Tourism Destination Recommendation System Using Collaborative Filtering and Modified Neural Network
Kurniawan Eka Permana, Sri Herawati, Wahyudi Setiawan · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2023
Tourism is one of the driving sectors of the national economy.Nowadays, the normal opening of tourist destinations after COVID-19 pandemic, tourist visits are currently increasing rapidly.Indonesia has a unique culture, nature, language, and cuisine.This is certainly a potential that can attract tourists to visit this archipelago country.To increase the attractiveness of tourism, one of the things that can be done is to create a recommendation system.This system needs to be built to provide users with personalized recommendations based on the input that the user has given.This research uses primary data.The data is a tourist place in the island of Madura.The amount of data consists of 160 tourist places.While the rating is done by 120 users.The system built consists of steps: preprocessing, creating, and training the model based on the data split, calculating the error, and showing destination recommendations to a user.Preprocessing converts "user" and "place" into integer indexes.Creating the model is done by embedding between "user" and "place".The rating is normalized to a number between zero and one using a sigmoid.Furthermore, training data is carried out using Modified Neural Network.The test results show validation_RMSE for each regency (Bangkalan, Sampang, Pamekasan, and Sumenep) is 0.3663, 0.3523, 0.3581, 0.3905.The recommendation system produces seven destinations as recommendations for places that have not been visited by the user.