Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Inferensi
Widiyaningrum, Eka Janivia
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Earthquake Point Clustering Using Self Organizing Maps (SOM) In Sulawesi and Maluku Regions Irwan, Irwan; Zaki, Ahmad; Widiyaningrum, Eka Janivia
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23331

Abstract

Earthquakes pose a major threat in Indonesia, particularly in complex tectonic regions like Sulawesi and Maluku. To support disaster mitigation, this research employs the Self Organizing Maps (SOM) method—an unsupervised technique that reduces data dimensionality into an intuitive two-dimensional form—to cluster earthquake data using four key variables: longitude, latitude, magnitude, and depth. The dataset includes 5,275 earthquake records from 2022, sourced from the Meteorology, Climatology, and Geophysics Agency (BMKG). SOM training produced 25 neurons, which were then grouped into three optimal clusters using hierarchical clustering, validated by internal metrics: the lowest Connectivity Index (296.1512), highest Silhouette Index (0.3304), and a Dunn Index of 0.0058. Cluster 1, with 13 neurons, covers eastern Sulawesi and Maluku, featuring medium magnitude and depth. Cluster 2, with 11 neurons, represents central to southern Sulawesi, characterized by low magnitude and shallow depth. Cluster 3, comprising a single neuron, includes western regions with high-magnitude, very deep earthquakes. Keywords⎯ Clustering, Earthquake, Internal Validation, Self Organizing Maps (SOM).
Co-Authors Ahmad Zaki Irwan Irwan