Statistical Pattern Recognition of Lithosphere Anomalous Activity Along the Indonesian Ring of Fire - S, Mika Alvionita; Satria, Ardika; Muliawati, Triyana; Lestari, Fuji; Harbowo, Danni Gathot (Journal of Science and Applicative Technology, 2025)
The introduction of statistical pattern recognition becomes highly important for assessing disaster threats such as earthquakes. This approach is significantly more comprehensive and suitable for long-term event forecasting. Therefore, in the future, efforts can be promptly made to reduce the risk of disasters resulting from anomalies in lithospheric activity, especially frequent earthquakes in the Sumatra Island region, Indonesia. Statistical pattern analysis of lithospheric activity anomalies can be categorized through classification. Earthquake classification is performed based on magnitude scale and mathematical calculations of earthquake parameter unit conversion. The classification method employed in this research includes machine learning methods like k-nearest neighbor and support vector machine. The evaluation metrics used for machine learning models are model accuracy and confusion matrix tables.