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This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian inference to generate relevant POI suggestions. As users begin to interact and provide ratings, the system progressively shifts to a collaborative filtering mechanism, leveraging cosine similarity to identify similar users within comparable contexts. The recommender system focuses on three categories of POIs: restaurants, hotels, and landmarks. These locations are retrieved through the Google Maps API, and only mapped locations are considered. The system was implemented on Android devices and evaluated through a user study involving 25 participants from diverse backgrounds, including software developers, IT students, and general users. Evaluation metrics such as normalized Discounted Cumulative Gain (nDCG) and classification accuracy were used to assess recommendation quality. Results demonstrate that the system performs better than traditional methods, with nDCG improvements reaching up to 83 percent. Users reported high satisfaction regarding the recommendations' accuracy, ease of use, and contextual relevance. While the system offers significant improvements, it also has certain limitations. Its dependency on Google Maps data may restrict its scope, and using only four contextual factors limits the system’s adaptability to more complex user preferences. Future enhancements could include additional dynamic contexts such as weather, POI popularity, and time-related trends, as well as integrating more advanced models to increase personalization and flexibility in real-world applications.

The hospitality industry in Makassar continues to develop along with the increasing number of domestic and foreign tourists. RedDoorz Hotel as one of  the providers of budget-friendly accommodation services  faces stiff competition in attracting visitors and maintaining guest loyalty. This study aims to analyze the innovative strategies implemented by Hotel RedDoorz Makassar in increasing the number of visitors and building customer loyalty. The research method used is a qualitative approach with in-depth interview techniques, observations, and secondary data analysis from company reports and customer reviews on digital platforms. The results of the study show that the use of digital promotion through social media, SEO optimization, and collaboration with online booking platforms contribute significantly to the increase in hotel occupancy rates. In addition, the implementation of loyalty programs, the provision of exclusive discounts, and excellent service play an important role in retaining guests who have stayed. The conclusion of this study confirms that an integrated digital promotion strategy and consistent customer service are the main factors in increasing guest attraction and loyalty at Hotel RedDoorz Makassar.