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"Penelitian ini akan berfokus pada evaluasi metode-metode sistem peringatan dini gempa bumi yang telah dipublikasikan oleh peneliti-peneliti lainnya dan dapat diakses secara publik. Tujuan dari adanya penelitian ini adalah untuk mengevaluasi kelebihan dan kekurangan dari masing-masing metode dalam memprediksi gelombang P/S, magnitudo, dan lokasi gempa bumi serta memberikan rekomendasi metode apa yang sebaiknya dikembangkan lebih lanjut, terutama untuk sistem peringatan dini gempa bumi di Indonesia. Penulis mengumpulkan data dari ratusan titik seismograf di Indonesia dan menggunakannya sebagai input untuk metode-metode yang digunakan dalam penelitian ini. Evaluasi yang akan dilakukan adalah evaluasi kuantitatif dengan menggunakan metrik-metrik yang sesuai dengan hasil dari metode-metode yang digunakan. Penelitian ini berkontribusi dalam memberikan rekomendasi sistem peringatan dini gempa bumi untuk Indonesia, sehingga dapat meningkatkan kesiapan dan keamanan masyarakat dalam menghadapi bencana alam.

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This research will focus on evaluating earthquake early warning systems that have been published by other researchers and are publicly accessible. The goal of this research is to assess the strengths and weaknesses of each method in predicting P/S waves, magnitude, and earthquake location, as well as to provide recommendations on which methods should be further developed, especially for earthquake early warning systems in Indonesia. I have collected data from hundreds of seismograph stations in Indonesia and used it as input for the methods utilized in this research. The evaluation will be quantitative, using metrics that correspond to the results of the methods employed. This research contributes to providing recommendations for earthquake early warning systems in Indonesia, thereby enhancing the preparedness and safety of the community in facing natural disasters."

"Indonesia terletak pada pertemuan lempeng tektonik aktif, jalur pegunungan aktif, sehingga menjadikan sebagian besar wilayahnya rawan terhadap bencana alam Gempa Bumi. Berdasarkan teori dan konsep manajemen bencana (disasters management) yang meliputi beberapa tahapan, yaitu: tahap tanggap darurat (response phase), tahap rekonstruksi dan rehabilitasi, tahap preventif dan mitigasi, dan tahap kesiapsiagaan (preparedness); maka upaya perencanaan sistem peringatan dini gempa bumi harus dipersiapkan dengan efisien untuk dapat mengurangi risiko yang diakibatkan. Dengan semakin berkembangnya teknologi informasi dan komunikasi berbasis Mobile phone, maka informasi dan komunikasi bukanlah suatu hal yang sulit terutama pada saat tanggap bencana. Dengan variabel-variabel yang diukur berupa media alat peringatan, prosedur penyelamatan diri, mobilisasi sumber daya dan pengetahuan menghadapi bencana gempa bumi. Variabel-variabel tersebut didapat dari studi literatur, kemudian setelah itu didapatkan faktor-faktor yang menyusun sistem peringatan dini (early warning system) gempa bumi yang akan ditambahkan ke dalam aplikasi. Setelah itu, survey dilakukan kepada masyarakat untuk mengetahui pengaruh tambahan fitur EWS (early warning system) bencana gempa bumi. Dari hasil survey yang telah diperoleh, dapat disimpulkan bahwa sebagian besar responden sangat setuju dengan persentase rata-rata dari semua pertanyaan di atas 50% bahwa adanya penambahan fitur earthquake early warning system dapat membuat masyarakat yang menggunakan aplikasi lebih waspada terhadap bencana gempa bumi.

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Indonesia is located at the confluence of active tectonic plates, an active mountain route, thus making most of its territory prone to natural disasters from earthquakes. Based on the theory and concept of disaster management (disasters management) which includes several stages, namely: the response phase, the reconstruction and rehabilitation stage, the preventive and mitigation stage, and the preparedness stage; hence the effort to plan an earthquake early warning system must be prepared efficiently in order to reduce the resulting risks. With the development of mobile phone-based information and communication technology, information and communication is not a difficult matter, especially during disaster response. With the measured variables in the form of media warning tools, self-rescue procedures, mobilization of resources and knowledge of earthquake disasters. These variables are obtained from literature studies, then after that, the factors that compile an earthquake early warning system will be added to the application. After that, a survey was conducted to the public to determine the effect of the additional EWS (early warning system) feature of the earthquake. From the survey results that have been obtained, it can be concluded that the majority of respondents strongly agree with the average percentage of all questions above 50% that the addition of the earthquake early warning system feature can make people using the application more alert to earthquakes."

