Integrated implementation of fuzzy logic and Dijkstra’s algorithm in travel routes planning

This paper addressed the fuzzy logic and integrated Dijkstra’s algorithm to determine an efficient path for travel routes planning. Dijkstra’s algorithm is the same as Breadth-First Search (BFS) algorithm, i.e. queuing principle, but the queue in Dijkstra’s Algorithm is a priority queue. The research methodology consists of data collection, clustering, fuzzy logic modelling, implementation of the Dijkstra algorithm, and testing the results. The collected data in this paper is the distance and travel time. Data clustering is needed to classify time data based on congestion level because travel time data is dynamic. The results of the clustering become a reference for selecting the time in collecting travel time data. The time represents the current state of the traffic. Fuzzy logic modelling is done after all the required data has been collected. This paper uses the Tsukamoto fuzzy method because this method has tolerance for data and is very flexible. The data is developed into a mathematical model to produce a crisp output (i.e. travel weight). The crisp output is then processed with Dijkstra’s algorithm to produce a solution that solves the problem in this paper. The final result of this research is that fuzzy logic and integrated Dijkstra’s algorithm is ideal for efficient pathfinding because it always gives the most efficient results from all possible paths available with the existing problem constraints.