Abstract:
A recommendation system is a technology that can facilitate internet users in
finding things. The recommendations given can be in the form of information, goods,
and others. This system has been widely used in various platforms including social
media, e-commerce, and many others. In this paper, the recommendation system made
is about the recommendation system on books. There are many types of books currently,
books about education, novels or fiction stories, comics, biographies, self-development
and others. However, because of the many choices of books that can be read, readers
will usually find it difficult to choose a book. To overcome this, readers will usually
look for a recommendation through friends or through the internet.
In the recommendation system there are several methods that can be used,
namely content-based filtering, collaborative filtering, and hybrid filtering. Hybrid
recommendation is a combination of two or more recommendation systems. In this
paper the method used is collaborative filtering, the way it works isto add up the ratings
or choices of a product, find user profiles by looking at the rating history given by users,
then generate new recommendations based on comparisons between user patterns. The
algorithm used in this book recommendation system is the K-Nearest Neighbors
algorithm. The K-Nearest Neighbors algorithm is one algorithm that can provide
recommendations with good accuracy. This system helps to save readers time in
searching for books so that readers can make the right decision about the book to be
read next, the recommendations given by the system to users are expected to be in
accordance with the preferences or interests of each reader who uses this application.
