IMPLEMENTATION OF THE DBSCAN METHOD FOR CLUSTER MAPPING OF EARTHQUAKE SPREAD LOCATION
Abstract
West Java area is located on the Pacific Circum and Mediterranean Circum routes, this causes West Java area to be an unstable area that is characterized by many active working volcanoes and frequent earthquakes. An analysis of the grouping of earthquake data in West Java Province area is urgently needed. The purpose of this study was to classify areas based on the density of earthquake occurrence areas in West Java using Density-Based Spatial Clustering of Application with Noise (DBSCAN). The population in this study are all earthquake events occurred in 2021. While the sample used in this study is data on the location of the distribution of earthquakes in West Java Province in 2021 taken from the BMKG online data website at dataonline.bmkg.go.id. This research began with nearest-neighbor analysis to see patterns of data distribution. If the data distribution pattern is grouped, then DBSCAN analysis can be continued. The DBSCAN algorithm uses a combination of parameters, namely minimum points (MinPts) and epsilon (Eps). Cluster results are evaluated using the silhouette coefficient. Then, in this study, deeper data exploration was carried out in three ways, namely: (1) Clustering based on the highest silhouette value, (2) clustering by lowering the MinPts value, and (3) clustering based on the smallest upper limit (supremum) value of the silhouette coefficient. The data exploration here aimed to form more clusters while still considering the silhouette coefficient value limits so that there are more areas prone to earthquakes but also maintaining the validity of the results obtained. Next, determine the best cluster results by comparing the cluster results obtained. The best cluster results were obtained at Eps=10000 and MinPts=3 which formed 12 clusters with a silhouette coefficient value of 0.713, which means that the clusters have a strong structure. It is hoped that the information regarding the grouping of areas where earthquakes frequently occur can be used as a form of earthquake disaster mitigation and minimize the impact of losses due to the earthquake.
Downloads
References
D. Hermon, Geografi Bencana Alam - Rajawali Pers. Jakarta: PT RajaGrafindo Persada, 2015.
A. M. Nur, “Gempa Bumi, Tsunami dan Mitigasinya,” Gempa Bumi, Tsunami Dan Mitigasinya, vol. 7, no. 1, 2010.
BPS-Statistics Indonesia, Statistical Yearbook of Indonesia in Infografis. Jakarta: BPS-Statistics Indonesia, 2022.
PVMBG, “Tipe Gunung Api di Indonesia,” Magma Indonesia, 2021. [Online]. Available: https://magma.esdm.go.id/v1/edukasi/tipe-gunung-api-di-indonesia-a-b-dan-c.
BNPB, “Bencana Indonesia 2021,” 2022. [Online]. Available: https://gis.bnpb.go.id/infografis/bencana/tahun 2021.png.
M. T. Furqon and L. Muflikhah, “Clustering the Potential risk of Tsunami using Density-Based Spatial Clustering of Application with Noise (DBSCAN),” J. Enviromental Eng. Sustain. Technol., vol. 3, no. 1, pp. 1–8, 2016.
S. Harini, H. Fahmi, A. D. Mulyanto, and M. Khudzaifah, “The earthquake events and impacts mapping in Bali and Nusa Tenggara using a clustering method,” in IOP Conference Series: Earth and Environmental Science, 2020.
M. T. Anwar, W. Hadikurniawati, E. Winarno, and A. Supriyanto, “Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation,” Adv. Sustain. Sci. Eng. Technol., vol. 1, no. 1, pp. 1–7, 2019.
F. Reviantika, C. N. Harahap, and Y. Azhar, “Analisis Gempa Bumi Pada Pulau Jawa Menggunakan Clustering Algoritma K-Means,” J. Din. Inform., vol. 9, no. 1, pp. 51–60, 2020.
A. Wahyu, and Rushendra, “Klasterisasi Dampak Bencana Gempa Bumi Menggunakan Algoritma K-Means di Pulau Jawa,” J. Edukasi dan Penelit. Inform., vol. 8, no. 1, pp. 175–179, 2022.
