Fast Building Identification Using Fuzzy Soft Set Based on Rapid Visual Building (RVS) - Sely Novita Sari; Rizqi Prastowo; Iwan Tri Riyadi Yanto; Korhan Cengiz; Basak Ozyurt; Tuna Topac (CV FOUNDAE, 2022)
Building damage can be caused by disasters such as earthquakes, landslides, etc. To minimize the fatality, the identification of buildings is needed to know the condition of buildings and whether the construction of buildings is able to endure if the disasters happen. This research uses the Rapid Visual Building (RVS) method to identify the building condition. The data are collected fromÂ Kalirejo, Kulon Progo. The survey is conducted by taking a simple building evaluation form (typical of the walls ) based on RVS data. The field assessment results are distinguished into several factors that affect the condition of typical building walls: the foundations, structures, walls, and roofs of the 11 categories on the assessment form. From the data obtained, it is used to classify the building condition using Fuzzy Soft Set. The results show that the classification has been made with good performance in terms of accuracy, precision and time response. The accuracy and recall are close to 100% with above 50% of prevision average and time response is quite 0.0051 second. Thus, it can be used toÂ predict the condition of buildings accurately.