Analisis Aktor Berpengaruh Dan Aktor Popular Dengan Metode Degree Centrality Dan Follower Rank Pada Tagar Twitter “#gejayanmemanggil”

  • Adang Rochiyat Badan Siber dan Sandi Negara
  • Arief Wibowo Universitas Budi Luhur


 The use of Twitter as a social networking media is currently growing rapidly not only used to convey feelings about themselves but can also convey opinions and feelings they feel related to problems that are happening. Twitter is currently considered social media that can be analyzed on the elements of social networking (social network analysis). One study that can be done is the analysis of user accounts (actors) on Twitter. In this research, an analysis has been carried out to find out who the influential actor is and how popular the actor is, on Twitter. The method used for the analysis of influential actors is degree centrality while the analysis of popular actors uses the follower rank method. Case studies were conducted on tweet data containing the hashtag "#gejayanmemanggil". The results showed that RyanResttu is the actor who has the highest influence value with the degree of centrality value of 0.108072 and the actor TirtoID is an actor who has high popularity with a follower rank value of 0.999880312. This research ignores the existence of buzzer accounts on Twitter. However, actors who have high influence value are not necessarily those who have high popularity values.

Author Biography

Arief Wibowo, Universitas Budi Luhur

Program Studi Magister Ilmu Komputer


[1] Agarwal, A., Xie, B., Vovsha, I., Rambow, O. dan Passonneau, R, 2011, Sentiment Analysis of Twitter Data, Proc. of 2011 LSM, Hal 30 - 38.

[2] Zhang, L., Ghosh, R., Dekhil, M., Hsu, M. and Liu, B, 2015, Combining lexicon-based and learning-based methods for Twitter sentiment analysis,” International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE), Hal 1 - 8.

[3] Fortunato, S, 2010, Community detection in graphs, Physics Reports, Hal 75 - 174.

[4] Setatama, M. S, 2012, Implementasi Social Network Analysis dalam Penyebaran Country Branding “Wonderful Indonesia,” Injury Research, 2, Hal 475 - 491.

[5] Taibi, M. dan Jamali Bujang Sanawi, 2017, #Vaccination: Identifying influencers in the vaccaination discussion on Twitter through social network visualisation, International Journal of Bussiness and Society, Hal 18 - 26.

[6] Kadiru, A., Marina, S. dan Abazi-Bexheti, L., 2013, E-Government Exploration using Social Network Analysis Methods, International Journal of Humanities and Management Sciences, Hal 151 - 154.

[7] Gialampoukidis, I., Kalpakis, G., Tsikrika, T., Papadopoulos, S., Vrochidis, S. and Kompatsiaris, I, 2017, Detection of Terrorism-related Twitter Communities using Centrality Scores, Proceedings of the 2nd International Workshop on Multimedia Forensics and Security - MFSec , Hak 21 - 25.

[8] Riquelme, F. and González-Cantergiani, P, 2016, Measuring user influence on Twitter: A survey,” Information Processing and Management, Hal 949 - 975.

[9] Bloch, F., Jackson, M. O. dan Tebaldi, P, 2017, Centrality Measures in Networks, ArXiv,