Analysis of East Java Tourism Data Using Business Intelligence for Mapping Destination Potential
Keywords:
Business Intelligence, Destination Potential, Spatial Mapping, Open Data, Data ClusteringAbstract
While multidimensional open data offers significant analytical potential for tourism management, transforming it into strategic spatial information remains a methodological challenge. Addressing this gap, this study aims to design a replicable Business Intelligence (BI) architecture to synthesize open tourism data for the objective calculation, clustering, and mapping of destination potential. Employing a quantitative approach, the BI pipeline was applied to data on thousands of destinations across various regencies in East Java. The system standardizes destination potential scores using an expert-based weighting matrix, followed by segmentation via spatial clustering algorithms. Computational results demonstrate that a region's peak potential relies fundamentally on the maturity of its physical infrastructure. Digital marketing acts purely as a multiplier only after the threshold for basic physical accessibility has been met. Overall, this integration of multidimensional evaluation and spatial mapping successfully evolves tourism analytics from descriptive reporting into a robust prescriptive tool for strategic resource allocation. Future research is encouraged to incorporate longitudinal time-series data and machine learning for criteria weighting, thereby evolving this static framework into a dynamic predictive model.
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