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All Journal EKSAKTA: Journal of Sciences and Data Analysis Journal of Science and Applicative Technology Jurnal Matematika UNAND Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Science and Technology Journal of Fundamental Mathematics and Applications (JFMA) Smart Society: Community Service and Empowerment Journal Indonesian Journal of Applied Mathematics
Mika Alvionita Sitinjak
Institut Teknologi Sumatera
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Journal : Journal of Science and Technology

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Recognition of Voronoi Cell Distribution in Earthquake Epicenter Data in the Sunda Strait Region, Indonesia Muliawati, Triyana; Lestari, Fuji; Harbowo, Danni Gathot; S, Mika Alvionita
JOSTECH Journal of Science and Technology Vol 4, No 2: September 2024
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v4i2.9830

Abstract

The Sunda Strait is currently one of the busiest transportation hubs. However, this area has a significant history of geological disasters caused by the dynamic tectonic activity of the Eurasian and Indo-Australian tectonic plates. These disasters include the supervolcanic eruption of Krakatoa in 1883, the Sunda Strait tsunami in 2018, and decades of frequent earthquakes. To address these challenges, this study analyzes the frequency and distribution of seismic activity in the Sunda Strait region based on epicenter data recorded in the United States Geological Survey (USGS) Earthquake Catalog. We collected 440 multivariate earthquake data points between 1990 and 2023 (over three decades). The results of this study show that a machine learning approach accurately identified four relevant parameters for k-means clustering, followed by a silhouette value analysis to recognize the distribution of Voronoi cells. Based on earthquake data from the Sunda Strait from 1990 to 2023, the two highest silhouette analysis values, 0.40 and 0.39, are located at k=3 and k=5 in k-means clustering. This approach has recognized and identified the cell area of earthquake activity in the Sunda Strait, particularly around Anak Krakatoa. This study provides new insights into the spatiotemporal characteristics and identifies clusters of earthquake-prone areas. The information generated in this study facilitates the evaluation of future earthquake disaster risks, especially those with epicenters in the Sunda Strait region.
Co-Authors Danni Gathot Harbowo Dhea Sukma Agustiana Dimas Wahyu Saputro Fadia Dilla Sabine Fuji Lestari Irawati, Febri Dwi Lis Nuraini Luluk Muthoharoh Muliawati Muliawati, Triyana Muthoharoh, Luluk Nadeak, Christyan Tamaro Nuramaliyah, Nuramaliyah Rahwani, Putri Hazizah Rekzi P. Manullang Riri Lestari Satria, Ardika Syaiful, Achmad Taj Shavira Tirta Setiawan, Tirta