%0 Thesis
%9 Skripsi
%A MUHAMMAD SIDDIQ AFIANTO,  NIM. 07650055
%B FAKULTAS SAINS DAN TEKNOLOGI
%D 2013
%F digilib:12274
%I UIN SUNAN KALIJAGA
%K Keywords: Collaborative filtering, recommendation systems, online gaming store.      
%T RANCANG BANGUN SISTEM REKOMENDASI GAME  MENGGUNAKAN COLLABORATIVE FILTERING  (STUDI KASUS : TOKO ONLINE KIOSK GAMES)    
%U https://digilib.uin-suka.ac.id/id/eprint/12274/
%X Kiosk Games is an online store which is sales of PC games. A large number  of games that they sold, making some customers difficulty in determining the choice  of what game they want to buy and match their tastes. That is why it need for a  recommendation system that is able to give recommendations for game titles to  simplify them in choosing games that will be purchased.  This study uses the Collaborative Filtering, where the system will look for  similarity model of purchase (similiar user) only between customers who are logged  in with another customer. Furthermore, the system will look for customer rating  based on the degree of similarity between the purchase order that already exist. The  more similar games that have been purchased, the higher the rating will be. Then, this  rating will be used to provide recommendations on the value of those games that is  fitting with the customer that is being logged in into the system.  The results of a game recommendation system using a collaborative filtering  method is able to make games title recommendations by providing customer value  rating from largest to smallest based on common purchases among customers who are  logged in with another customer.    
%Z Pembimbing :  M. Mustakim, S.T., M.T.