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All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Cyberspace: Jurnal Pendidikan Teknologi Informasi INOVTEK Polbeng - Seri Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL PENDIDIKAN TAMBUSAI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Antivirus : Jurnal Ilmiah Teknik Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JUKANTI (Jurnal Pendidikan Teknologi Informasi) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Mnemonic INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Abdimasku : Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Business and Audit Information System (JBASE) Jurnal Indonesia : Manajemen Informatika dan Komunikasi DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Minfo Polgan (JMP) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Eduvest - Journal of Universal Studies SmartComp Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
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Journal : Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)

Analisis Akun Berpengaruh pada Tagar #MencuriRadenSaleh di Twitter dengan Social Network Analysis Gilang Agung Saputra; Evangs Mailoa
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 3 (2023): JULY-SEPTEMBER 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i3.848

Abstract

Twitter users come from various backgrounds and interests, ranging from just looking for entertainment, information, business needs, or discussion. Many discussion topics can be found on Twitter especially using Hashtags. A topic will form communication with other accounts, one of the hashtags is the hashtag #MencuriRadenSaleh, the hashtag comes from the title of the film Mencuri Raden Saleh which was released on August 25, 2022. This study analyzes the communication network formed by the hashtag #MencuriRadenSaleh using the Social method Network Analysis to find influential accounts in the communication network that is formed, the analysis uses the calculation of degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. The data is in the form of tweets containing the hashtag #MencuriRadenSaleh taken from 15 August 2022 to 25 August 2022 with a total of 9041 nodes and 6064 edges. The results of the research conducted show that the accounts @umayshhhhb, @iqbaal, and @mrs_film are influential accounts in the formation of communication networks and information dissemination with the hashtag #MencuriRadenSaleh, the accounts @umayshhhhb and @iqbaal is owned by the actor Mencuri Raden Saleh, and @mrs_film is the official account of the film.
Analisis Akun Twitter Berpengaruh Terkait Serangan Cyber Bjorka menggunakan Metode Social Network Analysis Adilla, Denise Sheryl; Mailoa, Evangs
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 4 (2023): OCTOBER-DECEMBER 2023
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i4.1005

Abstract

Twitter has become a social media for spreading and viewing the amount of information, such as information recently related to the data theft committed by Bjorka and has become quite a lively conversation on Twitter. The purpose of this research is to find out the most influential accounts related to Bjorka's popularity on social media. This study uses statistical methods of degree centrality, betweenness centrality, and closeness centrality. Based on the research conducted, there is an account that has the highest influence, @conversewhteold  with a degree centrality value of 122, betweenness centrality 0.000067, and a Closeness Centrality 1.0 also from the top 8 accounts, 7 of them tweets revealed that Bjorka had made false publications related to the theft what was done was even falsifying and selling data, then there was 1 account stating that the application made by the government for the public did not have data security guarantees.
Analisis Node dengan Metode Degree Centrality dan Sentiment Analysis dalam Acara G20 Indonesia di Twitter Harnanda, Elifas Gavra; Mailoa, Evangs
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 4 (2023): OCTOBER-DECEMBER 2023
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i4.1022

Abstract

Twitter is a popular social media platform that is used to communicate through exchanging messages and one of them can use hashtag #G20Indonesia. #G20Indonesia is a hashtag originating from the G20 Indonesia event which was held in Bali from 15 to 16 November 2022. It aims to analyze data on research containing "#G20Indonesia". This analysis uses social network analysis and sentiment analysis methods. Analysis of social networks to find the most influential account in the network that is formed, analysis of social networks can use the degree of centrality in the maintenance of the most influential account. Sentiment Analysis to categorize Twitter tweets using AWS Comprehend with 10,001 nodes and 6,839 edges of data taken. The results of research conducted based on the method used are the account @wishnutama which is an influential account based on the communication network formed with #G20Indonesia on Twitter and the sentiment formed is 63.8% for neutral sentiment, 35.4% for positive sentiment and 0.8% for negative sentiment.