Analisis Time Series Untuk Deep Learning Dan Prediksi Data Spasial Seismik: Studi Literatur - Eko Nur Cahyo; Erma Susanti* (Jurnal Teknologi, Fakultas Teknologi Industri, Institut Sains & Teknologi AKPRIND Yogyakarta, 2022)
Time series analysis is a deep learning method to process data stored in a dense time. This method works for forecast data in particular cases, such as to analyze and provide the output of a trend from the data. Time series analysis can be used to analyze trends in natural disasters such as earthquakes in Indonesia. As technology advances today, in addition to using seismographs or tools to predict seismic phenomena on the earth's surface, time series analysis can be used to make forecasts and predictions. This paper will summarize various literature reviews about time series analysis and previous research to predict seismic spatial data over the last ten years. The goal is to afford the several approaches or algorithms to be able to forecast seismic spatial data to increase awareness. The results of this literature study were used to find trends, state of the arts, and research challenges and develop new models or methods to predict seismic spatial data. The study shows that deep learning methods can achieve better accurate performance in processing seismic spatial data and other complex data than conventional methods. The deep learning methods can use Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), linear regression, and Artificial Neural Network (ANN).