Predicting and determining antecedent factors of tourist village development using naive bayes and tree algorithm

Abstract

This study aims to predict the progress status of tourism villages in the Kedung Ombo area, Java, Indonesia, and find the antecedent factors of the progress of tourism villages in Indonesia. This study uses a modern approach, namely data mining. Data sources for tourist villages use the data available on the Google link and the observation method. The prediction technique uses the Naïve Bayes machine learning algorithm and Tree Decision on Orange 3.3.0 software. The number of tourist villages analyzed was 126. The results showed that all tourist villages in the Kedung Ombo area were at the development level of the four tourist village classifications of the Ministry of Tourism and Creative Economy. The antecedent factors for the progress of tourism villages are the completeness of ICT facilities, multi-stakeholder partnerships, strong government support, community involvement, and various attractions. Another finding is that the Tree Decision algorithm provides better predictions than the Naïve Bayes method. The results of this study can be used to design policies for developing tourist villages throughout Indonesia.