Performance Analysis of the Item-Based Collaborative Filtering Model in Yogyakarta Tourism Recommendations

Abstract

Yogyakarta is one of the most popular tourist destinations in Indonesia, offering a variety of attractions ranging from beaches and mountains to historical sites. This diversity poses a challenge for tourists in selecting destinations that match their preferences. This study employs the Item-Based Collaborative Filtering method to recommend tourist destinations based on the similarity between attractions, calculated using cosine similarity. The data analyzed includes 1,069 tourist destinations in Yogyakarta, obtained from Google Maps API, Scrapetable, and Outscraper. The results indicate that the developed recommendation model achieves high accuracy with a Mean Absolute Error (MAE) of 2.537. Compared to previous approaches, this method improves the relevance and quality of recommendations, helping tourists find destinations that suit their preferences. This study contributes to the development of more personalized and effective recommendation systems for the tourism sector.Yogyakarta is one of the most popular tourist destinations in Indonesia, offering a variety of attractions ranging from beaches and mountains to historical sites. This diversity poses a challenge for tourists in selecting destinations that match their preferences. This study employs the Item-Based Collaborative Filtering method to recommend tourist destinations based on the similarity between attractions, calculated using cosine similarity. The data analyzed includes 1,069 tourist destinations in Yogyakarta, obtained from Google Maps API, Scrapetable, and Outscraper. The results indicate that the developed recommendation model achieves high accuracy with a Mean Absolute Error (MAE) of 2.537. Compared to previous approaches, this method improves the relevance and quality of recommendations, helping tourists find destinations that suit their preferences. This study contributes to the development of more personalized and effective recommendation systems for the tourism sector.