Clustering Methods in Grouping Rural Destinations in West Java Province Based on Regional Vulnerability Indicators to the Impact of Hydrometeorological Disasters in 2021

Hanifah Vida Indrasari; Yuliagnis Transver Wijaya

Authors

Hanifah Vida Indrasari BPS Morotai Island
Yuliagnis Transver Wijaya STIS Jakarta

Keywords:

Cluster analysis, Hard clustering, Natural disasters, Regional vulnerability, Soft clustering

Abstract

Indonesia is an archipelagic country with a high incidence of hydrometeorological disasters, and this incidence is increasing annually. One of the provinces in Indonesia with the highest number of hydrometeorological disasters is West Java Province, where 98.97 per cent of the disasters are hydrometeorological. The area's characteristics also support this: it is dominated by mountains, experiences high rainfall, has 40 watersheds, and contains six faults suspected to remain active, making it vulnerable to hydrometeorological disasters. Research on regional vulnerability to hydrometeorological disasters can be conducted by clustering regions into groups with similar vulnerability levels. The purpose of this study was to group regencies or cities in West Java Province based on indicators of regional vulnerability to hydrometeorological disasters in 2021. The clustering methods used are hard clustering (single linkage, complete linkage, average linkage, Ward's method, and k-means) and soft clustering (Fuzzy C-Means). The optimal method for grouping regencies or cities in West Java Province is complete linkage, yielding 4 clusters. The result is that all clusters are vulnerable to social vulnerability.

References

Balasko, B., Abonyi, J., & Feil, B. (2005). Fuzzy Clustering and Data Analysis Toolbox For Use with Matlab. Veszprem: Department of Process Engineering University of Veszprem.

Barone, G., & Mocetti, S. (2014). Natural disasters, growth and institutions: A tale of two earthquakes. Journal of Urban Economics, 84, 52–66. https://doi.org/10.1016/j.jue.2014.09.002

Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The Fuzzy C-Means Clustering Algorithm. Computers & Geosciences, 10(2–3), 191–203. https://doi.org/10.1109/igarss.1988.569600

BNPB. (2016). Risiko bencana indonesia.

BNPB. (2021). IRBI Indeks Risiko Bencana Indonesia Tahun 2021.

BNPB, & Bappenas. (2018). RENCANA INDUK PENANGGULANGAN BENCANA 2015-2045.

BPBD Jawa Barat. (2021). Rekapitulasi Kejadian Bencana di Wilayah Provinsi Jawa Barat.

BPS Jabar. (2022). STATISTIK DAERAH PROVINSI JAWA BARAT 2022.

Clare, L., & Weninger, B. P. (2011). Social and biophysical vulnerability of prehistoric societies to Rapid Climate Change. Documenta Praehistorica, 37(1), 283–292. https://doi.org/10.4312/dp.37.24

Cutter, S. L. (1996). Vulnerability to hazards. Progress in Human Geography, 20(4), 529–539.

Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social Vulnerability to Environmental Hazards. 84(2).

Cutter, S. L., & Finch, C. (2008). Temporal and spatial changes in social vulnerability to natural hazards. Planning for Climate Change: A Reader in Green Infrastructure and Sustainable Design for Resilient Cities, 105(7), 2301–2306. https://doi.org/10.4324/9781351201117-16

Cutter, S. L., Mitchell, J. T., & Scott, M. S. (2000). Revealing the Vulnerability of People and Places : A Case Study of Georgetown County, South Carolina. 90(4), 713–737.

Dintwa, K. F., Letamo, G., & Navaneetham, K. (2019). Measuring social vulnerability to natural hazards at the district level in Botswana. Jamba: Journal of Disaster Risk Studies, 11(1), 1–11. https://doi.org/10.4102/JAMBA.V11I1.447

Djuraidah, A. (2009). Indeks Kerentanan Sosial Ekonomi untuk Bencana Alam di Indonesia. Prosiding Seminar Nasional Matematika Dan Pendidikan Matematika.

Habibi, M. (2013). MODEL SPASIAL KERENTANAN SOSIAL EKONOMI DAN KELEMBAGAAN TERHADAP BENCANA GUNUNG MERAPI (" Spatial Model of Social Economic and Institutional Vulnerability Of Merapi Disaster ") Pendahuluan Indonesia mempunyai karakteristik bencana yang kompleks , karena t. 2(1), 1–10.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis (7th ed.).

