Pencarian Jarak Terpendek dari Kampus USU ke Fasilitas Kesehatan di Kota Medan dengan Algoritma Bellman-Ford

Abstract

The Covid-19 pandemic crisis has highlighted the importance of healthcare accessibility in facing public health emergencies. This research focuses on the application of the Bellman-Ford algorithm to find the fastest distance from the University of Sumatera Utara (USU) Campus to various healthcare facilities in Medan City. The main objective is to evaluate the performance of the Bellman-Ford algorithm in determining the quickest route that allows efficient access to healthcare facilities, as well as to measure distance parameters. The scope of this research includes the exclusive use of the Bellman-Ford algorithm to find the shortest distance to healthcare facilities on the USU Campus. This study does not consider factors such as travel time, vehicle speed, or traffic conditions during the journey from the starting point to the destination. The shortest distance analyzed applies only to several healthcare facility locations on the USU Campus, with a total of thirty destinations considered. The Python programming language is utilized in this research, without the involvement of a Database Management System (DBMS). The analysis is conducted using geospatial data and available information on healthcare facility locations. Additionally, this research aims to identify potential obstacles that may arise in the application of the Bellman-Ford algorithm in a complex urban environment like Medan, as well as to evaluate its reliability and efficiency in emergency situations. The results of this study are expected to make a significant contribution to emergency route planning in the future and help improve healthcare accessibility in urban areas, especially during public health crises. Thus, this research is not only academically relevant but also practical in enhancing emergency health response. This research is also expected to serve as a reference for the government and related institutions in optimizing the planning and provision of healthcare services, particularly in facing pandemics and other emergency situations. Furthermore, this research provides insights into the development of more adaptive and responsive algorithms to meet the needs of society in critical situations..