Earthquake Prediction in Indonesia using Descriptive Statistics, Pearson Correlation, and Ensemble Machine Learning (Random Forest, XGBoost, LightGBM)
DOI:
https://doi.org/10.56873/jitu.9.1.6060Keywords:
Pearson Correlation, Machine learning, Statistics, Earthquake ClassificationAbstract
Indonesia is a seismically active region because it is crossed by the meeting point of three tectonic plates: the Indo-Australian Plate, the Eurasian Plate, and the Pacific Plate, commonly referred to as the Pacific Ring of Fire. This research seeks to forecast earthquake occurrences in Indonesia by integrating descriptive statistics, Pearson correlation analysis, and ensemble-based machine learning techniques including Random Forest, XGBoost, and LightGBM. The dataset is sourced from the BMKG and covers the period from September 1 to December 20, 2024. The methods used include descriptive statistics, which can help identify trends and patterns in earthquake data such as frequency, average magnitude, and geographical distribution; Pearson correlation to show the relationship between earthquake variables such as magnitude, depth, and location; and ensemble machine learning to help predict the likelihood of earthquakes based on historical data patterns. The use of descriptive statistics, Pearson correlation, and ensemble machine learning in earthquake prediction is an important step toward enhancing understanding of earthquakes and aiding in disaster risk mitigation efforts
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