DOI: https://doi.org/10.14710/medstat.15.2.220-230

APPLICATION OF BIPLOT ANALYSIS WITH ROBUST SINGULAR VALUE DECOMPOSITION TO POVERTY DATA IN SULAWESI ISLAND

*Febriyana Taki  -  Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119, Indonesia
Lailany Yahya  -  Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119, Indonesia
Muhammad Rezky Friesta Payu  -  Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119, Indonesia
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Poverty is defined as an inability of the individual to meet basic needs for a decent life. According to BPS data in
2020, Sulawesi Island ranks fifth as the poorest island in Indonesia. This study aims to find out the mapping of areas and indicators of poverty in Sulawesi Island using Biplot Analysis with Robust Singular Value Decomposition approach for outlier research data. Based on the results of the study, there are five objects that are outlier and the information provided by the biplot amounted 98.45%. District/city that have similar characteristics are divided into 4 groups. The indicator of poverty that has the most diversity is the School Old Expectations Numbers (Var 4) and the one with the least diversity is Poor Households Using Clean Water (Var 8). Indicators of poverty that are positively correlated are Literacy Numbers (Var 1) and Non-Working Poor Population (Var 5), while the negative correlated are The Non-Working Poor Population (Var 5) and Poor Households Using Clean Water (Var 8). There are 19 districts/cities that have literacy values above the average of all districts/cities and 11 districts/cities that have a per capita expenditure value below the average of all districts/cities.

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Keywords: Poverty; Biplot Analysis; Robust Singular Value Decomposition; Outlier

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Language : EN
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  1. Abdillah, J., Wiyono, V. H., & Samudro, B. R. (2019). Analisis Pola Konsumsi dan Kemiskinan di Jawa Tengah. Research Fair Unisri, 3(1), 132–138
  2. Acuna, E. & Rodrigues, C. (2004). The Treatment of Missing Value and its Effect is the Classifier Accuracy. Proceedings of the Meeting of the International Federation of Classification Societies (IFCS)
  3. Badan Pusat Statistik. (2020). Data dan Informasi Kemiskinan Kabupaten/Kota Tahun 2020. Jakarta: Badan Pusat Statistik
  4. Bappenas, K. P. (2018). Analisis Wilayah dengan Kemiskinan Tinggi. Kedeputian Bidang Kependudukan dan Ketenagakerjaan Kementerian PPN/Bappenas
  5. Fitria, H., D., & A., Y. (2013). Analisis Principal Component Biplot pada Bank Umum Persero yang Beroperasi di Jawa Tengah. Seminar Nasional Statistika 2013, 147–160
  6. Fitriana, A. R., Rusyana, A., & Wisreini. (2018). Analisis Biplot untuk Mengetahui Kebutuhan terhadap Lulusan Program Studi Statistika. Jurnal Matematika Statistika dan Komputasi, 8(1), 39–51
  7. Gabriel, K. (1971). The Biplot Graphic Display of Matrices With Application to Principal Analysis. Biometrika. 58(3), 4533–467
  8. Hawkins, D. ., Liu, L., & Young, S. . (2001). Robust Singular Value Decomposition, National Institute of Statistical Science. National Institute of Statistical Science, 122, 1–12
  9. Jolliffe, I. (2002). Principal Component Analysis. New York: Springer-Verlag
  10. Jolliffe, I. (2010). Principal Component Analysis, Second Edition. New York: Springer
  11. Leleury, Z. A. & Wokanubun, A. E. (2015). Analisis Biplot Pada Pemetaan Karakteristik Kemiskinan Di Provinsi Maluku. Barekeng: Jurnal Ilmu Matematika Dan Terapan, 9(1), 21–31
  12. Mattjik, A. A., & Sumertajaya, I. M. (2011). Peubah Ganda dengan Menggunakan SAS. Bogor: IPB Press
  13. Meirina, R., Solimun, & Handoyo, S. (2013). Penerapan Analisis Biplot Robust Singular Value Decomposition (RSVD) Pada Data Indikator Pendidikan di Provinsi Jawa Timur. Jurnal Mahasiswa Statistik, 1(3)
  14. Nugroho, S. (2008). Statistika Multivariat Terapan. Bengkulu: UNIB Press
  15. Seran, S. (2017). Hubungan antara Pendidikan, Pengangguran, dan Pertumbuhan Ekonomi dengan Kemiskinan. Jurnal Ekonomi Kuantitatif Terapan, 10(1), 59–71
  16. Srinadi, I. G. A. M., & Sumarjaya, I. W. (2016). Analisis Biplot untuk Pemetaan Posisi dan Karakteristik Usaha Pariwisata di Provinsi Bali. Jurnal Matematika, 6(1), 34–45
  17. Team, R. D. C. (2021). R Programming. R Development Core Team. https://www.r-project.org/
  18. Widowati, W., & Muzdalifah, L. (2017). Perbandingan Analisis Biplot Klasik dan Robust Biplot pada Pemetaan Perguruan Tinggi Swasta di Jawa Timur. Jurnal Riset Dan Aplikasi Matematika (JRAM), 1(1), 27–39. https://doi.org/10.26740/jram.v1n1.p2739

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