Analysis of Recommendation System on Travel Platform Using Content-Based Filtering and Collaborative Filtering Algorithms at PT. Angkasa Tour & Travel - Prasetyo , Dewo; Muliono, Rizki (Universitas Medan Area, 2025)
This study aims to evaluate the effectiveness of the recommendation system on the PT Angkasa Tour & Travel travel platform using content-based filtering and collaborative filtering algorithms. The background of the identified problem is the need to improve the accuracy and relevance of recommendations in the travel platform, which functions to assist users in choosing travel services that suit their preferences. This research method includes an analysis of the application of the content-based filtering algorithm that focuses on the characteristics of individual users and products, as well as the collaborative filtering algorithm that utilizes collective user behavior patterns. The results of the study indicate that content-based filtering is effective in providing recommendations based on specific user preferences and product attributes, while collaborative filtering is able to produce recommendations based on collective user behavior patterns. This study also reveals that the combination of the two approaches can improve the accuracy and relevance of recommendations, thus better meeting user needs. The conclusion of this study is that the integration of content-based and collaborative filtering in the recommendation system can provide a more comprehensive solution to meet user preferences and needs on the PT Angkasa Tour & Travel travel platform.