Optimizing the Search for the Nearest Coffee Shop Using the Haversine Algorithm in a Mobile Recommendation System

Abstract

This study aims to optimize the search for nearby coffee shops using the Haversine algorithm in a mobile recommendation system based on Location-Based Filtering (LBF). The system was developed by utilizing GPS, OpenStreetMap, and Firebase to provide real-time coffee shop recommendations according to the user’s location. The research methodology consisted of problem identification, coffee shop location data collection, implementation of the Haversine algorithm for geographic distance calculation, application of the Location-Based Filtering method to sort recommendations based on the nearest distance, and system evaluation using User Acceptance Testing (UAT) and distance accuracy comparison with Google Maps. The results showed that the system was able to calculate location distances with an average accuracy rate of 98.86% compared to Google Maps, with a distance difference ranging only from 0.03 to 0.05 km. In addition, the system successfully provided fast and relevant coffee shop recommendations based on the user’s real-time location. These findings indicate that the combination of the Haversine algorithm and Location-Based Filtering method is effective for implementation in a mobile-based coffee shop recommendation system to improve location search efficiency in real time.