"Sistem peringatan dini gempa bumi bertujuan memberikan respons cepat segera setelah terdeteksi gelombang P, sebelum gelombang destruktif mencapai permukaan. Penelitian ini mengembangkan model klasifikasi gempa menggunakan arsitektur Convolutional Neural Network (CNN) 1D berbasis data seismometer dari Stanford Earthquake Dataset (STEAD). Deteksi awal kedatangan gelombang P dilakukan menggunakan metode Short-Term Average/Long-Term Average (STA/LTA), kemudian sinyal dipotong dalam beberapa variasi jendela waktu sebagai input model. Model dikembangkan dengan dua parameter ground motion sebagai label klasifikasi biner, yaitu Peak Ground Velocity (PGV) dan Peak Ground Acceleration (PGA), untuk membedakan gempa besar dan kecil. Hasil evaluasi menunjukkan bahwa jendela 3 detik setelah gelombang P memberikan performa terbaik, dengan akurasi sebesar 95,45% untuk PGV dan 94,95% untuk PGA. PGV menunjukkan stabilitas metrik yang lebih baik, terutama dalam mengenali gempa besar. Penelitian ini menunjukkan bahwa informasi pada fase awal sinyal cukup untuk mendukung klasifikasi cepat dan akurat. Penelitian ini diharapkan dapat memberikan kontribusi terhadap pengembangan sistem peringatan dini gempa bumi berbasis deep learning.

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Earthquake early warning systems aim to provide a rapid response immediately after the detection of P-waves, before destructive shaking reaches the surface. This study develops an earthquake classification model using a one-dimensional Convolutional Neural Network (1D CNN) architecture, based on seismometer data from the Stanford Earthquake Dataset (STEAD). The initial detection of P-wave arrivals is performed using the Short-Term Average/Long-Term Average (STA/LTA) method, followed by segmentation of the signal into several time window variations for model input. The model is trained using two ground motion parameters as binary classification labels, namely Peak Ground Velocity (PGV) and Peak Ground Acceleration (PGA), to differentiate between large and small earthquakes. Evaluation results show that the 3-second window after the P-wave arrival yields the best performance, with an accuracy of 95.45% for PGV and 94.95% for PGA. PGV demonstrates better metric stability, particularly in recognizing large earthquakes. These findings suggest that information from the early phase of seismic signals is sufficient to support fast and accurate classification. This study contributes to the advancement of deep learning-based earthquake early warning systems that are both efficient and reliable. "

"Sistem Peringatan Dini Gempa Bumi (EEWS) memiliki peran penting dalam mengurangi risiko yang terkait dengan aktivitas seismik. Penelitian ini telah mengembangkan sebuah kerangka kerja EEWS berbasis deep learning untuk mengestimasi spektra respons percepatan SA(T) langsung dari spektogram sinyal gempa berdurasi pendek setelah kedatangan gelombang-P dari sensor akselerometer. Kerangka kerja yang diusulkan menawarkan metode estimasi tanpa langkah-langkah ekstraksi ciri konvensional setelah pemilihan fase dan memanfaatkan model Variational Autoencoder (VAE) untuk merekonstruksi nilai SA(T) dari input visual spektogram log-Mel. Dataset disusun dari data gempa bumi Indonesia (BMKG) dan Italia (INSTANCE) dengan eksperimen menggunakan potongan sinyal berdurasi 2, 2,5, dan 3 detik. Evaluasi menunjukkan bahwa CNN-DNN VAE dengan durasi 3 detik memberikan performa rata-rata terbaik, dengan skor R2 sebesar 0,9460 pada INSTANCE dan 0,8633 pada BMKG. Sementara itu, performa tertinggi untuk periode pendek T pada 0,01 detik dicapai oleh CNN-LSTM VAE pada data BMKG dengan nilai R2 sebesar 0,9749. Hasil ini menunjukkan bahwa meskipun CNN-DNN VAE umumnya lebih stabil, arsitektur CNN-LSTM VAE efektif pada kondisi tertentu, terutama untuk periode pendek yang cocok untuk bangunan rendah. Penelitian ini menunjukkan bahwa pendekatan spektogram mampu menghasilkan estimasi SA(T) yang cukup akurat serta dapat diterapkan pada EEWS di Indonesia.

