Abstract
In our daily lives, society always live up side by side with disaster. The causes of a disaster can possibly come from either (or both) natural or human & technological factors. Several studies have stated that : improving the disaster preparedness will reduce the impact / risk of disasters. Therefore, an ICT ecosystem will be developed to increase community preparedness in facing earthquakes. Earthquake is a natural phenomenon which is pretty difficult to predict, so that one of the actions that can be taken before a disaster occurs is to increase awareness, for example by analyzing the readiness of buildings to experience earth shocks.In this paper, we will present the process of analyzing textual and image data from the condition of the building, then classifying it based on the level of readiness of the building to receive shocks caused by earthquakes. The classification process will be executed through a perceptual assessment carried out by both lay users and experts in the field of building construction and disaster. The classification results will be the input data for the process of determining the location of the building by applying the Deep Convolutional Neural Network approach. The output of this identification process is a geo-tagged photos that can be used as a reference so that disaster mitigation can be carried out by related parties.
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