yang Mempengaruhi Tingkat Kemiskinan Kabupaten/Kota di Provinsi Jawa Timur Tahun 2009-2013. Jurnal Ilmiah Mahasiswa FEB, 4(2). https://jimfeb.ub.ac.id/index.php/jimfeb/article/view/2932

Badan Pusat Statistik. (2021). Indeks Pembangunan Manusia 2020. Jakarta: Badan Pusat Statistik. https://www.bps.go.id/id/publication/2021/04/30/8e777ce2d7570ced44197a37/indeks-pembangunan-manusia-2020.html

Badan Pusat Statistik Provinsi Sumatera Utara. (2021). Indikator Kesejahteraan Rakyat Provinsi Sumatera Utara 2020. Medan: Badan Pusat Statistik Provinsi Sumatera Utara. https://sumut.bps.go.id/id/publication/2021/01/29/eca629e4d550758eedff416c/indikator-kesejahteraan-rakyat-provinsi-sumatera-utara-2020.html

Balai Diklat Keuangan Makassar. (2021). Menuju Desa Mandiri dan Sejahtera: Dari Mana Dimulai? Kementerian Keuangan: Badan Pendidikan Dan Pelatihan Keuangan. https://bppk.kemenkeu.go.id/balai-diklat-keuangan-makassar/artikel/menuju-desa-mandiri-dan-sejahtera-dari-mana-dimulai-014554

Budiantara. (2011). Penelitian Bidang Regresi Spline Menuju Terwujudnya Penelitian Statistika yang Mandiri dan Berkarakter. PROSIDING SEMINAR NASIONAL MIPA UNDIKSHA 2011. https://ejournal.undiksha.ac.id/index.php/semnasmipa/article/view/2736

Craven, P., & Wahba, G. (1978). Smoothing Noisy Data with Spline Functions: Estimating the Correct Degree of Smoothing by The Method of Generalized Cross Validation. Numerische Mathematik, 31, 377–403. https://doi.org/10.1007/BF01404567

Hardle, W. (1990). Applied Nonparametric Regression. Cambridge: Cambridge University Press.

Murdiyana, M., & Mulyana, M. (2017). Analisis Kebijakan Pengentasan Kemiskinan di Indonesia. Jurnal Politik Pemerintahan, 10(1), 73–96. https://doi.org/10.33701/jppdp.v10i1.384

Sari, J. (2019). Analisis Tingkat Kemiskinan Masyarakat Di Provinsi Sumatera Utara. Al-Masharif: Jurnal Ilmu Ekonomi Dan Kesilaman, 7(2), 290–304. https://jurnal.uinsyahada.ac.id/index.php/Al-masharif/article/view/2190

Sugiyono. (2017). Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Bandung: Alfabeta.

Tripena, A. (2011). Penentuan Model Regresi Spline Terbaik. PROSIDING SEMINAR NASIONAL STATISTIKA UNIVERSITAS DIPONEGORO 2011, 92–102. http://eprints.undip.ac.id/33908/

Wahba, G. (1990). Spline Models for Observational Data. Madison: University of Wisconsin-Madison.

Wiguna, A. D. A., & Iskandar, D. D. (2021). Analisis Pengaruh PDB Perkapita, Pengeluaran Pemerintah dan Pengangguran Terhadap IPM di Negara-Negara ASEAN Tahun 2009-2018. Diponegoro Journal of Economics, 10(3), 148–161. https://ejournal3.undip.ac.id/index.php/jme/article/view/39683

Zuhdiyaty, N., & Kaluge, D. (2017). Analisis Faktor - Faktor Yang Mempengaruhi Kemiskinan di Indonesia Selama Lima Tahun Terakhir. Jurnal Ilmiah Dan Ekonomi Asia, 11(2), 27–31. https://doi.org/10.32812/jibeka.v11i2.42.

DOI: https://doi.org/10.20961/jiep.v23i1.48665

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