Literature Review of Earthquake Clustering Algorithms in Indonesia
DOI:
https://doi.org/10.59934/jaiea.v4i3.1072Keywords:
Clustering, Earthquake, Indonesia, Literature Review, Machine LearningAbstract
This study presents a structured literature review, often referred to as a Systematic Literature Review (SLR), based on 18 articles discussing clustering methods. The primary aim of this study is to explore how clustering techniques have been applied to earthquake data in Indonesia. To achieve this, the study addresses four key research questions. First, it examines which algorithms are most commonly used for earthquake clustering in Indonesia. Second, it evaluates which algorithms demonstrate the best performance. Third, it investigates which regions within Indonesia are most frequently studied in this context. Finally, it analyzes the types of datasets that are most often utilized for earthquake clustering in the country. The findings indicate that the K-Means algorithm is not only the most frequently used but also consistently shows strong performance. In addition, earthquake clustering studies most commonly focus on Indonesia as a whole, using publicly available datasets. These insights offer valuable guidance for researchers seeking to apply or further develop clustering methods for earthquake-related studies in Indonesia.
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