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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INFOKUM Cendikia : Media Jurnal Ilmiah Pendidikan Jurnal Info Sains : Informatika dan Sains Jurnal Komputer Indonesia (JU-KOMI) Jurnal Manajemen, Akuntansi dan Rumpun Ilmu Ekonomi (MAR-Ekonomi) "Journal of Data Science
Simamora, Siska
SEAN Instiute, Sumatera Utara, Indonesia
Automotive Engineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Other

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Journal : "Journal of Data Science

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Ground Acceleration Clustering Using Self-Organizing Map Method Siska Simamora; Amran Manalu; Paska Marto Hasugian
Journal Of Data Science Vol. 3 No. 02 (2025): Journal Of Data Science, September 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i2.7281

Abstract

Peak Ground Acceleration (PGA) is an important parameter in seismic studies because it is directly related to the level of shaking felt on the earth's surface. Analysis of ground acceleration data is needed to identify patterns, group regions based on their seismic characteristics, and support earthquake disaster mitigation efforts. This study uses the Self-Organizing Map (SOM) method, which is an unsupervised learning approach based on artificial neural networks that can map high-dimensional data into a two-dimensional map representation without losing its topological structure. The ground acceleration dataset used in this study consists of key seismic parameters such as depth, magnitude, source distance, and PGA values. The SOM learning process is carried out iteratively to produce a cluster map that groups earthquake data into several groups with different ground acceleration characteristics. The results show that the SOM method is able to identify ground acceleration distribution patterns more clearly than conventional methods, by producing clusters that represent variations in PGA from low to high. These findings can provide important contributions to earthquake risk mapping, regional spatial planning, and the formulation of more accurate disaster mitigation strategies.
Co-Authors Amran Manalu Andysah Putera Utama Siahaan Glorynta S B Nadeak Glorynta S B Nadeak Hasugian , Paska Marto Khairul Khairul Khairul, Khairul Mathelinea, Devy Muhammad Iqbal Rahmat Rezki Rahmat Rezki Reza Fahromi Reza Fahromi Rian Farta Wijaya Rizky Rinaldi Rizky Sihotang, Jonhariono Simangunsong, Pandi Barita Nauli Sipra Barutu Sitorus, Zulham Zahari Amir Bin Ismani