Comparison of machine learning algorithms to predict optimal dwelling time for package tour

Abstract

This paper shows the comparison between several well‐known classification algorithms in Machine Learning with the purpose of finding the most suitable algorithm to predict the dwelling time, that is, how long a certain tourist should stay in a particular tourist spot. This dwelling time prediction can be adopted by tour and travel agents to provide optimal scheduling for their package tours. The algorithm in question is strictly for classification because in this case, the dwelling time does not require a very specific number of minutes; thus, the time can be classified and restricted into several time frames. The origin and features of the dataset are described in this paper as well as the comparison methodology to show the procedure of how the comparison was made. Lastly, the performance results will be used to determine which algorithm to use for this specific case and it will be shown in a form of a graph.

Optimal dwelling time prediction for package tour using K‐nearest neighbor classification algorithm

“…Several classification algorithms were compared in Wahyutama and Hwang [22] to determine the best one for our system. The K NN algorithm provided the best performance and consistency in multiple comparison scenarios, making it suitable for our system.…”