<![CDATA[The hotel prepares rooms and resources according to the room booking. Advance booking from customers is a relationship between customers and hotels that ensures price stability for customers to enjoy services. Cancellation of hotel bookings and inability to satisfy potential customers is a widespread and alarming problem that can increase hotel operating costs and affect customer satisfaction. Given that the impact on the hospitality industry can be very bad, predicting hotel cancellations can be a solution to help build an appropriate operational strategy. Method used in this research is stacking machine learning model. Stacking consists of two levels, where in this study level 0 (base learner) uses the Naive Bayes, Logistic Regression, and Gradient Boosting Machine algorithms while at level 1 (meta learner) uses the Random Forest algorithm. Accuracy value of the stacking model classification and the gradient boosting machine has the highest accuracy value of 0.87. Sensitivity value of the stacking model is 0.86 and is the highest sensitivity value which means that the stacking model classification is very precise in predicting consumers in canceling hotel reservations. Specificity value of the gradient boosting machine is 0.88 and is the highest specificity value, which means that the gradient boosting machine classification is very precise in predicting consumers who do not cancel hotel reservations. Naive bayes and logistic regression classifications have accuracy, sensitivity, specificity, precision values that are not high. ]]>
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