<![CDATA[Rokan Hulu Regency possesses significant natural and cultural tourism potential; however, tourist visitation rates remain relatively low due to the lack of integrated destination information and concerns regarding environmental sustainability. This study aims to design and develop SMART TOUR, a sequence-prediction–based tourism recommendation system utilizing a deep learning approach. The system is designed to learn tourist travel patterns based on their visitation history by employing a dual-input Long Short-Term Memory (LSTM) model that integrates location and activity data. To address the limitations of real-world data, a synthetic dataset was constructed in a realistic manner by considering tourist preferences, inter-location distances, visit duration, and the popularity of tourist attractions. Experimental results indicate that the proposed recommendation system achieves competitive performance, with a Top-1 Accuracy of 39.7%, Top-3 Accuracy of 76.2%, and Top-5 Accuracy reaching 92.4%. Evaluation was conducted using the Top-K Accuracy approach, which is considered most relevant in the context of recommendation systems as it reflects user behavior in selecting from multiple alternatives. This study demonstrates that integrating LSTM models with predictive techniques can enhance the quality of tourism recommendations and support the development of more environmentally friendly and sustainable tourism practices. SMART TOUR is expected to serve as an innovative solution to strengthen the tourism appeal of Rokan Hulu and empower local communities through the adoption of technology.]]>
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