Han, J., Pei, J., & Tong, H. (2022). Data Mining: Concepts and Techniques (Fourth). Elsevier Science. https://books.google.co.id/books?hl=id&lr=&id=NR1oEAAAQBAJ&oi=fnd&pg=PP1&dq=Han,+J.,+%26+Kamber,+M.+(2006).+Data+Mining:+Concepts+and+Techniques,+Second+Edition.+San+Francisco:+Elsevier,+Inc&ots=_M9GNJsct-&sig=ksWO0XJnwK4adMvMPf8ImJnBiy4&redir_esc=y#v=one

Hanniva, Kurnia, A., Rahardiantoro, S., & Mattjik, A. A. (2022). Penggerombolan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan Metode K-Means dan Fuzzy C-Means. Xplore: Journal of Statistics, 11(1), 36–47. https://doi.org/10.29244/xplore.v11i1.855

Irmayani, S., Azhar, Z., & Adry, M. R. (2018). PENGARUH FAKTOR EKONOMI, SOSIAL EKONOMI DAN IKLIM TERHADAP BENCANA ALAM DI INDONESIA. Photosynthetica, 2(1), 1–13. http://link.springer.com/10.1007/978-3-319-76887-8%0Ahttp://link.springer.com/10.1007/978-3-319-93594-2%0Ahttp://dx.doi.org/10.1016/B978-0-12-409517-5.00007-3%0Ahttp://dx.doi.org/10.1016/j.jff.2015.06.018%0Ahttp://dx.doi.org/10.1038/s41559-019-0877-3%0Aht

Jeong, S., & Yoon, D. K. (2018). Examining vulnerability factors to natural disasters with a spatial autoregressive model: The case of south Korea. Sustainability (Switzerland), 10(5), 1–13. https://doi.org/10.3390/su10051651

Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.).

Maharani, Y. N., Nugroho, A. R. B., Adiba, D. F., & Sulistiyowati, I. (2020). Pengaruh Kerentanan Sosial Terhadap Ketangguhan Masyarakat dalam Menghadapi Bencana Erupsi Gunung Merapi di Kabupaten Sleman. Jurnal Dialog Penanggulangan Bencana, 11(1), 1–12. https://www.bnpb.go.id/jurnal/jurnal-dialog-penanggulangan-bencana-vol-11-no-1-tahun-2020

Mosby, K., Birch, T., Moles, A., & Cherry, K. E. (2021). Disasters. Handbook of Rural Aging, 111–115. https://doi.org/10.7591/9781501701498-008

Nugraha, A. L., Awaluddin, M., Sukmono, A., & Wakhidatus, N. (2022). Pemetaan Dan Penilaian Kerentanan Bencana Alam Di Kabupaten Jepara Berbasis Sistem Informasi Geografis. Geoid, 17(2), 185. https://doi.org/10.12962/j24423998.v17i2.9370

Pangestu, H. D., Putra, A. D., & Syah, A. (2021). Analisis Indeks Risiko dan Potensi Kebencanaan (Studi untuk Wilayah Kabupaten Lampung Tengah). Jurnal Rekayasa Sipil …, 9(3), 481–490. http://repository.lppm.unila.ac.id/id/eprint/36857

Peraturan Kepala Badan Nasional Penanggulangan Bencana Nomor 02 Tahun 2012 Tentang Pedoman Umum Pengkajian Risiko Bencana, Peraturan Kepala Badan Nasional Penanggulangan Bencana 1 (2012). https://www.bnpb.go.id/uploads/24/peraturan-kepala/2012/perka-2-tahun-2012-tentang-pedoman-umum-pengkajian-resiko-bencana.pdf

Pramana, S., Yuniarto, B., Mariyah, S., Santoso, I., & Nooraeni, R. (2018). Data mining dengan R Konsep Serta Implementasi. IN MEDIA.

