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"Gempabumi yang terjadi akibat pelepasan energi di dalam permukaan bumi akan menghasilkan penjalaran gelombang seismik. Gelombang tersebut akan terekam oleh stasiun penerima yang nantinya dilakukan pemrosesan data sebagai kebutuhan interpretasi dari seismogram. Pada proses pengolahan data salah satunya yaitu penentuan waktu tiba gelombang. Penentuan waktu tiba dari gelombang primer dan sekunder masih dilakukan dengan cara manual oleh operator sehingga memiliki kekurangan seperti waktu yang lama, tingkat subjektivitas yang tinggi dan hasil akurasi yang rendah. Pada penelitian ini dilakukan inovasi dalam penentuan arrival time dengan pendekatan deep learning yaitu menggunakan algoritma Convolutional Neural Network (CNN) dan Long Short Term Memory (LSTM). Program yang dibuat dengan menerapkan kedua algoritma ini akan dilakukan pengujian terhadap data lain. Hasil uji pada program yang sudah dibuat kemudian dilakukan komparasi pada hasil picking dari IRIS Wilber. Uji yang dilakukan menggunakan data dari gempa Palu 28 September 2018. Hasil uji dari program komputer yang dibuat dengan perbandingan picking hasil IRIS Wilber memberikan rata-rata eror sekitar 0.005 dan komparasi waktu dari origin time memiliki perbedaan sekitar 2 detik. Program ini sudah menghasilkan hasil prediksi yang cukup akurat.

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Earthquakes that occur due to the release of energy in the earth's surface will result in the propagation of seismic waves. These waves will be recorded by the receiving station which will later be processed as a result of the interpretation of the seismogram. One of the data processing processes is determining the arrival time of the waves. The determination of the arrival time of the primary and secondary waves is still done manually by the operator so that it has drawbacks such as a long time, a high degree of subjectivity and low accuracy. In this study, innovation was carried out in determining arrival time with a deep learning approach, namely using the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) algorithms. Programs created by applying these two algorithms will be tested on other data. The test results on the program that has been made are then compared to the picking results from IRIS Wilber. The test was carried out using data from the Palu earthquake on 28 September 2018. The test results from a computer program made with a comparison of IRIS Wilber's picking results give an average error of around 0.005 and a comparison of the time from the origin time has a difference of about 2 seconds. This program has produced predictive results that are quite accurate."

"In this research the model of earth layers between earthquake's epicenter in Hokkaido Japan and observation station in Black Forest of Observatory (BFO), Germany is investigated. The earth model is 1-D that represents the average speed model. The earth model is obtained by seismogram comparison between data and synthetic seismogram in time domain and three components simultaneously. Synthetic Seismogram is calculated with the Green's function of the Earth by MINor Integration (GEMINI) program, where program's input is initially the earth model IASPEI91, PREMAN and also the Centroid Moment Tensor (CMT) solution of the earthquake. A Butterworth low-pass filter with corner frequency of 20 mHz is imposed to measured and synthetic seismogram. On seismogram comparison we can find unsystematic discrepancies, covering the travel time and waveform of all wave phases, namely on P, S, SS wave and surface wave of Rayleigh and Love. Solution to the above mentioned discrepancies needs correction to the earth structure, that covering the change of earth crust thickness, the gradient of �?�h and value of zero order coefficient in �?�h and �?�v in upper mantle, to get the fitting on the surface wave of Love and Rayleigh. Further correction to accomplish the discrepancies on body waves is conducted on layers beneath upper mantle down to depth of 630 km, where a little change at speed model of P and S wave is carried out. The number of oscillation amount especially on Love wave is influenced by earth crust depth earth. Good fitting is obtained at phase and amplitude of Love wave, but also at amplitude of some body wave too. This effect is not yet been exploited for the determination of moment tensor."

"Penelitian ini menginvestigasi struktur kecepatan S di Lautan Hindia melalui fitting seismogram, akibat gempa C081499A, Sumatra Selatan dan direkam di stasiun RER, Pulau Reunion, Perancis. seismogram observasi dibandingkan dengan seismogram sintetik dalam domain waktu dan ketiga komponen kartesian secara simultan. Seismogram sintetik dihitung dengan program GEMINI, dimana input awalnya adalah model bumi global Ocean dan PREMAN. Selain itu pada kedua seismogram dikenakan low-pass filter dengan frekuensi corner pada 20 mHz. Analisis seismogram menunjukkan penyimpangan yang sangat kuat pada pengamatan atas waktu tiba, jumlah osilasi dan tinggi amplitudo, pada gelombang permukaan Love dan Rayleigh dan gelombang ruang S. Untuk menyelesaikan simpangan yang dijumpai diperlukan koreksi atas struktur bumi meliputi ketebalan kulit bumi, gradien kecepatan βh dan besar koefisien-koefisien untuk βh dan βv di upper mantle, dan sedikit perubahan pada kecepatan S di lapisan-lapisan bumi hingga kedalaman 400 km. Fitting seismogram diperoleh dengan baik pada waveform fase gelombang, baik waktu tempuh osilasi utama dan jumlah osilasi. Hasil riset ini menunjukkan, bahwa daerah Lautan Hindia mempunyai koreksi atas struktur kecepatan S dengan nilai positif terhadap model lautan. Hasil ini berbeda dengan hasil riset seismologi lainnya.

