<![CDATA[The hospitality industry utilizes digital reservation systems that generate large volumes of hotel room booking transaction data. However, these data are often not optimally analyzed to support data-driven decision making. This study aims to analyze and compare the performance of the Apriori and FP-Growth algorithms in discovering association patterns in hotel room bookings. The research employs a quantitative approach using Association Rule Mining (ARM) techniques and the CRISP-DM framework on booking transaction data from Sapphire Boutique Hotel. The dataset consists of booking transaction data from Sapphire Boutique Hotel, including room type, additional facilities, booking time, and length of stay attributes. Algorithm performance is evaluated based on computation time, the number of generated association rules, and rule quality measured using support, confidence, and lift values. The results indicate that both algorithms are capable of generating relevant booking patterns; however, FP-Growth demonstrates superior performance in terms of computational efficiency and the number of patterns produced compared to Apriori. These findings are expected to support the development of recommendation systems and data-driven marketing strategies in the hospitality industry.]]>
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