Regresi Logistik Biner dalam Penentuan Ketepatan Klasifikasi Tingkat Kedalaman Kemiskinan Provinsi-Provinsi di Indonesia

Ni Putu Nanik Hendayanti; Maulida Nurhidayati

doi:10.31958/js.v12i2.2483

Regresi Logistik Biner dalam Penentuan Ketepatan Klasifikasi Tingkat Kedalaman Kemiskinan Provinsi-Provinsi di Indonesia

Ni Putu Nanik Hendayanti, Maulida Nurhidayati

Abstract

This study aims to find out the variables that affect the poverty depth index of provinces in Indonesia as well as the level of classification produced by logistic regression analysis method. This analysis was chosen because the variables tied to this study had values of 0 and 1 (categorically). The variable tied to this research is the level of poverty depth of provinces in Indonesia in 2019. While the free variables are PPP (adjusted per capita expenditure), MYS (Mean Years School) and EYS (Expected Years of Schooling) of provinces in Indonesia in 2019. The population used in this study is all provinces in Indonesia with sampling techniques is census because the population is less than 100. The samples in this study are the same as the population of all provinces in Indonesia. The results showed that MYS and EYS influenced the level of poverty depth with the accuracy of the overall prediction is 85.3% which indicates that there are still differences in classification results obtained from the original data with the logit regression model obtained.

Keywords

Classification; Poverty; Logistic Regression; Depth of Poverty.

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References

Cahyat, A. (2004). Bagaimana kemiskinan diukur?: Beberapa model pengukuran kemiskinan di Indonesia. Center for International Forestry Research (CIFOR), 2 November 2004. https://doi.org/10.17528/cifor/001641

Darma, D. C., Purwadi, & Wijayanti, T. C. (2020). Ekonomika Gizi: Dimensi Baru di Indonesia. Jakarta: Yayasan Kita Menulis.

Ferezagia, D. V. (2018). Analisis Tingkat Kemiskinan di Indonesia. Jurnal Sosial Humaniora Terapan, 1(1), 1–6.

Gudono. (2017). Analisis Data Multivariat. Yogyakarta: BPFE.

Parhusip, H. A., & Pertiwi, A. T. (2014). Studi Tingkat Kemiskinan di Indonesia dengan Analisa DisKriminan ECM Dan Metode Fisher. Prosiding Seminar Nasional Matematika dan Pendidikan Matematika 2014, 5(1), 333–346.

Rusdarti, R., & Sebayang, L. K. (2013). Faktor-Faktor Yang Mempengaruhi Tingkat Kemiskinan Di Provinsi Jawa Tengah. Jurnal Economi, 9(1), 1–9.

Samsudin, H., Sadiman, & Pachrozi, I. (2019). Kajian Sosial: Menuju Kemiskinan Satu Digit. Banyuasin: Bappeda Litbang Kabupaten Banyuasin.

Samuelson, P. A., & Nordhaus, W. D. (2004). Ilmu Makroekonomi. Jakarta: Media Global Edukasi.

Suhartono, E. (2009). Pekerjaan Sosial di Dunia Industri. Bandung: Alfabeta.

Tambun, J. M. S., & Bangun, R. (2018). Pemodelan Faktor-Faktor yang Mempengaruhi Indeks Kedalaman Kemiskinan dan Indeks Keparahan Kemiskinan Kabupaten/Kota di Sumatera Utara Menggunakan Regresi Data Panel. Jurnal Administrasi Publik, 6(1), 100–110.

Widarjono, A. (2015). Analisis Multivariat Terapan Dengan Program SPSS, AMOS, dan SMARTPLS (II). Yogyakarta: UPP STIM YKPN.

Yogo, Pradono, & Aritenang, A. (2018). Pengantar Ekonomika Wilayah: Pendekatan Analisis Praktis. Bandung: ITB.

DOI: http://dx.doi.org/10.31958/js.v12i2.2483