RANCANG BANGUN SISTEM REKOMENDASI BUKU BERBASIS ITEM-BASED COLLABORATIVE FILTERING MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS
DOI:
https://doi.org/10.47111/jti.v19i1.18829Keywords:
K Nearests Neighbors, Machine Learning, , Recommendation SystemAbstract
This research aims to design and develop a book recommendation system using the item-based collaborative filtering method with the K-Nearest Neighbors algorithm. The K-Nearest Neighbors algorithm was chosen because of its ability to measure similarity between items based on distance calculations, thereby providing relevant recommendations for users. The method involves processing user rating data for books, followed by the development of a model using an item-based approach to find similar books. The model is tested using cross-validation techniques with evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to measure the accuracy of the recommendations. The test results indicate that the proposed model has a good level of accuracy, with an average RMSE value of 0.8191 and an MAE of 0.6235. This system is expected to enhance the user experience in finding books that match their preferences and to contribute significantly to the development of machine learning-based recommendation systems.
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