Open AccessComparison of machine learning algorithms to predict optimal dwelling time for package tour
Author(s) -
Wahyutama Aria Bisma,
Hwang Mintae
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12651
Subject(s) - computer science , scheduling (production processes) , graph , machine learning , artificial intelligence , algorithm , data mining , mathematical optimization , mathematics , theoretical computer science
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.