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Background: The world economy is affected by the coronavirus disease (COVID-19) pandemic, which affects the coffee industry. Coffee tourism is an emerging new type of tourism in Thailand that is formed in response to the growing demand from visitors with a particular affinity for coffee. Coffee tourism may contribute considerably to the expansion of Thai tourism given proper guidance and assistance. Methods: This study used a stochastic neuro-fuzzy decision tree (SNF-DT) to analyze coffee tourism in Thailand. This research surveyed 400 international and Thai coffee tourists. According to this study, Thai visitors mostly visit coffee tourism locations in Thailand for enjoyment. They also wanted to visit coffee fields to obtain personal knowledge about coffee production and marketing. Responses from foreign coffee tourists indicated that many of their journeys to coffee tourism destinations were entirely for enjoyment rather than business. They also wanted to meet local tour guides and acquire handmade and locally produced things to better understand coffee tourism. Results: According to the study results, coffee tourism management in northern Thailand appears to be well received by international tourists. We also compared the suggested model with the traditional model to demonstrate its efficacy. The performance metrics are the prediction rate, prediction error, and accuracy. The estimated results for our proposed technique are prediction rate (95%), prediction error (97%), and accuracy (94%). Recommendations: Major global businesses such as tourism have been harmed by COVID-19’s unprecedented effects. This study attempts to determine the role of coffee tourism in livelihoods based on real-time data using a machine-learning approach. More research is needed to analyse the factors of the coffee tourism experience using different machine learning approaches. |
