Indonesia, strategically positioned along the Pacific Ring of Fire, is exceptionally susceptible to
seismic activities. Accurate estimation of ground motion parameters is crucial for mitigating the
impact of earthquakes. Recent advancements in machine learning have expanded its application
across various fields, including the analysis of seismic data. This research proposes the use of
Artificial Neural Networks (ANNs) to enhance the accuracy and reliability of estimating ground
motion parameters. Our methodology involves the collection and processing of data from seismic
stations across Indonesia, provided by BMKG (Meteorological, Climatological, and Geophysical
Agency of Indonesia). The dataset encompasses records from eight seismological stations, capturing
three channels. This study primarily focuses on the Cianjur region, a zone highly prone to seismic
disturbances, and intends to develop a robust model capable of accurately estimating ground motion
parameters, particularly in regions with incomplete or limited seismic data. Therefore, this research
is expected to contribute to ongoing efforts to improve the estimation of earthquake-related seismic
parameters by estimating earthquake-related seismic parameters using the Artificial Neural Network
(ANN) method and model optimization using the Particle Swarm Optimization (PSO) algorithm. The
results were evaluated using PSO and without PSO. It is shown that the model using PSO shows
better performance than without using PSO. This can be seen from the evaluation results in the ANN
HNE + PSO Set 1 scenario obtained an MSE score of 0.185, MAE 0.209, as well as the highest
regression score value obtained in the scenario of 0.904. In the acceleration on displacement dataset
experiment there is an ANN HNN + PSO Set 7 model scenario with an MSE score of 0.035, MAE
0.096, as well as the highest regression score value obtained in that scenario of 0.982. In the
acceleration on velocity experiment there is ANN HNE + PSO Set 2 with an MSE score of 0.000,
MAE 0.001, as well as the highest regression score value obtained in the scenario of 0.999. In
acceleration + displacement there is an ANN HNN + PSO Set 7 scenario with an MSE score of 0.035,
MAE 0.084, as well as the highest regression score value obtained in the scenario of 0.982. In
acceleration + velocity + displacement there is an ANN HNE + PSO Set 2 scenario with an MSE
score of 3.723, MAE 0.001, as well as the highest regression score value obtained in the scenario of
0.999. In acceleration + velocity there is an ANN HNE + PSO Set 2 scenario with an MSE score of
5.018, MAE 0.004, as well as the highest regression score value obtained in the scenario of 0.999.
The other scenarios generated in this study show good performance in general, but with variations in
performance depending on the data set used.
Keyword: ANN, Cianjur, Earthquake, Estimation, PSO