Content-Based Filtering with TF-IDF and Cosine Similarity for Bandung Raya Tourism Recommendation

Abstract

Bandung Raya offers hundreds of tourist destinations spread across Bandung City, Bandung Regency, and West Bandung Regency, which causes information overload and makes it difficult for tourists to select destinations that match their preferences. Recommender systems, as one of the most widely applied branches of machine learning, provide a way to filter such information automatically. This study develops a machine learning-based recommender system for tourism destinations in Bandung Raya using Content-Based Filtering, in which destination descriptions are represented as numerical vectors through TF-IDF weighting and compared using Cosine Similarity. The dataset consists of 331 destinations with name, description, category, and region attributes. Text preprocessing is performed in five stages using the Sastrawi library for the Indonesian language, producing a TF-IDF matrix of 331 by 583 and a similarity matrix of 331 by 331. The model is deployed as a website using Flask as the backend, React as the frontend, and a REST API as the interface, supporting both name-based search and free-text query search. Functional validation uses Black-Box Testing, while recommendation quality is measured using Precision at K and Mean Average Precision. All eight functional scenarios passed, with Precision at 5 of 96.00 percent, Precision at 10 of 90.00 percent, and Mean Average Precision of 98.86 percent, indicating that, for the five evaluated queries, relevant destinations are ranked highly.