UMN Knowledge Center

K-Means Clustering Video Trending di Youtube Amerika Serikat Mencari Pola dan Pengelompokkan Video-video Trending

Widjaja, Kevin and Oetama, Raymond Sunardi (2020) K-Means Clustering Video Trending di Youtube Amerika Serikat Mencari Pola dan Pengelompokkan Video-video Trending. Ultima InfoSys : Jurnal Ilmu Sistem Informasi, 11 (2). pp. 78-84. ISSN 2549-4015 (In Press)

Full text not available from this repository.
Official URL: https://ejournals.umn.ac.id/index.php/SI/article/v...

Abstract

Youtube is the most popular video platform in the world today. Successful YouTubers can create videos that are widely viewed by many Youtube users around the world. A lot of viral videos on Youtube came from the United States. But, making viral videos on Youtube is a tough challenge, both for seasoned YouTubers and especially for new YouTubers. This research focuses on discovering the properties of these viral videos by clustering them into distinct clusters. K-Means algorithm is used for the clustering process. The purpose of this clustering process is to look for patterns in the data that were previously unseen. The result shows that the videos are divided into three clusters which are built from 3 variables; views, likes and dislikes. The patterns and insights found in this study can be useful for aspiring video makers who want to achieve success as a Youtuber.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 004 Computer Science, Data Processing, Hardware
000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods (3D Graphics, Digital Video, Data Mining, Augmented Reality)
600 Technology (Applied Sciences) > 600 Technology > 600 Technology
Divisions: Fakultas Teknik Informatika > Program Studi Sistem Informasi
Depositing User: mr admin umn
Date Deposited: 25 Nov 2021 09:29
Last Modified: 25 Nov 2021 09:29
URI: http://kc.umn.ac.id/id/eprint/19294

Actions (login required)

View Item View Item