Perbandingan Efektivitas Algoritma Long Short-Term Memory (LSTM) dan Convolution Neural Network (CNN) dalam Deteksi Sinyal Seismik. (Comparison of the Effectiveness of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) Algorithms in Seismic Signal Detection).

Setiawan, Alfin Dwi (2024) Perbandingan Efektivitas Algoritma Long Short-Term Memory (LSTM) dan Convolution Neural Network (CNN) dalam Deteksi Sinyal Seismik. (Comparison of the Effectiveness of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) Algorithms in Seismic Signal Detection). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Seismic signals are vibrations that propagate through the earth's layers and contain important information about the earth's structure and geological changes, which are significant for natural disaster mapping and natural resource exploration. Using seismic signal data from earthquakes in Indonesia, this research goes through several stages: data collection, preprocessing, model building, performance evaluation, and result analysis. LSTM and CNN models are designed and trained using the preprocessed data, then evaluated using metrics such as accuracy, precision, and recall. To overcome data imbalance, the oversampling technique Synthetic Minority Over-sampling Technique (SMOTE) and feature standardization through StandartScaler were used. The results show that the CNN model excels in terms of accuracy, especially at training and test data ratios of 30:70 and 10:90, with accuracy reaching 0.81. The addition of SMOTE increased the accuracy of both models to 0.97, but the addition of StandartScaler decreased the accuracy. Further testing using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) shows that the LSTM model is better at handling errors than CNN, with an MSE value of 0.35 and RMSE of 0.59. The results of this study confirm that LSTM and CNN are effective for seismic signal detection, with the addition of SMOTE providing significant performance improvement on unbalanced datasets. This research makes an important contribution in the development of more reliable and adaptive seismic signal detection systems, particularly for geophysical applications and volcano disaster mitigation.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Seismic signals, LSTM, CNN, volcanic detection, artificial intelligence.
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Divisions:	Fakultas Teknik > Program Studi Teknik Informatika
Depositing User:	1462000137 Alfin Dwi Setiawan
Date Deposited:	05 Sep 2001 19:22
Last Modified:	03 Oct 2001 19:45
URI:	http://repository.untag-sby.ac.id/id/eprint/41510

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