Article Sidebar
Main Article Content
Abstract
To address the limitations of the Social Vulnerability Index (SoVI) in only providing a general overview without pinpointing areas of social vulnerability, a correlational approach paired with a clustering method can be applied. This approach helps in identifying dominant factors and pinpointing socially vulnerable districts or cities in Central Java. The study employs the K-Medoids algorithm, which is advantageous when dealing with outliers in the dataset. Three different distance measures are considered: Euclidean, Manhattan, and Minkowski distances, to identify the optimal clustering of social vulnerability. The evaluation of the best cluster is conducted using the Davies-Bouldin Index, a metric for validating clustering models by averaging the similarity of each cluster to its most similar counterpart. Findings indicate that using the K-Medoids algorithm with Manhattan distance yields the most effective clustering, resulting in two distinct clusters. Cluster 1, comprising 25 districts/cities, is identified as the most vulnerable to natural disasters and challenges in education, demography, economy, and health. Meanwhile, Cluster 2, encompassing 10 districts/cities, includes urban areas with the highest social vulnerability, notably in the proportion of rental housing.
Keywords
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
- Anggara, M., Sujiani, H., & Nasution, H. (2016). Pemilihan Distance Measure Pada K-Means Clustering Untuk Pengelompokkan Member di Alvaro Fitness. Jurnal Sistem dan Teknologi Informasi (JUSTIN), 1(1), 1-6.
- Aurora, P., Deepali, & Varshney, S. (2016). Analysis of K-Means and K-Medoids Algorithm for Big Data. Procedia Computer Sains, 78: 7-12.
- Berawi, M.A., Aulidtyo, A., Miraj, P., & Gunawan. Developing affordable housing for low-income households in developing countries using participative payment. IOP Conference Series: Materials Science and Engineering. DOI: 10.1088/1757-899X/650/1/012013.
- Birkmann, J., Setiadi, N.J., & Gebert, N. (2008). Socio-economic Vulnerability at The Local Level in Context of Tsunamy Early War Evacuation Planning in The City of Padang, West Sumatera. Proceedings of the International Conference on Tsunami Warning (ICTW). Bali, Indonesia, 1-8.
- BNPB, PDSI Pusdatinkom. (2024). Buku Data Bencana Tahun 2023. Jakarta, Indonesia: Pusdatinkom BNBP.
- Cutter, S.L. (2024). The origin and diffusion of the social vulnerability index (SoVI). International Journal of Disaster Risk Reduction, 109, 104576. DOI: https://doi.org/10.1016/j.ijdrr.2024.104576
- Cutter, S.L., Boruff, B.J., & Shirley, W.L. (2003). Social Vulnerability to Environmental Hazards.
- Farissa, R.A., Mayasari, R., & Umaidah, Y. (2021). Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Sillhouette Coefficient. Journal of Applied Informatics and Computing (JAIC), 5(2), 109-116.
- Han, J. & Kamber, M. (2017). Data Mining, Concept and Techniques. Waltham: Morgan Kauffman Publisher.
- Kuran, C. H. A., Morsut, C., Kruke, B. I., Krüger, M., Segnestam, L., Orru, K., Naevestad, T. O., Airola, M., Keränen, J., Gabel, F., Hansson, S., & Torpan, S. (2020). Vulnerability and vulnerable groups from an intersectionality perspective. International Journal of Disaster Risk Reduction, 50. DOI: https://doi.org/10.1016/j.ijdrr.2020.101826
- Kurniawan, R., Nasution, B.I., Agustina, N., & Yuniarto, B. (2022). Revisiting Social Vulnerability Analysis in Indonesia Data. Data in Brief; 40, 1-7. DOI: https://doi.or/10.1016/j.dib.2021.107743
- Mankiw, G.N. (2010). Macroeconomics. Seventh Edition. New York: Worth Publishers.
- Nasution, B.I., Kurniawan, R., Siagian, T.H., & Fudholi, A. (2020). Revisiting Social Vulnerability Analysis in Indonesia: An Optimized Spatial Fuzzy Clustering Approach. International Journal of Disaster Risk Reduction, 51: 1-11. DOI: https://doi.org/10.1016/j.ijdrr.2020.101801
- Ningrat, F.R., Maruddani, D.A.I., & Wuryandari, T. (2016). Analisis Cluster dengan Algoritma K- Means dan Fuzzy C-Means Clustering untuk Pengelompokkan Data Obligasi Korporas. Jurnal Gaussian, 5(4), 641-650.
- Perumahan dan Kesehatan Lingkungan Menggunakan Metode KMedoids. In Prosiding Seminar Nasional Unimus (Vol. 6).
- Rufat, S. (2014). Spectroscopy of Urban Vulnerability, Annals of the Association of American Geographers, 103(3): 505-525. DOI: https://doi.or/10.1080/00045608.2012.702485
- Setyawati, A. W. (2017). Impelmentasi Algoritma Partitioning Around Medoid (PAM) untuk Pengelompokkan Sekolah Menengah Atas di DIY Berdasarkan Nilai Daya Serap ujian Nasional. Skripsi. Yogyakarta: Fakultas Sains dan Teknologi, Universitas Sanata Darma.
- Siagian, T.H., Purhadi, P., Suhartono, S., & Ritonga, H. (2014). Social vulnerability to natural hazards in Indonesia: driving factors and policy implications, Nat. Hazards. Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 70(2): 1603-1617. DOI: https://doi.org/10.1007/s11069-013-0888-3
- Social Science Quarterly. 2003; 84(2): 242–261. DOI : https://doi.or/10.1111/1540-6237.8402002 Fadlurohman, A., & Nur, I. M. (2023). Pengelompokkan Provinsi di Indonesia Berdasarkan Indikator
- Sunge, A.S., Heryadi, Y., Religia, Y., & Lukas. (2020). Comparison of Distance Function to Performance of K-Medoids Algorithm for Clustering. International Conference on Smart Technology and Applications (ICoSTA), Surabaya, Indonesia, 1-6. DOI: 10.1109/ICoSTA48221.2020.1570615793.
- UNISDR. (2023). Implementation of the Hyogo Framework for Action: Summary of Reports 2007- 2013. United Nations Secretariat of The International Strategy of Disaster Reduction (UNISDR). Geneva: Switzerland.
- Vercillis, C. (2009) Business Intelligence, Data Mining and Optimization for Decision making. Milan: Wiley.
- Wira B, Budianto AE, Wiguna AS. Impelmentasi Metode K-Medoids Clustering Mengetahui Pola Pemilihan Program Studi Mahasiswa Baru Tahun 2018 di Universitas Kanjuruhan Malang. Jurnal Terapan Sains & Teknologi. 2019; 1(3): 54-69.