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All Journal "Journal of Data Science Journal Majelis Paspama
Amran Manalu
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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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.
Performance of K-Means Algorithm for Ground Acceleration Clustering Siska Simamora; Amran Manalu; Paska Marto Hasugian
Journal Majelis Paspama Vol. 2 No. 2 (2024): Journal Majelis Paspama, July 2024
Publisher : Journal Majelis Paspama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia is one of the most seismically active regions in the world due to the convergence of the Indo-Australian, Eurasian, and Pacific tectonic plates. This condition exposes the country to frequent earthquakes with varying magnitudes and intensities that may cause severe structural damage and pose risks to human safety. Ground acceleration, particularly Peak Ground Acceleration (PGA), is a key parameter for evaluating earthquake impacts and is strongly influenced by geological conditions, hypocentral depth, and epicentral distance. However, the complexity and large volume of ground acceleration data often hinder manual interpretation. This study applies the K-Means clustering algorithm to classify ground acceleration data obtained from seismic records at several observation points. Prior to clustering, data preprocessing was performed through data cleaning and min–max normalization to ensure quality and comparability across variables. The optimal number of clusters was determined using the Elbow method and Silhouette Score. The results reveal distinct distribution patterns of ground acceleration, which are closely related to local seismic conditions. These findings are expected to contribute to the development of preliminary ground acceleration zonation, providing valuable insights for earthquake hazard mapping and risk mitigation efforts in Indonesia.
Co-Authors Hasugian , Paska Marto Simamora, Siska