skip to main content

CLUSTERING OF EARTHQUAKE RISK IN INDONESIA USING K-MEDOIDS AND K-MEANS ALGORITHMS

Isna Hidayatur Rifa  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
*Hasih Pratiwi scopus  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
Respatiwulan Respatiwulan  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
Open Access Copyright (c) 2020 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Earthquake is the shaking of the earth's surface due to the shift in the earth's plates. This disaster often happens in Indonesia due to the location of the country on the three largest plates in the world and nine small others which meet at an area to form a complex plate arrangement. An earthquake has several impacts which depend on the magnitude and depth. This research was, therefore, conducted to classify earthquake data in Indonesia based on the magnitudes and depths using one of the data mining techniques which is known as clustering through the application of k-medoids and k-means algorithms. However, k-medoids group data into clusters with medoid as the centroid and it involves using clustering large application (CLARA) algorithm while k-means divide data into k clusters where each object belongs to the cluster with the closest average. The results showed the best clustering for earthquake data in Indonesia based on magnitude and depth is the CLARA algorithm and five clusters were found to have total members of 2231, 1359, 914, 2392, and 199 objects for cluster 1 to cluster 5 respectively.
Fulltext View|Download
Keywords: Earthquake, Data Mining, Clustering; K-Medoids Algorithm; K-Means Algorithm

Article Metrics:

  1. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J. M., & Perona, I. (2013). An Extensive Comparative Study of Cluster Validity Indices. Pattern Recognition, 46(1), 243–256. https://doi.org/10.1016/j.patcog.2012.07.021
  2. Bird, P. (2003). An Updated Digital Model of Plate Boundaries. Geochemistry, Geophysics, Geosystems, 4(3). https://doi.org/10.1029/2001GC000252
  3. Briceno, S. (2007). Perkataan Menjadi Tindakan: Panduan untuk Mengimplementasikan Kerangka Kerja Hyogo. http://www.unisdr.org/ files/594_Bahasa HFA.pdf
  4. Febriani, B. S. & Hakim, R. F. (2015). Analisis Clustering Gempa Bumi Selama Satu Bulan Terakhir dengan Menggunakan Algoritma Self-Organizing Maps (SOMs) Kohonen. Prosiding Seminar Nasional Matematika dan Pendidikan Matematika UMS, 715–722
  5. Fowler, C. M. R. (2005). The Solid Earth: An Introduction to Global Geophysics (2nd Edition). Cambridge University Press
  6. Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd Edition). Elsevier Inc
  7. Hermawati, F. . (2013). Data Mining. CV Andi Offset
  8. Kaufman, L. & Rousseeuw, P. J. (1987). Clustering By Means of Medoids
  9. Milsom, J., Masson, D., Nichols, G., Sikumbang, N., Dwiyanto, B., Parson, L., & Kallagher, H. (1992). The Manokwari Trough and the Western End of the New Guinea Trench. Tectonics, 11(1), 145–153. https://doi.org/https://doi.org/10.1029/91TC01257
  10. Pratiwi, H., Rini, L. S., & Mangku, I. W. (2018). Marked Point Process for Modelling Seismic Activity (Case Study in Sumatra and Java). Journal of Physics: Conference Series, 1022(1). https://doi.org/10.1088/1742-6596/1022/1/012004
  11. Saraçli, S., Doǧan, N., & Doǧan, I. (2013). Comparison of Hierarchical Cluster Analysis Methods by Cophenetic Correlation. Journal of Inequalities and Applications, 203, 1–8. https://doi.org/10.1186/1029-242X-2013-203
  12. Septiana, L., & Djohan, N. (2015). Analisis Perbandingan Algoritma K-Means Clustering dan Expectation-Maximation (EM) untuk Klasifikasi Butir Beras. Jurnal Teknik Dan Ilmu Komputer, 4(15), 245–253
  13. Struyf, A., Hubert, M., & Rousseeuw, P. J. (1997). Integrating Robust Clustering Techniques in S-PLUS. Computational Statistics and Data Analysis, 26(1), 17–37. https://doi.org/10.1016/S0167-9473(97)00020-0
  14. Sunarjo, Gunawan, M.T., & Pribadi, S. (2010). Gempa Bumi Indonesia (Populer). Badan Meteorologi Klimatologi dan Geofisika. Jakarta
  15. USGS. Search Eartquake Catalog. (n.d.). United States Geological Survey

Last update:

  1. Clustering of disaster prones areas in Java Island

    Kadek Mei Purnama Dewi, Mey Lista Tauryawati, Ahmad Fuad Zainuddin. RECENT ADVANCES IN MATERIALS AND MANUFACTURING: ICRAMM2023, 3231 , 2024. doi: 10.1063/5.0230700
  2. Clustering Earthquakes in West Java Using Machine Learning Algorithm

    Naikson Fandier Saragih, Yolanda Yulianti Pratiwi, Indra Kelana Jaya, Indra M Sarkis, Hanifullah Hafidz Arrizal, Marzuki Sinambela. 2023 International Conference of Computer Science and Information Technology (ICOSNIKOM), 2023. doi: 10.1109/ICoSNIKOM60230.2023.10364541
  3. ROBUST PORTFOLIO SELECTION WITH CLUSTERING BASED ON BUSINESS SECTOR OF STOCKS

    La Gubu, Dedi Rosadi, Abdurakhman Abdurakhman. MEDIA STATISTIKA, 14 (1), 2021. doi: 10.14710/medstat.14.1.33-43
  4. An early warning model for starfish disaster based on multi-sensor fusion

    Longyu Li, Tao Liu, Hui Huang, Hong Song, Shuangyan He, Peiliang Li, Yanzhen Gu, Jiawang Chen. Frontiers in Marine Science, 10 , 2023. doi: 10.3389/fmars.2023.1167191
  5. Java Island Health Profile Clustering using K-Means Data Mining

    Muhammad Andryan Wahyu Saputra, Sri Harini. International Journal on Information and Communication Technology (IJoICT), 8 (1), 2022. doi: 10.21108/ijoict.v8i1.606
  6. Profiling the Spatial and Temporal Properties of Earthquake Occurrences Using ST-DBSCAN Algorithm

    Lizda Iswari. 2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA), 2022. doi: 10.1109/ICITDA55840.2022.9971295
  7. Cluster Analysis and Seismicity of The Samosir Using Machine Learning Approach

    Marzuki Sinambela, Eva Darnila, Indra Kelana Jaya, Indra M Sarkis, Alex Rikki. 2023 Eighth International Conference on Informatics and Computing (ICIC), 2023. doi: 10.1109/ICIC60109.2023.10381924
  8. Application of Improved K-Means Algorithm in Collaborative Recommendation System

    Hui Jing, Fernando Simoes. Journal of Applied Mathematics, 2022 , 2022. doi: 10.1155/2022/2213173
  9. Detection of coherent thermohaline structures over the global ocean using clustering

    Emmanuel Romero, Esther Portela, Leonardo Tenorio-Fernandez, Laura Sánchez-Velasco. Deep Sea Research Part I: Oceanographic Research Papers, 209 , 2024. doi: 10.1016/j.dsr.2024.104344

Last update: 2024-11-21 21:48:03

No citation recorded.