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Ulul Albab, Jauhar Fauzi
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Computer Science & IT

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Recommendation System Yogyakarta Tourism Using TF-IDF and Cosine Similarity Methods with Word Normalizer Ulul Albab, Jauhar Fauzi; Rohman, Arif Nur
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11751

Abstract

The abundance of tourism information in Yogyakarta often overwhelms tourists due to non-standard text data. This research develops a tourism recommendation system using Content-Based Filtering by integrating TF-IDF and Cosine Similarity algorithms, enhanced with a Word Normalizer stage. The research method involves data preprocessing including case folding, filtering, stopword removal, and stemming combined with word normalization to standardize irregular spellings. Text feature representation is calculated using TF-IDF weighting, followed by measuring similarity between destinations through vector-based Cosine Similarity. The query testing of Pantai Parangtritis against Pantai Ngandong yielded the highest similarity score of 0.9397. System performance evaluation showed a Precision@5 of 0.84, Recall@5 of 0.10, and Mean Average Precision (MAP) of 0.81. In conclusion, strengthening the method with a Word Normalizer significantly improves the validity of top-ranked recommendations, enabling tourists to accurately find relevant attractions according to their preferences.
Co-Authors arif nur rohman