EARLY WARNING TRAINING MODEL DEVELOPMENT EARTHQUAKE WITH INTERNET OF THINGS BASED ON SETS VALIDATION
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Abstract
The goal of this research is to create an earthquake early warning training model using
the internet of things (IoT) based on SETS (Science, Environment, Technologies, Social).
The research began with the development of an IoT-based early warning tool that uses
Android as a mode of public dissemination of earthquake events. When the application is
finished, it is incorporated into the earthquake early warning training material, where the
training model employs SETS. This training model aims to increase community
preparedness and vigilance in order to save themselves when earthquake information is
disseminated by the created application. The study's findings enabled the development of
an earthquake early warning application called Ewae, as well as the training of the
community using a SETS-based training model. The model development results in six
new syntaxes: a) Organization and Orientation, b) Concept formation, c) Application
introduction, d) Simulating applications, e) Disaster response, and f) Training evaluation.
The Ewae application's output includes model books, instructor manuals, and participant
handbooks.
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