Open AccessNews recommendation system using collaborative filtering method
Author(s) -
Agung Wahana,
Dian Sa’adillah Maylawati,
B. A. Wiwaha,
Muhammad Ali Ramdhani,
Abdusy Syakur Amin
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1402/7/077010
Subject(s) - collaborative filtering , computer science , rank (graph theory) , information retrieval , recommender system , world wide web , the internet , multimedia , mathematics , combinatorics
In the era of Internet of Things (IoT), various facts, news, and up-to-date information are presented online that can be accessed quickly, anytime, and anywhere. However, sometimes the news which displayed is not up-to-date or not in accordance with the interests of the reader. This study aims to build a system that can recommend the latest news, which is most often accessed, and that is in accordance with the interests of the reader. The method that used in this study is Collaborative Filtering (CF) to rank news as the best recommendation for readers. Based on the results of experiments conducted on 19 examples of news, the percentage of accuracy results of the recommendations was around 84.2% compared to the manual calculation. This shows that CF capable to provide news recommendations good enough.