Rabby, Y. W., Hossain, B., & Hasan, M. U. (2019). Social vulnerability in the coastal region of Bangladesh: An investigation of social vulnerability index and scalar change effects. International Journal of Disaster Risk Reduction, 101329. https://doi.org/10.1016/j.ijdrr.2019.101329

Ramadhan, A., Prawita, K., Izzudin, M. A., & Amandha, G. (2021). Analisis strategi dan klasterisasi ketahanan pangan nasional dalam menghadapi pandemi covid-19. Teknologi Pangan : Media Informasi Dan Komunikasi Ilmiah Teknologi Pertanian, 12(1), 110–122. https://doi.org/10.35891/tp.v12i1.2179

Rufat, S. (2013). Spectroscopy of Urban Vulnerability. Annals of the Association of American Geographers, 103(3), 505–525. https://doi.org/10.1080/00045608.2012.702485

Schmidtlein, M. C., Shafer, J. M., Berry, M., & Cutter, S. L. (2011). Modeled earthquake losses and social vulnerability in Charleston , South Carolina. Applied Geography, 31(1), 269–281. https://doi.org/10.1016/j.apgeog.2010.06.001

Schumacher, I., & Strobl, E. (2011). Economic development and losses due to natural disasters: The role of hazard exposure. Ecological Economics, 72, 97–105. https://doi.org/10.1016/j.ecolecon.2011.09.002

Siagian, T. H., & Parwanto, N. B. (2017). Mengukur Risiko dan Kerentanan Bencana pada Skala Lokal di Indonesia melalui Downscaling World Risk Index.

Siagian, T. H., Purhadi, P., Suhartono, S., & Ritonga, H. (2014). Social vulnerability to natural hazards in Indonesia: Driving factors and policy implications. Natural Hazards, 70(2), 1603–1617. https://doi.org/10.1007/s11069-013-0888-3

Songwathana, K. (2018). The Relationship between Natural Disaster and Economic Development: A Panel Data Analysis. Procedia Engineering, 212(2017), 1068–1074. https://doi.org/10.1016/j.proeng.2018.01.138

Syafiyah, U., Puspitasari, D. P., Asrafi, I., Wicaksono, B., & Sirait, F. M. (2022). Analisis Perbandingan Hierarchical dan Non-Hierarchical Clustering Pada Data Indikator Ketenagakerjaan di Jawa Barat Tahun 2020. Seminar Nasional Official Statistics, 2022(1), 803–812. https://doi.org/10.34123/semnasoffstat.v2022i1.1221

Taghizadeh-Hesary, F., Sarker, T., Yoshino, N., Mortha, A., & Vo, X. V. (2021). Quality infrastructure and natural disaster resiliency: A panel analysis of Asia and the Pacific. Economic Analysis and Policy, 69, 394–406. https://doi.org/10.1016/j.eap.2020.12.021

Thamrin, N., & Wijayanto, A. W. (2021). Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island. Indonesian Journal of Statistics and Its Applications, 5(1), 141–160. https://doi.org/10.29244/ijsa.v5i1p141-160

UNISDR. (2009). United Nations International Strategy for Disaster Reduction (UNISDR).

Watung, C. H. T., Sela, R. L. E., & Tondobala, L. (2018). TINGKAT KETANGGUHAN DAN KETAHANAN KOTA MANADO TERHADAP BENCANA. Jurnal Perencanaan Wilayah Dan Kota, 5. https://doi.org/10.5614/jpwk.2014.25.1.1

Wijaya, Y. T., & Halim, I. T. (2022). Measuring and Profiling Social Vulnerability to Natural Disaster in Indonesia in 2019. Jurnal Matematika, Statistika Dan Komputasi, 19(1), 183–194. https://doi.org/10.20956/j.v19i1.21686

Wu, H., Huang, M., Tang, Q., Kirschbaum, D. B., & Ward, P. (2016). Hydrometeorological Hazards: Monitoring, Forecasting, Risk Assessment, and Socioeconomic Responses. Advances in Meteorology, 2016, 11–14. https://doi.org/10.1155/2016/2367939

Wu, K.-L. (2012). Analysis of parameter selections for fuzzy c-means. Pattern Recognition, 45(1), 407–415. https://doi.org/10.1016/j.patcog.2011.07.012

Zhou, Y., Li, N., Wu, W., Wu, J., & Shi, P. (2014). Local Spatial and Temporal Factors Influencing Population and Societal Vulnerability to Natural Disasters. 34(4), 614–639. https://doi.org/10.1111/risa.12193