Klasifikasi Status Erupsi Gunung Berapi Dengan Menggunakan Analisa STA/LTA dan LSTM. (Classification of Volcanic Eruption Status Using STA/LTA Analysis and LSTM).

Wicaksono, Febriansyah Dwi Kurnia (2023) Klasifikasi Status Erupsi Gunung Berapi Dengan Menggunakan Analisa STA/LTA dan LSTM. (Classification of Volcanic Eruption Status Using STA/LTA Analysis and LSTM). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Volcanoes in Indonesia are components of an active volcanic mountain system. This leads to frequent natural disasters, including volcanic eruptions. Generally, volcanic eruption disasters are measured using seismometers. However, the results obtained from tests using seismographs still do not provide classification regarding the types of earthquakes and the types of threat levels from volcanic eruptions. The Long-Short Term Memory (LSTM) algorithm is a modified version of Recurrent Neural Network (RNN) that enables predictions to be made using historical data. In this study, the author applies the STA/LTA algorithm to detect seismic events in seismic signal image data and the LSTM algorithm to predict the type and threat level of a volcanic eruption. This research employs three types of LSTM algorithms: classic LSTM, Vanilla LSTM, and BiLSTM. The evaluation of the results is conducted using the confusion matrix method, which yields an average accuracy of 0.84 for the classic LSTM model, 0.83 for the vanilla LSTM model, and 0.84 for the BiLSTM model.. Keywords: seismic event, confusion matrix, volcanic eruption, LSTM, STA/LTA.

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