Model tingkat kemiskinan Provinsi Jawa Timur dengan analisis regresi spasial

Authors

  • Safa'at Yulianto Akademi Statistika (AIS) Muhammadiyah Semarang
  • Cika Awani Ayuwida Akademi Statistika (AIS) Muhammadiyah Semarang

Keywords:

proverty, spatial regression, spatial error model

Abstract

Poverty is a major problem in the developing countries, as in Indonesia. In 2008, East Java Province was one of Indonesia's richest provinces, but it also occupied third place with a high number and percentage of poor people. The poverty figures in East Java Province have been declining before, and 2008 is the lowest seen figure of both size and percentage. The poverty of an area is affected by the poverty of its surroundings. So in the analyzing data containing spatial effects or regional aspects, spatial regression was used. The best Spatial Regression Model used is the Spatial Error Model (SEM). The variable used in this study is the poverty rate in the East Java Province as variable (Y), with nine X variables: namely the head of the woman's household (X1), the number of children not in school 7-18 years (X2), the number of individuals with disabilities (X3), the number of individuals with chronic disease (X4), the number of individuals not working (X5), the unprotected drinking water sources (X6),  defecating without latrines (X7), non- electrical lighting (X8), the fuel cook using oil and wood (X9). The weighting matrix used is the Queen Contiguity Matrix. The results of the modeling using SEM and the factors that affect poverty are the number of individuals with disabilities (X3) and the unprotected drinking water sources (X6).

References

Amelia, M. (2012). Penerapan Regresi Spasial untuk Data Kemiskinan Kabupaten di Pulau Jawa [Skripsi]. Bogor: Departemen Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor.

Anselin, L. (1998). Spatial Econometrics: Methods and Models. London: Kluwer Academic Publisher.

BPS (Badan Pusat Statistik). (2014). Perkembangan Pembangunan Provinsi Jawa Timur 2014. BPS Jawa Timur

Bappenas. (2014). Memantapkan Perekonomian Nasional bagi Peningkatan Kesejahteraan Rakyat yang Berkeadilan. Jakarta: Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional.

Cressie, N. (1991). Statistics for Spasial Data. New York: Wiley.

Kadji, Y. (2013). Kemiskinan dan Konsep Teoritisnya. Http://repository.ung.ac.id/hasilriset/show//318/kemiskinan-dan-konsep-teoritisnya.html.

Prastyo, A. A. (2010). Analisis Faktor-faktor yang Mempengaruhi Tingkat Kemiskinan. Semarang: Fakultas Ekonomi, Universitas Diponegoro.

Putri, A. M. (2014). Faktor-Faktor yang Mempengaruhi Tingkat Kemiskinan di Provinsi Jawa Timur Tahun 2008-2012. Jurnal Ekonomi Pembangunan, 1-9.

Ruswanto, B., Nurjazuli, & Raharjo, M. (2012). Analisis Spasial Sebaran Kasus Tuberkulosis Paru Ditinjau Dari Faktor Lingkungan Dalam dan Luar Rumah di Kabupaten Pekalongan. Kesehatan Lingkungan Indonesia, 22-28.

Sari, D. M., Kusrini, D. E., & Suhartono. (2013). Pemodelan Kasus Tindak Pidana di Kota Surabaya dengan Pendekatan Regresi Spasial. Jurnal Sains dan Seni Pomits, 135-140.

Yulianto, S., Djuraidah, A., & Wigena, A. H. (2011). Model Otoregresif Simultan Bayes untuk Analisis Data Kemiskinan. Prosiding Seminar Nasional Statistika (pp. 406-413). Bandung: Universitas Padjadjaran.

Yulianto, S., Fadhilah, O. N., (2020). Pemodelan Regresi Spasial pada Tingkat Kemiskinan di Provinsi Jawa Barat. Prosiding Seminar Nasional Matematika dan Pendidikan Matematika (pp. 185-193). Semarang: Universitas PGRI Semarang.

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Published

2021-08-22

How to Cite

Yulianto, S., & Ayuwida, C. A. (2021). Model tingkat kemiskinan Provinsi Jawa Timur dengan analisis regresi spasial. Prosiding Seminar Nasional Matematika Dan Pendidikan Matematika, 6, 121–127. Retrieved from http://conference.upgris.ac.id/index.php/senatik/article/view/1833

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