TOURIST ATTRACTIONS RECOMMENDER SYSTEM USING COLLABORATIVE FILTERING METHODS AND K-NEAREST NEIGHBORS

Raditya Muhammad; Fauzan Sukmapratama; Nur Azizah Khoirunnisa

doi:10.24042/aisj.v2i2.18529

TOURIST ATTRACTIONS RECOMMENDER SYSTEM USING COLLABORATIVE FILTERING METHODS AND K-NEAREST NEIGHBORS

Raditya Muhammad, Fauzan Sukmapratama, Nur Azizah Khoirunnisa

Abstract

The many tourist attractions and their various types certainly benefit tourists. Bandung is a famous tourist center and is visited by many domestic and foreign tourists. However, the large number of tourist attractions in Bandung often makes it difficult for tourists to determine their destination, especially if they only have limited time. This research aims to design a tourist attraction recommendation system based on collaborative filtering and K-Nearest Neighbors which can help users by providing recommendations for suitable tourist attractions by displaying seven tourist attraction recommendations. By using collaborative filtering, the system will recommend the best tourist attractions based on ratings and reviews given by users on the internet. Based on the RMSE test results, the train and test data values are in the range of 0.2 - 0.5, which shows that the accuracy of the model is good.

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DOI: http://dx.doi.org/10.24042/aisj.v2i2.18529

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