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The advancement of technology is projected to result in the creation of efficient tsunami detection early warning systems to aid individuals, especially those disabilities-friendly, in raising their consciousness and preparing for the worst-case disaster scenarios before they occur. This qualitative descriptive study uses data-gathering procedures based on the library research method. The numerous TEWS has been developed as an effort to recover, rehabilitate, and reconstruct and are carried out in such a way as to anticipate and prepare residents to be more alert and alert to the occurrence of tsunami. IoT based on IMU devices can be utilized as TEWS sensors with minimum limitation. IDSL information concerning elevation is highly correlated with the BIG forecast information. The Android-based received a response time of fewer than five seconds to start receiving with retrieving the tsunami and earthquake data. In conclusion, the EWS needs to be developed along with professional sign- language translators in all catastrophe knowledge as required by the National Regulation on the fundamental rights of individuals with disabilities as part of disclosing information for deaf citizens. Hence, recommendations for further research are needed to develop the TEWS integrated with VBEWS, ViBEWS, ViSEWS, and sign language.

Indonesia is an area with three different volcanic arcs. Almost every year, there are disasters in Indonesia, particularly tsunamis. On the other side, Indonesia has SDGs 2030 focused on disaster management or risk reduction. Based on Structural Equation Modelling (SEM) analysis, this study will look at the part that SDGs education plays in disaster readiness. To attain the research orientation, the researchers developed a tsunami education program and assessed its validity and reliability to find a suitable model. The research approach included a cross-sectional survey to gather quantitative data on the students' replies. The data are valid and expected based on the normal tests. Two of the five indicators from the model influence analysis have been verified, making the model just partially acceptable. However, it is known that only a small number of factors have an impact on other variables based on total effect, direct effect, and indirect effect analyses. Five variables were identified as not significant in the final analysis. Therefore, not all of the model's hypothetical variables can be accepted entirely. The scope of research can be expanded in the future by using more focused variables, a larger sample size, and more respondents.

Since the 2018 Palu earthquake in Central Sulawesi of Indonesia, many researchers worldwide have been concerned about the ground liquefaction phenomenon associated with seismic events. The present study analyzes research trends related to liquefaction through bibliometric methods and examines the contributions of Indonesian authors to the Scopus metadata. In performing this analysis 3,489 documents were identified, 95 of which were from Indonesia. The findings indicated that the number of scientific publications related to ground liquefaction had increased significantly, and that most of these studies were published in the United States. At the same time, the University of Canterbury in New Zealand has been a leading contributor. The most prolific author in this subject was G. Owen. Visualization of liquefaction research trends results in the following five clusters: (1) Reliability and accuracy of the occurrence of liquefaction; (2) Liquefaction consistency; (3) Map and city where such liquefaction occurred; (4) Ground deformation resulting from liquefaction; (5) The country(s) that experienced such liquefaction. The present study helps identify global trends and developments related to ground liquefaction by providing in addition a map for further studies.

Both tsunamis and climate change have a significant impact in many aspects. Therefore, it is necessary to conduct research through scientific publications on the most effective mitigation efforts to reduce these impacts. This study's objectives are to analyze the research profile of climate change and tsunami mitigation using bibliometric analysis. A total of 1750 climate change mitigation documents and 139 tsunami mitigation documents were analyzed. The study results show that the publication of climate change mitigation has increased rapidly in the last decade exponentially, while the publication of tsunami mitigation tends to stagnate. Furthermore, research trends in climate change mitigation, namely emission control, environmental policy, and carbon sequestration, are the most researched mitigation efforts. Meanwhile, the leading causes of climate change are greenhouse gases and carbon dioxide. On the other hand, the trend of tsunami mitigation research is more towards disaster management and mitigation, the causes of tsunamis by earthquakes, and coastal area mitigation objects. The research profile on climate change mitigation follows the 13th goal of the SDGs, namely climate action, while tsunami mitigation follows the 11th goal of the SDGs.