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The research investigated the S speed of earth structure under Indian Ocean using seismogram fitting, due to the C081499A earthquake, South Sumatra and recorded in the observation station RER at Reunion Island, France. The observed seismogram is compared to its synthetic in time domain and three cartension components simultaneously. Synthetic seismogram is calculated with the GEMINI program, the initial inputs are the global earth models of Ocean and PREMAN. Prior to seismogram comparison, a low-pass filter with corner frequency of 20 mHz is imposed. The result of analysis shows a very strong deviation at the arrival time, oscillation amount and amplitude height of Love and Rayleigh surface waves and S body wave. To overcome the found discrepancies a correction to the earth structure is needed covering the earth crust thickness, speed gradient of βh and zero-order coefficient for the βh and βv in upper mantle, and a little change in S speed in earth layers down to a depth of 400 km. Seismogram fitting is better obtained at waveform of the wave phase, either the travel time or oscillation number of S wave and Love surface wave. The results shows that the Indian Ocean has correction to the S speed structure, which is positive to standard earth model. This result differs from other seismology research."

"In this research the S speed structure is investigated by seismogram analysis of Washington's earthquake, C022801L using data of TUC station, Tucson, Arizona, U.S.A. The seismogram comparison between the observed and the synthetic seismogram is conducted in time domain and three components simultaneously. The initially input for the calculation of synthetic seismogram is earth model of PREMAN and CMT solution from the earthquake. A low-pass Butterworth filter with corner frequency of 20 mHz is convolved to observed and synthetic seismogram. Waveform comparison shows a real deviation when travel time and waveform of some wave phase are compared, namely on S wave, surface wave of Love and Rayleigh and wave ScS and ScS-2. This research shows, how sensitive the waveform is to the earth model, better than the method of travel time or the dispersion analysis. Research hereinafter is addressed to finish the found discrepancies at S wave, surface wave of Love and Rayleigh and ScS and ScS-2 wave, in observation station TUC. To obtain the seismogram fitting, correction for S speed structure in earth model is needed, that are changes of earth crust thickness, the speed model of  in upper mantle covering the speed gradient of h and value of zeroeth order coefficient for the h and v, for accomplishing the discrepancies at surface wave of Love and Rayleigh. Further correction on S speed is conducted to accomplish the deviation at S wave at earth layering systems from Upper Mantle up to a 630 km depth. Mean while for the ScS and ScS-2 wave phase the correction is carried out on S speed in the earth layers up to CMB. Fitting Seismogram is obtained at waveform of various wave phases that is S wave, surface wave of Love and Rayleigh and ScS, ScS-2 wave, either on travel time or especially also at oscillation number in Love wave. This result indicates that the anisotropy is occurred not only in upper mantle but till deeper earth layers, till CMB."

"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."

"Makalah ini memaparkan hasil pengembangan beberapa model acuan untuk menentukan jumlah stasiun pencatat percepatan gempabumi kuat pada tingkatan negara berdasarkan kondisi geografis, demografis, dan sosial-ekonomi. Beberapa model ini dapat digunakan dalam pengembangan lebih lanjut sistem pencatat gempa bumi kuat Indonesia. Dasar pengembangan model adalah sistem serupa di Selandia Baru, Jepang, Taiwan, Iran, Turki, dan Italia. Parameter jumlahstasiun pencatat yang diusulkan adalah jumlah stasiun per 1000 km2luas daratan, dan tiga buah model regresi eksponensial telah dikembangkan berdasarkan fungsi kepadatan penduduk negara, fungsiProduk Domestik Bruto (PDB) per kapita, dan fungsi Indeks Daya-Saing Global (GCI) kelompok Persyaratan Dasar. Berdasarkan tiga modelini, jumlah minimum stasiun pencatat yang dibutuhkan adalah sekitar 750 stasiun.

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AbstractAn empirical study to develop benchmark models at country-level to assess the suggested number of earthquake strong-motion stations based on a framework encompassing geographic, demographic, and socio-economic parameters is reported. The models are to provide a working estimate of the required number of stations for improving the strong-motion instrumentation program of Indonesia. National earthquake strong-motion networks of New Zealand, Japan,Taiwan, Iran, Turkey, and Italy were used as the references.The parameter proposed is the number of stations in land area of 1,000 km2, and three models based on the exponential regression analysis are presented as functions of population density, Gross Domestic Product (GDP) per capita, and the Global Competitiveness Index (GCI) Basic Requirements Index. Using the models, it is suggested that Indonesia would require at least 750 stations."