D. Kurmiati, M. Zakiy Fauzi, Ripangi, A. Falegas, and Indria, “Clustering of Earthquake Prone Areas in Indonesia Using K-Medoids Algorithm Klasterisasi Daerah Rawan Gempa Bumi di Indonesia Menggunakan Algoritma K-Medoids,” Indones. J. Mach. Learn. Comput. Sci. , vol. 1, no. 1, pp. 47–57, 2021.
B. S. Febriani and R. F. Hakim, “Analisis Clustering Gempa Bumi Selama Satu Bulan Terakhir Dengan Menggunakan Algoritma Self-Organizing Maps (SOMs) Kohonen,” in Prosiding Seminar Nasional Matematika dan Pendidikan Matematika UMS, 2015, pp. 715–722.
N. N. Sari, “K-AFFINITY PROPAGATION (K-AP) CLUSTERING UNTUK KLASIFIKASI GEMPA BUMI (Studi Kasus: Gempa Bumi di Indonesia Tahun 2017),” Universitas Islam Indonesai, 2018.
R. R. Arista, R. A. Asmara, and D. Puspitasari, “Pengelompokan Kejadian Gempa Bumi Menggunakan Fuzzy C-Means Clustering,” J. Teknol. Inform. dan Terap., vol. 04, no. 02, pp. 3–10, 2017.
M. Amiruzzaman, R. Rahman, M. R. Islam, and R. M. Nor, “Logical Analysis of Built-in DBSCAN Functions in Popular Data Science Programming Languages,” Mist Int. J. Sci. Technol., vol. 10, no. June, pp. 25–32, 2022.
D. Ebdon, Statistics in Geography. Blackwell Ppublishing, 1985.
J. Han, M. Kamber, and J. Pei, Data mining: Data Mining Concepts and Techniques. USA: Morgan Kaufman, 2014.
B. N. Boots and A. Getls, Point Pattern Analysis. Sage Publication, 2020.
E. Prasetyo, Data Mining: Konsep dan Aplikasi menggunakan Matlab. Jakarta: CV Andi Offset, 2012.
I. M. S. Putra, “Algoritma DBSCAN (Density-Based Spatial Clustering of Application with Noise) dan contoh perhitungannya.,” Bali, 2018.
M. Ester, H. P. Kriege, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Satial Databases with Noise,” in KDD-96, 1996, pp. 226–231.
P. J. Rousseeuw, “Silhouettes: A graphical Aid to the Iinterpretation and Validation of Cluster Analysis,” J. Comput. Appl. Math., vol. 20, no. C, pp. 53–65, 1987.
N. Kaoungku, S. Keerachart, R. Chanklan, K. Kerdprasop, and N. Kerdprasop, “The silhouette width Criterion for Clustering and Association Mining to Select Image Features,” Int. J. Mach. Learn. Comput., vol. 8, no. 1, pp. 69–73, 2018.
A. Struyf, M. Hubert, and P. J. Rousseeuw, “Clustering in an Object-Oriented Environment,” J. Stat. Softw., vol. 1, no. 4, pp. 1–30, 1997.
R. Hidayati, A. Zubair, A. H. Pratama, and L. Indana, “Analisis Silhouette Coefficient pada 6 Perhitungan Jarak K-Means Clustering,” Techno.COM, vol. 20, no. 2, pp. 186–197, 2021.
L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics, 2005.
BPBD Provinsi Jawa Barat, “Peta Bahaya Gempa Bumi di Provinsi Jawa Barat,” Badan Nasional Penanggulangan Bencana Daerah Provinsi Jawa Barat, 2022. [Online]. Available: https://bpbd.jabarprov.go.id/peta-krb-2/.
Copyright (c) 2023 Muhammad Bariklana, Achmad Fauzan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
