IMPLEMENTATION OF COLLABORATIVE FILTERING ALGORITHM FOR TOURIST SPOT RECOMMENDATIONS

Abstract

Tourist information and diverse tourist interests often confuse tourists in choosing which tourist destinations to visit. Meanwhile, tourist information that is available in printed form or can be accessed online still requires tourists to sort and select for themselves according to their interests and preferences. Moreover, when deciding to visit a tourist spot, sometimes the tourists do not know whether the tourist place they want to see is following their wishes or not. Even tourists feel dissatisfied when visiting tourist attractions because the tourists chose the wrong tourist spot. Therefore, to make it easier to select tourist attractions, a recommendation system is required to give suggestions for tourist attractions according to customer tastes. This thesis aims to implement the Item-based Collaborative Filtering method and K-Nearest Neighbour (KNN) algorithm to the recommendation system to produce recommendations for tourism based on the wishes of tourists. This method aims to predict a certain item for a user based on the preferences of previous users and the reviews of other similar users. In addition to providing recommendations, this system will also provide information related to existing tourism potential. This system will make it easier for tourists to find suitable tourist attractions to feel satisfied and happy.