Main Article Content

Abstract

The first goal of the SDGs is to end poverty in any form. The COVID-19 pandemic has greatly affected several economic indicators, especially absolute poverty, especially in Sulawesi Island, which has increased poverty indicators, leading to the movement of values between districts/cities.  The grouping will show similar characteristics of absolute variable poverty. By the Fuzzy method clustering, each observation has a degree of membership so that from the degree of membership can be identified which areas have vulnerable to move from one cluster to another. Grouping using fuzzy algorithms will get an overview of districts of concern to the government during the pandemic so that the variable indicators of absolute poverty do not worsen due to the pandemic. Comparison with the absolute variables of poverty in 2019 and 2020 in the headcount index (P0), Poverty Gap Index (P1), and Poverty Severity Index (P2) in districts/cities on the island of Sulawesi based on silhouette coefficients shows that optimum clusters formed as many as 2 clusters, with a coefficient of 0.57 and 0.60 respectively. Cluster 1 has characteristics including areas with absolute poverty rates that tend to be more prosperous than cluster 2 in the 2019 and 2020 data groups on the island of Sulawesi. The fuzzy algorithm detects areas prone to displacement from cluster 1 to cluster 2, namely Bombana, Bone, Sangihe Islands, South Konawe, and Siau Tagulandang Biaro in 2019 and Bombana, Bone, Sangihe, and Maros Islands in 2020. The COVID-19 pandemic in March 2020 has not had much impact on the macro indicators of poverty seen in the transfer of membership from 2019 to 2020, which only occurred to 3 districts that changed, namely bolaang mongondouw and konawe selatan from cluster 1 to cluster 2 and Maros from cluster 2 to cluster 1.

Keywords

Absolute Poverty COVID-19 Fuzzy Clustering Silhouette Coefficient Sulawesi

Article Details

How to Cite
Novidianto, R., & Irfani, R. (2021). Fuzzy Clustering Algorithm to Catching Pattern of Change in District/City Poverty Variables Before and The Beginning of The Covid-19 Pandemic in Sulawesi Island. Parameter: Journal of Statistics, 1(2), 1-10. https://doi.org/10.22487/27765660.2021.v1.i2.15446

References

  1. Abbasi, S., Nejatian, S., Parvin, H., Rezaie, V., & Bagherifard, K. (2019). Clustering ensemble selection considering quality and diversity. Artificial Intelligence Review, 52(2), 1311–1340.
  2. Abdy, M. (2018). Pengklasteran dengan Algoritma Fuzzy C-Means. Jurnal Matematika, Statistika Dan Komputasi, 12(1), 30–35.
  3. Ansari, Z., Azeem, M. F., Ahmed, W., & Babu, A. V. (2015). Quantitative evaluation of performance and validity indices for clustering the web navigational sessions. ArXiv Preprint ArXiv:1507.03340.
  4. Badan Pusat Statistik. (2020a). Penghitungan dan Analisis Kemiskinan Makro di Indonesia Tahun 2020. BPS.
  5. Badan Pusat Statistik. (2020b). Persentase Penduduk Miskin Maret 2020. Berita Resmi Statistik.
  6. Bagherinia, A., Minaei-Bidgoli, B., Hosseinzadeh, M., & Parvin, H. (2020). Reliability-based fuzzy clustering ensemble. Fuzzy Sets and Systems.
  7. Bagherinia, A., Minaei-Bidgoli, B., Hossinzadeh, M., & Parvin, H. (2019). Elite fuzzy clustering ensemble based on clustering diversity and quality measures. Applied Intelligence, 49(5), 1724–1747.
  8. Döring, C., Lesot, M.-J., & Kruse, R. (2006). Data analysis with fuzzy clustering methods. Computational Statistics & Data Analysis, 51(1), 192–214.
  9. Foster, J. E. (1998). Absolute versus relative poverty. The American Economic Review, 88(2), 335–341.
  10. Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis (Vol. 344). John Wiley & Sons.
  11. Maipita, I. (2013). Memahami dan Mengukur Kemiskinan. Absolute Media.
  12. Mattjik, A. A., Sumertajaya, I., Wibawa, G. N. A., & Hadi, A. F. (2011). Sidik peubah ganda dengan menggunakan SAS.
  13. McKibbin, W., & Fernando, R. (2020). The economic impact of COVID-19. Economics in the Time of COVID-19, 45.
  14. Mojarad, M., Nejatian, S., Parvin, H., & Mohammadpoor, M. (2019). A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters. Applied Intelligence, 49(7), 2567–2581.
  15. Nejatian, S., Parvin, H., & Faraji, E. (2018). Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification. Neurocomputing, 276, 55–66.
  16. Nurwati, N. (2008). Kemiskinan: Model Pengukuran, Permasalahan dan Alternatif Kebijakan. Jurnal Kependudukan Padjadjaran, 10(1), 1.
  17. Rao, S. G., & Govardhan, A. (2015). Performance validation of the modified k-means clustering algorithm clusters data. International Journal of Scientific & Engineering Research, 6(10), 726–730.
  18. Rashidi, F., Nejatian, S., Parvin, H., & Rezaie, V. (2019). Diversity-based cluster weighting in cluster ensemble: an information theory approach. Artificial Intelligence Review, 52(2), 1341–1368.
  19. Ravallion, M., & Chen, S. (2007). Absolute poverty measures for the developing world, 1981-2004. The World Bank.
  20. Romero, J. C., Linares, P., & López, X. (2018). The policy implications of energy poverty indicators. Energy Policy, 115, 98–108.
  21. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
  22. World Bank. (2020). Poverty. https://www.worldbank.org/en/topic/poverty/overview