El Aziz, Khalifallah (2026) Optimasi dan Evaluasi Model Ground Motion Prediction Equation (GMPE) untuk Gempa Subduksi Intraslab di Pulau Jawa Berbasis Mixed-Effect Model dengan Marine Predators Algorithm (MPA). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Ground motion prediction equation (GMPE) merupakan model empiris untuk memperkirakan parameter gerakan tanah akibat gempa. Meskipun telah tersedia berbagai model gmpe untuk gempa subduksi intraslab, perbedaan basis data penyusun menyebabkan performanya belum tentu sesuai terhadap karakteristik gempa di pulau Jawa sehingga diperlukan optimasi dan evaluasi model. Oleh karena itu, penelitian ini bertujuan mengoptimasi dan mengevaluasi model Ashadi & Kaka (2019), Zhao et al. (2006), Abrahamson & Gülerce (2020), dan Kuehn et al. (2020) yang kemudian disebut AK19, ZEA06, AG20, dan KBCG20 menggunakan data gempa subduksi intraslab di pulau jawa periode 2020–2024 dengan pendekatan mixed-effect model berbasis one-stage maximum likelihood dengan algoritma particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), grey wolf optimizer (GWO), dan marine predators algorithm (MPA). Selanjutnya, performa algoritma optimasi dievaluasi berdasarkan analisis konvergensi, metrik statistik, boxplot, dan uji wilcoxon. Sedangkan untuk model GMPE hasil optimasi dievaluasi menggunakan analisis korelasi, metrik evaluasi, dan analisis residual. Hasil penelitian menunjukkan bahwa seluruh model mengalami peningkatan performa setelah optimasi. MPA sebagai algoritma utama mampu bersaing dengan algoritma pembanding dalam stabilitas dan konsistensi. Model gmpe terpilih berdasarkan hasil evaluasi model adalah AK19 hasil optimasi menggunakan TLBO karena menunjukkan performa terbaik sebagai model gmpe untuk karakteristik gempa subduksi intraslab di pulau jawa.
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Ground Motion Prediction Equations (GMPEs) are empirical models used to estimate earthquake-induced ground motion. Although several GMPEs have been developed for intraslab subduction earthquakes, differences in the underlying databases may limit their applicability to the seismic characteristics of Java Island. Therefore, this study optimized and evaluated the AK19, ZEA06, AG20, and KBCG20 models using intraslab subduction earthquake records from Java Island during 2020–2024. The optimization employed a One-Stage Maximum Likelihood Mixed-Effect Model with PSO, TLBO, GWO, dan MPA. Algorithm performance was evaluated using convergence analysis, statistical metrics, boxplots, and the Wilcoxon signed-rank test, while the optimized GMPE models were assessed using correlation analysis, evaluation metrics, and residual analysis. The results show that all models improved after optimization. As the proposed optimization algorithm, MPA demonstrated competitive stability and consistency compared with the benchmark algorithms. Based on the overall model evaluation, the AK19 model optimized using MPA achieved the best performance and is recommended as the most representative GMPE for intraslab subduction earthquakes in Java Island.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Ground Motion Prediction Equation (GMPE), Intraslab Earthquake, Mixed-effect Model, Metaheuristic Optimization, One Stage Maximum Log-likelihood.,Ground Motion Prediction Equation (GMPE), Intraslab Earthquake, Mixed-effect Model, Metaheuristic Optimization, One Stage Maximum Log-likelihood. |
| Subjects: | G Geography. Anthropology. Recreation > GB Physical geography Q Science > QA Mathematics > QA9.58 Algorithms Q Science > QC Physics Q Science > QE Geology > QE538.8 Earthquakes. Seismology Q Science > QE Geology > QE539.2.S4 Seismic models |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Physics > 45201-(S1) Undergraduate Thesis |
| Depositing User: | Khalifallah El Aziz |
| Date Deposited: | 04 Aug 2026 02:10 |
| Last Modified: | 04 Aug 2026 02:10 |
| URI: | http://repository.its.ac.id/id/eprint/142207 |
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