This study presents the development of a decision tree-based recommendation system designed to enhance the travel experience for tourists. As the tourism industry continues to grow, the need for personalized recommendations that cater to individual preferences becomes increasingly critical. The proposed system utilizes decision tree algorithms to analyze a range of factors, including user demographics, travel history, and destination attributes, to provide tailored suggestions for attractions, accommodations, and activities. By employing a systematic approach to decision-making, the model effectively identifies patterns in user preferences, allowing for real-time recommendations that adapt to changing user inputs. The effectiveness of the system is evaluated through user feedback and comparative analysis with existing recommendation methods, demonstrating its ability to improve user satisfaction and engagement. The findings highlight the potential of decision tree methodologies in creating intelligent, user-friendly applications that cater to the unique needs of tourists, ultimately fostering a more enriching travel experience. This research contributes to the broader field of travel and tourism technology, offering insights into the application of data-driven decision-making in enhancing customer experiences.

This study presents the development of a decision tree-based recommendation system designed to enhance the travel experience for tourists.