Clustering of Earthquake Prone Areas in Indonesia Using K-Medoids Algorithm

Authors

  • Fiona Ramadhani Senduk Telkom University
  • Indwiarti Indwiarti Telkom University
  • Fhira Nhita Telkom University

DOI:

https://doi.org/10.34818/INDOJC.2019.4.3.359

Abstract

Located right above the ring of fire makes Indonesia prone to natural disasters, especially earthquakes. With the number of earthquakes that have occurred, disaster mitigation is very much needed. The use of data mining methods will certainly help in disaster mitigation. One method that can be used is clustering. The clustering algorithm used in this study is k-Medoids, and comparison with the k-means algorithm is also carried out. The data used are earthquake data from all regions in Indonesia during 2014-2018 that were recorded by the United State Geological Survey (USGS). The results obtained showed that k-medoids giving better silhouette results and computational time than k-means. For the k-medoids cluster results, the highest value of silhouette was 0.4574067 with k = 6. The analysis of each cluster is presented in this paper.

Keywords: clustering,data mining, earthquake, k-medoid.

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References

Sugito, T., Nanin (2008). Tsunami (31-32).Universitas Pendidikan Indonesia.

Gongqian, Xu., Hanghang, He., Ying ,Tao., Yang, Wang. (2005) Earthquake. Roskilde Universitetscenter's Digitale Arkiv.

Malizia, D. ( 2018) A Visual Guide to Volcanoes and Earthquake. The Rosen Publishing Group, Inc. New York.

Clifton, Christopher (2010). Encyclopædia Britannica: Definition of Data Mining.

Han, J., Kamber, M., Pei, J. (2012) Data Mining Concept and Techniques, 3rd ed. Morgan Kaufmann-Elsevier, Amsterdam.

Bharati, M., & Ramageri, M. (2010). Data Mining Techniques an Applications. Indian Journal of Computer Science and Engineering. 1(4), 301-305.

Aggarwal, C., Reddy, C.: Data Clustering: Recent Advances and Applications. Data Mining and Knowledge Discovery Series. Chapman and Hall/CRC, Taylor & Francis Group, Boca Raton (2013)

Kamat, R.,K., Kamath, R.,S. (2017) Earthquake Cluster Analysis: K-Means Approach. Journal of Chemical and Pharmaceutical Sciences 10 (1)

Savaş, C., Yıldız, M. S., Eken, S., İkibaş, C., & Sayar, A. (2019). Clustering Earthquake Data: Identifying Spatial Patterns From Non-Spatial Attributes. In Big Data and Knowledge Sharing in Virtual Organizations (pp. 224-239). IGI Global.

Novianti, Pepi, Dyah Setyorini, & Rafflesia, Ulfasari. (2017) "K-Means cluster analysis in earthquake epicenter clustering." International Journal of Advances in Intelligent Informatics 3.2. 81-89.

Soni, K. G., & Patel, A. (2017). Comparative Analysis of K-means and K-medoids Algorithm on IRIS Data. International Journal of Computational Intelligence Research, 13(5), 899-906.

Arora, P., & Varshney, S. (2016). Analysis of k-means and k-medoids algorithm for big data. Procedia Computer Science, 78, 507-512.

Selvi, H. Z., & Çağlar, B. (2016). Using K-Means and K-Medoids Methods for Multivariate Mapping. International Journal of Applied Mathematics, Electronics and Computers, 4(Special Issue-1), 342-345.

Kaur, N. K., Kaur, U., & Singh, D. D. (2014). K-Medoid clustering algorithm-a review. International Journal of Computer Application and Technology (IJCAT), 1(1), 2349-1841.

Larose, Daniel T. 2005. Discovering Knowledge in Data : An Introduction to Data Mining. John Willey & Sons, Inc

Kaufman, L. and Rousseeuw, P.J. (1987), Clustering by means of Medoids, in Statistical Data Analysis Based on the Norm and Related Methods, edited by Y. Dodge, North-Holland, 405–416.

Prasetyo, Eko. (2014) Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab. Yogyakarta: ANDI

Hertz, T. (2006). Learning Distance Functions: Algo Rithms and Applications. Hebrew University.

Mohibullah, M., Hossain, M. Z., & Hasan, M. (2015). Comparison of euclidean distance function and manhattan distance function using k-mediods. International Journal of Computer Science and Information Security, 13(10), 61.

García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (pp. 195-243). Switzerland: Springer International Publishing.

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Published

2020-01-07

How to Cite

Senduk, F. R., Indwiarti, I., & Nhita, F. (2020). Clustering of Earthquake Prone Areas in Indonesia Using K-Medoids Algorithm. Indonesian Journal on Computing (Indo-JC), 4(3), 65–76. https://doi.org/10.34818/INDOJC.2019.4.3.359

Issue

Vol. 4 No. 3 (2019): December, 2019

Section

Computational and Simulation

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