Prediksi Kejadian Gempa Bumi di Indonesia Berbasis Pola Runtun Waktu Dengan Autoregressive Intergrated Moving Average

Abstract

Earthquake prediction plays a crucial role in disaster mitigation efforts, especially in earthquake-prone areas like Indonesia, which is located on the Pacific Ring of Fire. This study employs the Autoregressive Integrated Moving Average (ARIMA) model for predicting earthquake occurrences in Indonesia based on time series data. The ARIMA model, a powerful tool for analyzing and forecasting time-dependent data, is utilized to identify patterns and trends in historical seismic data. By applying this model, the research aims to enhance the accuracy of earthquake predictions, which is vital for disaster preparedness and risk reduction strategies. The results of this study are expected to provide valuable insights for policymakers in Indonesia to formulate proactive disaster mitigation policies, such as early warning systems, evacuation plans, and infrastructure resilience measures. The findings will contribute to improving the nation's ability to respond to seismic events, thereby minimizing loss of life and property. The use of the ARIMA model in this context will also facilitate better planning and resource allocation in anticipation of future earthquakes.