Open AccessInformation System for Disaster Mitigation Using Google Data Traffic
Author(s) -
Handaru Jati,
Muhammad Izzuddin Mahali,
Satriyo Agung Dewanto
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2111/1/012017
Subject(s) - data collection , computer science , disaster area , emergency management , process (computing) , information system , agency (philosophy) , transport engineering , data mining , operations research , computer security , geography , engineering , philosophy , electrical engineering , epistemology , statistics , mathematics , meteorology , political science , law , operating system
The Head of the Data Information Center and Public Relations of the National Disaster Management Agency predicts the potential for forest fires in 2018 will increase. The areas that are burned are border areas, both provincial/district/city borders because border areas are poorly supervised. This study aims to (1) Develop an Information System that can be used for Disaster Mitigation Using Google Data Traffic (2) Ensure that the developed Information System is suitable for use for Disaster Mitigation Using Google Data Traffic to obtain accurate traffic data. Google Data Traffic is a giant database used by the Google company to provide real-time traffic information through the Google Maps application. Google Maps uses multiple iterations of satellite cameras and uses Google Earth to map traffic including congestion information and the shortest possible path alternatives. The development method chosen is the Rational Unified Process (RUP). This research uses several methods in data collection including observation, interviews, and questionnaires. Interviews were conducted with prospective users of the application, namely firefighter drivers and people in disaster-prone areas. Questionnaires are used to provide a set of questions to respondents. In the determination test, the recall and precision values are calculated from the data generated by the developed system. While the function test uses descriptive data analysis techniques with the percentage feasibility formula. This research produces an application that can assist firefighters in carrying out their daily duties to be more effective and efficient and the test results on this application show that it is declared feasible in terms of efficiency, performance and compatibility.