RANCANG BANGUN SISTEM REKOMENDASI GAME MENGGUNAKAN COLLABORATIVE FILTERING (STUDI KASUS : TOKO ONLINE KIOSK GAMES)

MUHAMMAD SIDDIQ AFIANTO, NIM. 07650055 (2013) RANCANG BANGUN SISTEM REKOMENDASI GAME MENGGUNAKAN COLLABORATIVE FILTERING (STUDI KASUS : TOKO ONLINE KIOSK GAMES). Skripsi thesis, UIN SUNAN KALIJAGA.

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Abstract

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.

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