Classification of Tourist Attractions in Yogyakarta Based on Price and Rating Using the Random Forest Algorithm
DOI:
https://doi.org/10.22303/csrid-.18.2.2026.192-204Keywords:
Classification, Tourist Destination, Yogyakarta, Random Forest, Machine LearningAbstract
Yogyakarta is one of the leading tourist destinations in Indonesia, offering various attractions with diverse
entrance ticket prices and user ratings. However, unstructured information often makes it difficult for tourists
to identify destinations that align with their travel budget and experience preferences. Therefore, this study aims
to classify tourist attractions in Yogyakarta based on price and rating using the Random Forest algorithm. The
dataset used includes entrance ticket prices, user ratings, visit duration, and geographical distance from the city
center of Yogyakarta, allowing a more comprehensive analysis of the relationship between economic value and
visitor satisfaction. Prior to model training, data preprocessing and class balancing were performed using the
SMOTE technique. The model was evaluated using an 80:20 train-test split and 5-fold cross-validation to obtain
more robust and stable performance results. The findings indicate that the features of Price and Rating have the
greatest influence on classification outcomes, while geographical distance also plays a meaningful role. The
proposed model achieves good classification performance and can serve as a foundation for future development
of tourism recommendation systems based on pricing and satisfaction aspects. This research provides a novel
contribution to the application of machine learning in the tourism sector, particularly through the integration
of geographical factors in tourist attraction price classification.
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