Analisis Biplot Pada Pengelompokan Kecamatan Di Kabupaten Tasikmalaya Berdasarkan Indikator Kemiskinan

Annisa Siti Utami, Anindya Apriliyanti Pravitasari, Irlandia Ginanjar

Abstract


Poverty is a social problem that continues to exist in people's lives according to Nurwati, 2008. Therefore, the problem of poverty is the center of attention of the Tasikmalaya Regency government. In the National Long-Term Development Plan (RPJPN) 2005-2025 the problem of poverty is seen in a multidimensional framework, therefore poverty is not only related to income measurement, but related to several things. This is because poverty is not only related to the size of income but involves several things. In the Tasikmalaya Regency Regional Medium-Term Development Plan (RPJMD), the target for achieving the poverty rate in 2021 is 10.23%. Based on BPS publications, there are 10.75% of the population of Tasikmalaya Regency who are categorized as poor, meaning that the Tasikmalaya Regency government's target has not been achieved. So it is necessary to make efforts to overcome the problem of poverty. This study aims to group sub-districts in Tasikmalaya Regency based on the similarity of poverty indicators owned by each sub-district by using biplot analysis. The data used is poverty indicator data for 39 sub-districts in Tasikmalaya Regency in 2021. From the research results it is known that the amount of variation that can be described is 97%, meaning that the plots formed can best describe actual conditions. data information. In addition, three clusters have the same poverty indicators. Cluster 1 contains sub districts that have an indicator in the form of a high student to school ratio in SMA/SMK/MA. Cluster 2 contains sub districts that have moderate to low indicators on all variables except the ratio of SMP/MTs students and the ratio of SMA/SMK/MA students. Meanwhile, Cluster 3 consists of sub-districts that have an indicator in the form of a high ratio of SMP/MTs students.

Keywords


poverty, Sustainable Development Goals (SDGs), biplot analysis

Full Text:

PDF

References


N. Nurwati, “Kemiskinan : Model Pengukuran, Permasalahan dan Alternatif Kebijakan,” Jurnal Kependudukan Padjadjaran, vol. 10, no. 1, 2008.

Badan Pusat Statistik Jawa Barat, “Jumlah Penduduk Miskin (Ribu Jiwa), 2019-2021.” Badan Pusat Statistik Provinsi Jawa Barat. [Online]. Available: https://jabar.bps.go.id/indicator/23/83/1/jumlah-penduduk-miskin.html

UNDP, “The 2020 Global Multidimensional Poverty Index (MPI),” The Oxford Poverty and Human Development Initiative (OPHI), 2020.

I. T. Jolliffe, Principal component analysis, 2nd ed. in Springer series in statistics. New York: Springer, 2002.

A. A. Mattjik and I. M. Sumertajaya, “Sidik Peubah Ganda dengan Menggunakan SAS,” 2011.

I. Ginanjar, U. S. Pasaribu, and S. W. Indratno, “A measure for objects clustering in principal component analysis biplot: A case study in inter-city buses maintenance cost data,” presented at the STATISTICS AND ITS APPLICATIONS: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II), 2016, Jawa Barat, Indonesia, 2017, p. 020016. doi: 10.1063/1.4979432.

S. Sharma, Applied multivariate techniques. New York: J. Wiley, 1996.

T. Purwandari, I. Ginanjar, and D. D. Dewi, “Multiple Correspondence Analysis for Identifying the Contribution of Infant Mortality Indicator Categories,” J. Phys.: Conf. Ser., vol. 1776, no. 1, p. 012064, Feb. 2021, doi: 10.1088/1742-6596/1776/1/012064.

A. C. Rencher and W. F. Christensen, Methods of multivariate analysis, Third Edition. in Wiley series in probability and statistics. Hoboken, New Jersey: Wiley, 2012.

B. S. Everitt, S. Landau, M. Leese, and D. Stahl, Cluster Analysis, 1st ed. in Wiley Series in Probability and Statistics. Wiley, 2011. doi: 10.1002/9780470977811.

J. Han and M. Kamber, Data mining: concepts and techniques, 3rd ed. Burlington, MA: Elsevier, 2012.

J. F. Hair, Ed., Multivariate data analysis, 7th ed. Upper Saddle River, NJ: Prentice Hall, 2010.

S.-C. Chu, J. Roddick, and J.-S. Pan, “Efficient k-medoids algorithms using multi-centroids with multi-runs sampling scheme,” presented at the The Sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2002.

W. As, M. K. Aidid, and M. Nusrang, “Pengelompokan Kabupaten/Kota Provinsi Sulawesi Selatan dan Barat Berdasarkan Angka Partisipasi Pendidikan SMA/SMK/MA Menggunakan K-Medoid dan CLARA,” j. variansi, vol. 1, no. 3, p. 48, Dec. 2019, doi: 10.35580/variansiunm12899.

A. T. Rahman, “Coal Trade Data Clustering Using K-Means (Case Study Pt. Global Bangkit Utama),” 2017.




DOI: http://dx.doi.org/10.12962/j27213862.v1i1.19128

Refbacks

  • There are currently no refbacks.




Creative Commons License
Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.

ISSN:  0216-308X

e-ISSN: 2721-3862

Web
Analytics Made Easy - StatCounter View My Stats