"Beras merupakan makanan pokok mayoritas masyarakat Indonesia. Jika dibandingkan dengan konsumsi tahun 2019, konsumsi beras nasional meningkat sekitar 4,67 persen pada tahun 2021. Hal ini menunjukan bahwa setiap tahun konsumsi beras nasional akan meningkat karena seiring dengan pertumbuhan jumlah penduduk Indonesia. Sehingga dibutuhkan data produksi beras yang akurat dan tepat waktu untuk dapat menjaga ketersediaan stok beras nasional. Data citra satelit bisa menjadi alternatif untuk memprediksi produksi padi dikarenakan kekurangan yang dimiliki oleh metode survei yang dilakukan oleh BPS yaitu biaya yang cukup tinggi dan terdapat tenggang waktu diseminasi data. Gabungan citra SAR dan Optik dapat meningkatkan akurasi dari model yang dibangun. Selain itu penggunaan model deep learning memiliki akurasi yang lebih baik jika dibandingkan metode machine learning konvensional salah satunya kombinasi CNN dan Bi-LSTM yang mampu mengekstraksi fitur serta memiliki kemampuan untuk memodelkan data temporal dengan baik. Output yang diperoleh dengan menggunakan metode CNNBiLSTM untuk mengklasifikasikan fase pertumbuhan padi, menghasilkan akurasi yang terbaik dengan nilai akurasi 79,57 pada data testing dan 98,20 pada data training serta F1-score 79,78. Dengan menggunakan kombinasi data citra sentinel 1 dan 2 akurasi dari model LSTM dapat ditingkatkan. Selanjutnya akurasi yang didapatkan untuk model regresi produktivitas padi masih kurang baik. Akurasi terbaik dihasilkan oleh model random forest dengan nilai MAPE 0.1336, dan RSME 0,6871.

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Rice is the staple food of the majority of Indonesian people. When compared to consumption in 2019, national rice consumption will increase by around 4.67 percent in 2021. This shows that every year rice consumption will increase in line with the growth of Indonesia's population. So that accurate and timely rice production data is needed to be able to maintain the availability of national rice stocks. Satellite imagery data can be an alternative for predicting rice production due to the drawbacks of the survey method conducted by BPS, which relatively high cost and the time span for data dissemination. The combination of SAR and Optical images can increase the accuracy of the model built. In addition, the use of deep learning models has better accuracy when compared to classical machine learning methods, one of them is the combination of CNN and Bi-LSTM which are able to extract features and have the ability to model temporal data properly. The output obtained using the CNNBiLSTM method to classify rice growth phases, produces the best accuracy with an accuracy value of 79.57 on testing data and 98.20 on training data and an F1-score of 79.78. By using a combination of sentinel 1 and 2 image data, the accuracy of the LSTM model can be improved. Furthermore, the accuracy obtained for the rice production regression model is still not good. The best accuracy was produced by the random forest model with a MAPE value of 0.1336 and RSME of 0.6871."

"Seismometer adalah instrumen penting dalam memantau gempa bumi dan aktivitas seismik lainnya. Namun, kinerjanya dapat menurun seiring waktu karena berbagai faktor, seperti kondisi lingkungan, komponen yang menua, dan gangguan eksternal. Hal ini dapat menyebabkan pengumpulan data yang tidak akurat. Saat ini belum ada metode yang dapat digunakan untuk mengevaluasi kinerja seismometer. Dalam penelitian ini, mengembangkan metode diagnosis kesehatan seismometer yang berbasis pada analisis sinyal seismik.  Metode yang dikembangkan mengunakan model machine learning SVM dan random forest  berdasarkan feature korelasi silang dan  rasio amplitudo,  Metode ini menghasil kan 4 indikator kesehatan yaitu Excellent, Good, Fair dan Poor, Nilai korelasi silang dan rasio amplitudo di dapatkan  melalui korelasi antara 2 jenis sinyal seismik yaitu sinyal seismik target dan beberapa sinyal seismik referensi sehingga dapat diketahui bahwa seismometer yang dalam kondisi sangat bagus memiliki nilai korelasi silang dan rasio amplitudo ± 0.9 – 1. Metode yang digunakan sudah dievaluasi dengan mengunakan 6 event gempa teleseismik : Jepang 2024, Alaska Peninsula 2023, New Caledonia 2023, Turkey 2023, Tongga 2023 dan Solomon 2022 dengan model SVM dan Random Forest untuk mengklasifikasikan kesehatan seismometer didapatkan akurasi 95 % dna 88 %.

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Seismometers are crucial instruments for monitoring earthquakes and other seismic activities. However, their performance can degrade over time due to various factors such as environmental conditions, aging components, and external disturbances. This can lead to inaccurate data collection. Currently, there is no method available to evaluate the performance of seismometers. In this study, we developed a seismometer health diagnosis method based on seismic signal analysis. The developed method uses SVM and random forest machine learning models based on cross-correlation features and amplitude ratios. This method produces four health indicators: Excellent, Good, Fair, and Poor. The cross-correlation values and amplitude ratios are obtained through the correlation between two types of seismic signals, namely the target seismic signal and several reference seismic signals. It can be known that seismometers in excellent condition have cross-correlation values and amplitude ratios of approximately 0.9 – 1. The method used has been evaluated using six teleseismic earthquake events: Japan 2024, Alaska Peninsula 2023, New Caledonia 2023, Turkey 2023, Tonga 2023, and Solomon 2022. Using SVM and Random Forest machine learning models to classify seismometer health, accuracies of 95% and 88% were obtained respectively."