Sistem Rekomendasi Destinasi Wisata Menggunakan Algoritma Collaborative Filtering Berbasis Lokasi (LBS)

Abstract

The development of information technology in the tourism sector has resulted in an increasing number of available tourist destinations, making it difficult for users to select destinations that match their preferences and geographical location. This study aims to design and implement a web-based tourist destination recommendation system by integrating Item-Based Collaborative Filtering (ICF) and Location-Based Service (LBS). The study employed a quantitative approach consisting of data collection and preprocessing, user-item matrix construction, similarity calculation using Cosine Similarity, rating prediction, LBS integration using the Haversine Formula, system development, and performance evaluation using Precision, Recall, response time, and spatial accuracy. Testing using interaction data from 50 users and 100 tourist destinations produced a Precision value of 0.84, Recall of 0.78, an average response time of 1.92 seconds, and spatial accuracy of 91%. These results indicate that the integration of ICF and LBS can provide relevant, efficient, diverse, and geographically appropriate recommendations. Therefore, the developed system is effective in helping users obtain more personalized and contextual tourist destination recommendations.