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All Journal Jurnal Masyarakat Informatika JAIS (Journal of Applied Intelligent System) Sinkron : Jurnal dan Penelitian Teknik Informatika JSiI (Jurnal Sistem Informasi) Journal of Robotics and Control (JRC) Abdimasku : Jurnal Pengabdian Masyarakat Djtechno: Jurnal Teknologi Informasi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Kusumawati, Yupie
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Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Journal : Journal of Robotics and Control (JRC)

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Optimizing Parameters for Earthquake Prediction Using Bi-LSTM and Grey Wolf Optimization on Seismic Data Shidik, Guruh Fajar; Pramunendar, Ricardus Anggi; Purwanto, Purwanto; Hasibuan, Zainal Arifin; Dolphina, Erlin; Kusumawati, Yupie; Sriwinarsih, Nurul Anisa
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22199

Abstract

Earthquakes pose a significant threat to societies worldwide, underscoring the urgent need for advanced prediction technologies. This study introduces an optimization technique aimed at reducing the error rate in earthquake prediction by selecting the most suitable parameters for a Bi-LSTM (Bidirectional Long Short-Term Memory) model. Despite Bi-LSTM's promising outcomes, variations in parameters can impact performance, necessitating careful parameter selection. This research employs Grey Wolf Optimization (GWO) to optimize parameters and evaluates its effectiveness against other group optimization approaches to identify the most efficient parameters for earthquake prediction. Additionally, a multiple input multiple output (MIMO) architecture is implemented to enhance prediction accuracy. The evaluation results demonstrate that GWO outperforms other optimization techniques, achieving a reduced loss score of 0.364. The ANOVA method yields a p-value approaching 0, indicating statistical significance. This study contributes to the development of early warning systems for earthquake disasters by emphasizing the importance of parameter optimization in earthquake prediction and showcasing the effectiveness of Bi-LSTM and GWO methodologies.
Co-Authors Ajib Susanto Alzami, Farrikh Candra Irawan Caturkusuma, Resha Meiranadi Erlin Dolphina Guruh Fajar Shidik Ibnu Utomo WM Indriawati, Aulia Karis Widyatmoko Mulyono, Ibnu Utomo Wahyu Mutiarachim, Atika Nigrum, Novita Kurnia Ningrum, Novita Kurnia Nurchayati Nurchayati Nurhindarto, Aris Purwanto Purwanto Ricardus Anggi Pramunendar Sembiring, Rinawati Sulistiyono, MY Teguh Winarsih, Nurul Anisa Sri Yessy Aviantary Putri Zahrotul Umami, Zahrotul Zainal Arifin Hasibuan