Expert system and classical probability for setting up hotel's dynamic price level: A case of four-star hotel in Bali

Abstract

Dynamic price in the hospitality industry is a term to indicate the selling prices of hotel rooms that can be changed at certain times dynamically. Based on the author's observation at Mercure Bali Nusa Dua Hotel, the process of deciding the dynamic price level in the hotel is greatly influenced by individuals hence highly subjective, therefore prone to inconsistent pricing mechanism and decision which in turn affects the revenue of the hotel. In this paper, the authors propose expert systems combined with classical probabilities to solve the problem. Fuzzy logic and forward chaining are used to form inference rules that produce a knowledge base of the system. Next, the classical probability is used to calculate the confidence level of the conclusion. The algorithms are tested with the price level of the hotel in 2018. The result shows an initial accuracy of 73.66% with average deviations of 0.36. By classifying the deviations with the rule-based classifier method, the accuracy increases to 90.77%. It is shown that the difference between the actual data is small. The proposed technique potentially increases the hotel's revenue. The usability score of the proposed system is 91.88, indicating the usability of the proposed system is grade A and excellent rating.