Open AccessPrediksi Kerentanan Personal Terhadap Covid 19 dengan Menggunakan Pendekatan Graf
Author(s) -
Nuril Lutvi Azizah,
Uce Indahyanti,
Cindy Cahyaning Astuti
Publication year - 2021
Publication title -
jurnal ilmiah soulm@th : jurnal edukasi pendidikan matematika/jurnal ilmiah soulmath : jurnal edukasi pendidikan matematika
Language(s) - English
Resource type - Journals
eISSN - 2581-1290
pISSN - 2337-9421
DOI - 10.25139/smj.v9i1.3328
Subject(s) - residence , covid-19 , global positioning system , vulnerability (computing) , computer science , graph , location data , geography , statistics , computer security , mathematics , telecommunications , sociology , medicine , demography , disease , pathology , infectious disease (medical specialty) , theoretical computer science
The Covid 19 pandemic happened in arround the world include in Indonesia. It has impacts in many fileds. This research developed to solve the Covid 19 problem. This research requires complex variables based on the varying data in the field. Based on surveys and data, it found that there are 65% of personals know the status of their area in danger zone or safe zone for Covid 19. However, there are still many personals ignore the zone status that has been informed previously by the relevant goverment. The purpose of this study is to determine personal vulnerability to Covid-19 based on zones or regions. Moreover, prediction of vulnerability based on personal distance, the number of personal confirm Covid-19 arround the areas, and other variables such as immunity, and the accuracy of GPS applications. The methods is carried out by creating a vulnerabelity prediction model through GPS tracking based on the position or residence, then create to graph model in shortest path. Initial predictions are given a minimum distance between the personal and individuals confirms is one meter. The result of this research is percentage of personal vulnerability on the number of confirmed Covid 19 detections based on zones or regions. The prediction includes three models such as susceptible, quite susceptible, and safe. Personal susceptible in the percentage arround 90%-100%, quite susceptible in the percentage 75%-90%, and consideres safe in less that 75%.Keywords: prediction, vulnerability, graph