The primary objective research is to obtain information from the Scopus database from 1972 to 2021 related to tsunamis from a mechanical perspective. The preliminary data get from the Scopus database considering that the Scopus indexed documents have gone through a rigorous review process and have credibility in publishing research results. 119 documents (years 1972-2021) get from Scopus data- based obtained based on the keyword Tsunami Mechanism. The VOSViewer application visualizes the research results related to the tsunami mechanism. Of all the papers, there are 80 of 119 documents in the form of articles on Tsunami Mechanisms from 1972 to 2021. Then, the scientist who studied the most tsunamis was Pararas-Carayannis, G., with seven documents. The author is in the first position of all written documents published in International Journals indexed by Scopus. The cities most affected by the tsunami were Japan. The affiliate most associated with tsunamis is The University of Tokyo in Japan. Earth and Planetary Sciences is the subject area in the first position with 77 documents. The keyword that appears the most is "tsunami" in 59 documents. The journal "Pure and Applied Geophysics" was the primary source. The implication of this research is to provide empirical evidence that research related to tsunami is still an exciting topic at present and in the future.

The present study analyzes research trends related to tsunami education and mitigation through bibliometric analyses, and explores the level of contributions of Indonesian researchers in the Scopus database. A total of 1293 documents were identified by this analysis, with 201 originating from Indonesia. The results showed that scientific publications related to tsunami education and mitigation have increased significantly, and that United States has published most of such papers. At the same time, the institution that won a lead role was Tohoku University, in Japan. Three institutions from Indonesia are ranked seventh, fourteenth, and fifteenth, namely the Bandung Institute of Technology, the University of Indonesia, and the University of Gadjah Mada. The most prolific author on this topic is N. Tanaka. Visualization of research trends on tsunami education and mitigation resulted in four clusters: (1) Tsunami waves; (2) Tsunami problems and monitoring; (3) Tsunami early warning systems; (4) Preparedness in dealing with tsunamis, and in the post-tsunami recovery process. The present research can help in the identification of trends related to tsunami education and tsunami impact mitigation, as well as in worldwide development in providing an overview for further study.

This study aims to analyse the Mw 7.0 normal faulting type earthquake that occurred on October 30, 2020, in Izmir-Turkey. After the earthquake, a tsunami was followed with a maximum water level of 3.8 meters in the Aegean Sea. The used method in this study is the measurement of the tsunami faulting model parameters by using a direct procedure. The measured tsunami parameters are the dominance period (Td), rupture duration (Tdur), and rupture duration more than 50 seconds (T50Ex) as well as the results of the multiplication between Td and T50Ex (TdT50Ex). In this study, we used seismogram velocity of vertical component recorded by the Global Seismographic Networks (GSN) network stations and the GEOFON (GE) network with an epicenter distance of 0 to 40°, totaling 66 seismic stations. Based on the measurement results, the tsunami discriminants Tdur = 65.11 seconds, Td = 9.28 seconds, T50Ex = 1.43 and TdT50Ex = 13.27 seconds > 10 seconds (threshold). The value of the tsunami discriminants TdT50Ex that exceeds this threshold strengthens that the normal type earthquake is the cause of the tsunami.

Many communities in Indonesia are potentially affected by tsunamis but do not have adequate preparedness in dealing with such disasters. This study aims to improve the preparedness capacity of the people living around the coast that are potentially affected by the tsunami through tsunami mitigation learning by using OMBAK learning model. The learning is done online to avoid the transmission of covid-19. Before the online learning begins, the student community is asked to work on the tsunami mitigation pre-test. The number of people living around the coast who took part in this learning was 62 people, ranging in age from 13 to 55 years old. Furthermore, students were briefed via a zoom meeting (synchronous) about the implementation of this online learning, especially relating on how to learn tsunami mitigation by using disaster mitigation video media. In this tsunami mitigation learning process, students are allowed to learn asynchronously. We also allow students to discuss tsunami mitigation materials through the WhatApp Group (WAG) during the learning process. After completing the learning process, students were asked to work on post- test questions. Based on the results of the percentage analysis on the learning implementation, it was found that the implementation of tsunami mitigation learning by using OMBAK learning model was carried out very well. In addition, based on the analysis of t-test and gain score from pre- test and post-test data, it was obtained that there was a significant increase in the students’ tsunami mitigation ability.