K-Prototypes Algorithm for Clustering The Tectonic Earthquake in Sulawesi Island

  • Suwardi Annas Department of Statistics, Universitas Negeri Makassar
  • Irwan Irwan Department of Mathematics, Universitas Negeri Makassar
  • Rahmat H Safei Department of Statistics, Universitas Negeri Makassar
  • Zulkifli Rais Department of Statistics, Universitas Negeri Makassar
DOI: https://doi.org/10.30812/varian.v5i2.1908
Keywords: Algorithm K-Prototype, Clustering, Earthquake, Magnitude

Abstract

Natural disasters that had occurred in Indonesia consist of hydro-meteorology: floods, droughts, and landslides, geophysical: volcanic earthquakes and volcanic eruptions, and biological: epidemics. Regarding the tectonic earthquake on Sulawesi Island, there are at least 2 earthquake disasters that became national disasters, namely in Central Sulawesi and West Sulawesi in the range of 2017 to 2021. This study aims to cluster tectonic earthquakes on Sulawesi Island, from 2017 to 2020, as the basis for formulating disaster mitigation plans. This study used tectonic earthquake data from 2017 to 2020 obtained from BMKG Gowa, Indonesia. The variables used are magnitude, depth, and distance category. Because they are mixed variables, this study used a k-prototype algorithm. There are four clusters in 2017, six clusters in 2018, five clusters in 2019, and six clusters in 2020 based on the ratio of within-cluster distance against between-cluster distance. It can be related to the active fault on Sulawesi Island. The characteristics of clusters form each year are the greater magnitude of the earthquake, the deeper of deep and the category distance is dominated by the regional level.

References

REFERENCESAhmad, A. and Dey, L. (2011). A k-means type clustering algorithm for subspace clustering of mixed numeric and categoricaldatasets.Pattern Recognition Letters, 32(7):1062–1069.Akramunnisa, A. and Fajriani, F. (2020). K-Means Clustering Analysis pada Persebaran Tingkat Pengangguran Kabupaten/Kota diSulawesi Selatan.Jurnal Varian, 3(2):103–112.Ansori Mattjik, A. and Sumertajaya (2011).Sidik Peubah Ganda Dengan menggunakan SAS. IPB PRESS Edisi.Dinh, D.-T., Huynh, V.-N., and Sriboonchitta, S. (2021). Clustering mixed numerical and categorical data with missing values.Information Sciences, 571:418–442.Iriawan, N., Fithriasari, K., Ulama, B., Suryaningtyas, W., Susanto, I., and Pravitasari, A. (2018). Bayesian Bernoulli MixtureRegression Model for Bidikmisi Scholarship Classification.Jurnal Ilmu Komputer dan Informasi, 11(2).Ji, J., Bai, T., Zhou, C., Ma, C., and Wang, Z. (2013). An improved k-prototypes clustering algorithm for mixed numeric andcategorical data.Neurocomputing, 120:590–596.Ji, J., Pang, W., Zhou, C., Han, X., and Wang, Z. (2012). A fuzzy k-prototype clustering algorithm for mixed numeric and categoricaldata.Knowledge-Based Systems, 30:129–135.Kuo, R.-J., Zheng, Y., and Nguyen, T. P. Q. (2021). Metaheuristic-based possibilistic fuzzy k-modes algorithms for categorical dataclustering.Information Sciences, 557:1–15.Kuo, T. and Wang, K.-J. (2022). A hybrid k-prototypes clustering approach with improved sine-cosine algorithm for mixed-dataclassification.Computers & Industrial Engineering, page 108164.Li, C., Wu, X., Cheng, X., Fan, C., Li, Z., Fang, H., and Shi, C. (2019). Identification and analysis of vulnerable populations formalaria based on K-prototypes clustering.Environmental research, 176:108568.Mau, T. N. and Huynh, V.-N. (2021). An LSH-based k-representatives clustering method for large categorical data.Neurocomputing,463:29–44.Nooraeni, R., Arsa, M. I., and Projo, N. W. K. (2021). Fuzzy Centroid and Genetic Algorithms: Solutions for Numeric and CategoricalMixed Data Clustering.Procedia Computer Science, 179:677–684.Pham, D.-T., Suarez-Alvarez, M. M., and Prostov, Y. I. (2011). Random search with k-prototypes algorithm for clustering mixeddatasets.Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 467(2132):2387–2403.Sarma, T. H., Viswanath, P., and Reddy, B. E. (2013). Speeding-up the kernel k-means clustering method: A prototype based hybridapproach.Pattern Recognition Letters, 34(5):564–573.Sulastri, S., Usman, L., and Syafitri, U. D. (2021). K-prototypes Algorithm for Clustering Schools Based on The Student AdmissionData in IPB University.Indonesian Journal of Statistics and Its Applications, 5(2):228–242.White, L. T., Hall, R., Armstrong, R. A., Barber, A. J., BouDagher Fadel, M., Baxter, A., Wakita, K., Manning, C., and Soesilo,J. (2017). The geological history of the Latimojong region of western Sulawesi, Indonesia.Journal of Asian Earth Sciences,138:72–91.
Published
2022-05-01
How to Cite
[1]
S. Annas, I. Irwan, R. Safei, and Z. Rais, “K-Prototypes Algorithm for Clustering The Tectonic Earthquake in Sulawesi Island”, Jurnal Varian, vol. 5, no. 2, pp. 191 - 198, May 2022.
Issue
Vol 5 No 2 (2022)
Section
Articles
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