Smart Tourism In Padang: Minangkabau Cultural And Gastronomic Recommendations Using Best First Search

Abstract

The city of Padang possesses significant tourism potential, driven by its wealth of natural attractions, Minangkabau culture, and distinctive gastronomy all of which serve as major draws for visitors. However, existing tourism information remains fragmented across various platforms, lacks systematic integration, and fails to provide destination recommendations tailored to user preferences. This situation hinders the implementation of "smart tourism," which requires intelligent, adaptive, and data-driven information systems. This study aims to develop a tourism recommendation model that integrates Padang's tourist destinations, culture, and gastronomy using the Best-First Search algorithm to generate recommendations that are more relevant, efficient, and aligned with traveler needs. The research employs an applied methodology comprising several stages: identifying and classifying data on tourist destinations, culture, and gastronomy; designing a recommendation model based on the Best-First Search algorithm; implementing the system; and testing and evaluating the model's performance using a dataset of Padang's tourist attractions. The results demonstrate that the developed model successfully integrates tourism information in a structured manner and generates destination recommendations that match user preferences based on selected criteria. Furthermore, the Best-First Search algorithm accelerates the process of finding optimal recommendation routes, thereby offering the potential to support the development of smart tourism services that are more effective, interactive, and focused on the visitor experience in Padang.