Evaluasi Kinerja Metode Content-Based Filtering pada Sistem Rekomendasi Destinasi Wisata dan Rute Pendakian Berbasis Web

Abstract

This study aims to evaluate the Content-Based Filtering method in a web-based recommendation system for tourist destinations and hiking routes. The study integrates two domains: 1,621 tourist destinations in Bogor and 126 mountain hiking routes across Java. Tourism data were collected through web scraping from Google Maps, while hiking route data were obtained from Gunungbagging.com and  Muncak.id. The attributes used include category, location, ticket price, facilities, rating, distance, elevation gain, estimated duration, difficulty level, and recommended user experience. Hiking duration was estimated using the Naismith Rule. The system constructs a user profile based on user preferences and computes the similarity between the user profile and item characteristics using Cosine Similarity. For tourism data, a Deep Autoencoder was employed to reduce 54 features into 8 latent dimensions, while the Haversine Formula was applied as a geospatial filter to account for location proximity. Evaluation using Precision@10 yielded average scores of 0.80 for hiking route recommendations and 0.85 for tourist destination recommendations. The results indicate that Content-Based Filtering effectively produces relevant and precise recommendations that align with user preferences and supports more objective and efficient decision-making.