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Earthquake Early Warning System (EEWS) possess an important role in reducing risk involving seismic activity. This research has developed a deep learning-based EEWS framework for estimating acceleration response spectra SA(T) straight from the spectrogram of a trimmed earthquake signal shortly after the arrival of P-wave of an accelerometer. The proposed framework offers alternative method without the step of conventional feature extraction after a phase-picking process and uses Variational Autoencoder (VAE) model to reconstruct the SA(T) value from the input of log-Mel spectrogram. The dataset consists of Indonesian earthquake data (BMKG) and Italy (INSTANCE), with the experiment conducted using trimmed signal with 2, 2.5, 3 seconds of time window. The evaluation shows that CNN-DNN VAE with a duration of 3 seconds provides the best average performance, with an R2 score of 0.9460 on INSTANCE and 0.8633 on BMKG. Meanwhile, the highest performance for the short period T at 0.01 second is achieved by CNN-LSTM VAE on BMKG data with an R2 of 0.9749. These results indicate that although CNN-DNN VAE is generally more stable, the CNN-LSTM VAE architecture is effective under certain conditions, especially for short periods suitable for low-rise buildings. This research shows that the spectrogram approach could produce SA(T) estimation quite accurate and can be applied on EEWS in Indonesia."

"This book features chapters based on selected presentations from the International Congress on Advanced Earthquake Resistance of Structures, AERS2016, held in Samsun, Turkey, from 24 to 28 October 2016.It covers the latest advances in three widely popular research areas in Earthquake Engineering: Performance-Based Seismic Design, Seismic Isolation Systems, and Structural Health Monitoring.The book shows the vulnerability of high-rise and seismically isolated buildings to long periods of strong ground motions, and proposes new passive and semi-active structural seismic isolation systems to protect against such effects. These systems are validated through real-time hybrid tests on shaking tables.Structural health monitoring systems provide rapid assessment of structural safety after an earthquake and allow preventive measures to be taken, such as shutting down the elevators and gas lines, before damage occurs. Using the vibration data from instrumented tall buildings, the book demonstrates that large, distant earthquakes and surface waves, which are not accounted for in most attenuation equations, can cause long-duration shaking and damage in tall buildings.The overview of the current performance-based design methodologies includes discussions on the design of tall buildings and the reasons common prescriptive code provisions are not sufficient to address the requirements of tall-building design. In addition, the book explains the modelling and acceptance criteria associated with various performance-based design guidelines, and discusses issues such as selection and scaling of ground motion records, soil-foundation-structure interaction, and seismic instrumentation and peer review needs.The book is of interest to a wide range of professionals in earthquake engineering, including designers, researchers, and graduate students."

"Penelitian ini berfokus pada pengembangan sistem peringatan dini gempa bumi yang memanfaatkan arsitektur event-driven dan model deep-learning. Tujuannya adalah untuk memodelkan data seismik guna mendeteksi gelombang awal, hiposenter, magnitudo, dan kedalaman gempa. Penulis mengumpulkan data dari ratusan titik seismograf dan mengolahnya dengan model deep-learning untuk menghasilkan prediksi yang akurat. Sistem ini dirancang untuk memberikan visualisasi dan informasi yang mendukung Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) dalam mendeteksi aspek-aspek kritis gempa. Selain itu, penulis mengembangkan sistem terdistribusi untuk mengelola permintaan dan pengolahan data skala besar dengan efisiensi tinggi. Antarmuka pemrograman aplikasi (API) juga disajikan untuk memungkinkan prediksi data yang mudah diakses dan dipahami. Terakhir, integrasi antara model machine learning dengan backend dan frontend dirancang untuk memberikan tampilan yang ramah pengguna. Penelitian ini berkontribusi dalam mengembangkan sistem peringatan dini gempa yang lebih canggih dan responsif, sehingga dapat meningkatkan kesiapan dan keamanan masyarakat dalam menghadapi bencana alam.

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This study focuses on the development of an earthquake early warning system utilizing event-driven architecture and deep-learning models. The aim is to model seismic data to detect initial waves, hypocenters, magnitude, and depth of earthquakes. Data from hundreds of seismograph points were collected and processed using deep-learning models to generate accurate predictions. The system is designed to provide visualizations and information to support the Meteorology, Climatology, and Geophysics Agency (BMKG) in detecting critical earthquake aspects. Additionally, a distributed system was developed to manage large-scale data requests and processing efficiently. An Application Programming Interface (API) is also presented for accessible and understandable data predictions. Finally, the integration of machine learning models with backend and frontend is designed to offer a user-friendly display. This research contributes to the development of a more sophisticated and responsive early warning system, enhancing public preparedness and safety in the face of natural disasters."