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Abstract
The economy is a benchmark to determine the extent of the development of a country. Indonesia, which is now a developing country, is ranked 5th as the poorest country in Southeast Asia. Of course, the government must pay attention because until now, poverty has become one of Indonesia's main problems. Ending poverty everywhere and in all its forms is goal 01 of the Sustainable Development Goals (SDGs) program. One of the efforts that can be done is by planning as part of the implementation of the target, namely eliminating poverty and appropriate social protection for all levels of society so that the SDGs are achieved. Therefore, it is important to do a spatial analysis by making a model of poverty estimation in Indonesia and grouping to identify areas in Indonesia that have the highest poverty mission. The clustering method used in this grouping is Self Organizing Map (SOM). In this study, Spatial Autoregressive (SAR) analysis was used to create a predictive model. This is because poverty is very likely to have a spatial influence or be influenced by location to other areas in the vicinity. The results of the SAR model that can be formed are . Furthermore, the region with the highest mission is grouped using the Self Organizing Map (SOM) clustering based on variables that significantly affect the amount of poverty in Indonesia. From the results of the analysis obtained four clusters, each of which has its characteristics to classify 34 provinces in Indonesia. The clusters formed include cluster 1 consisting of 17 provinces, cluster 2 consisting of 9 provinces, cluster 3 consisting of 1 province, and cluster 4 consisting of 7 provinces.
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References
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References
Rustam, "Perencanaan Pertumbuhan Ekonomi Jawa Timur Dalam Rangka Mengurangi Angka Pengangguran Dan Kemiskinan," p. 6(1), 2010.
N. d. Baiq, "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DI INDONESIA," media.neliti, p. 34, 2012.
bappenas, "Tujuan Pembangunan Berkelanjutan SDGs," [Online]. Available: http://sdgs.bappenas.go.id/tujuan-1/.
I. d. S. T. Raharjo, "SUSTAINABLE DEVELOPMENT GOALS (SDGs)DAN PENGENTASAN KEMISKINAN," http://jurnal.unpad.ac.id/share/article/view/13198/6032, p. 159.
Septiana, N. I. (2015, 04 17). Analisis Kemiskinan di Provinsi Jawa Tengah Menggunakan Metode Regresi Spasial. Dspace. Retrieved from https://dspace.uii.ac.id/handle/123456789/6715
Lazumi, F. (2018, 01 25). PENGELOMPOKAN KABUPATEN/KOTA BERDASARKAN KARAKTERISTIK KEMISKINAN DI PROVINSI NUSA TENGGARA TIMUR MENGGUNAKAN ALGORITMA SELF ORGANIZING MAPS (SOM). Retrieved from Dspace: https://dspace.uii.ac.id/handle/123456789/5621
Sugiyono. (2007). Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung: Alfabeta.
Kuswanto, D. (2012). Statistik Untuk Pemula dan Orang Awam. Jakarta: Laskar Aksara.
Anselin, L. (1999). Spatial Econometrics: Methods and Models. Dordrecht: Academic Publishers.
Sugiarto, E., & Kusmayadi. (2000). Metodologi Penelitian dalam Bidang Kepariwisataan. Jakarta: Gramedia Pustaka.
Anselin, L. (1999). Spatial Econometrics: Methods and Models. Dordrecht: Academic Publishers.
Guthikonda. (2005). ohonen Self Organizing Maps. Retrieved from http://www.shy.am/wp-content/uploads/2009/01/kohonen-self-organizing-maps-shyam-guthikonda.pdf