Analisis Pengguna Media Sosial Terhadap Isu UU Cipta Kerja Menggunakan SNA dan Naive Bayes

Mau, S.; Sembiring, Irwan; Purnomo, Hindriyanto Dwi

doi:10.47065/bits.v4i1.1610

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Nodes are useful for describing an entity such as an individual, group or organization. While edges are useful for describing the relationship that exists between one nodes and other nodes (Rabbani et al, 2020), while according to Mau et al (2022), social network analysis itself is a method that serves to analyze and has a focus on measuring relationships and interactions that are owned and described in some information, and according to Novovanica et al ( 2022) SNA is a mapping and, at the same time, ensures the relationship and flow of relationships between a person, group, or organization. Data from the results of Twitter itself can be analyzed or visualized in the form of a graph.…”

Section: Theory 21 Social Network Analysismentioning

confidence: 99%

Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method

Harman Akbar Tullah,

Muh Akbar,

Alem Febri Sonni

et al. 2023

j. inspir.

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The main objective of this research is to uncover the important role played by the social media platform Twitter in shaping public opinion regarding the 2023 Civil Servant Candidate (CPNS) selection process in Indonesia. Using advanced techniques such as social network analysis and Python language processing, as well as the application of the Naive Bayes algorithm, this research carefully examines the conversation patterns and topic trends prevalent on Twitter during the CPNS selection phase. The findings of this research unequivocally highlight the enormous influence of Twitter on public sentiment related to CPNS selection, as demonstrated by the classification model's impressive accuracy rate of approximately 95.19%. In addition, this research successfully identifies the influential roles played by key actors, prominent accounts, and narratives in shaping public perceptions. These groundbreaking insights foster a comprehensive understanding of the dynamic nature of public opinion in the context of CPNS selection, providing an invaluable basis for designing more effective communication strategies for the government and prospective civil servants.

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Section: Theory 21 Social Network Analysismentioning

confidence: 99%

Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method

Harman Akbar Tullah,

Muh Akbar,

Alem Febri Sonni

et al. 2023

j. inspir.

View full text Add to dashboard Cite

show abstract

“…Further research by (Istiavan, Sembiring, 2022) shows that efforts to map certain issues can be done simply using the SNA method through the aspect of the value of degree centrality and the value of betweenness centrality. In conclusion, although the calculation of closeness centrality has a high value, it does not necessarily have a good value in terms of information dissemination.…”

Section: Introductionmentioning

confidence: 99%

Identification of Netizen Communication regarding the Issue of the National Capital Relocation Plan # IKN Nusantara on Twitter

Herwindo,

Murtiningsih,

Juliadi

2023

J. Komun. Ikat. Sarj. Komun. Indones.

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The plan to move the capital city of the Republic of Indonesia (RI) from DKI Jakarta Province to East Kalimantan Province sparks pros and cons. Various comments appeared on social media. This study aims to: (1) find out the issues carried in the #IKNNusantara hashtag network; (2) categorize comments into negative, positive, and neutral sentiment categories; (3) find the structure and relationships of the actors in the network. The research methodology is quantitative research approach, the SNA (Social Network Analysis) method using NodeXL software and Sentiment Analysis using the online application Brand24.com on the hashtag #IKNNusantara on Twitter. The results of sentiment analysis show that the conversation is dominated by neutral sentiment. The total number of tweets up to February 16, 2022, was 255. SNA results show that the network density between actors in #IKNNusantara is still low at 0.23 percent. The network diameter describes the network pattern spreads and the cohesiveness between actors is low. The most influential actors based on the value of centrality are the accounts @jokowi and @ir with the highest in-degree values, which shows that a lot of talks are directed to this account. Accounts @d_n4bil5 and @newiding30 who have the highest out-degree are the most active in disseminating information. The @jokowi and @d_n4bil5 accounts serve as bridges because they have the highest betweenness value. Accounts @doddyjasmar and @royadam_lubis have the highest closeness values. Furthermore, the @d_n4bil5 and @langonlegend accounts with the highest eigenvector values are the actors with the most links to other actors. With this result, the government needs to continue to monitor the issue continuously and build awareness among netizens and the Indonesian people in general about the importance of IKN development. The use of social media focusing on delivering messages through the 7 (seven) most influential accounts above can have a significant impact. In terms of issue management, the government also needs to consider the most appropriate strategies to respond to issues according to the type of issue and the government's position on the issue.

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“…Furthermore, this study applied to identify influencers on Facebook [7], [8] or Twitter [5], [6] and many other social media platforms. Some recent studies allow this method to be applied in a variety of issues such as to analyze coauthorship networks in academic issues [9], to identify opinion leaders in political issues on the Twitter social network [10], [11], to study the influential actors in a network in the dissemination of information related to vaccines of covid 19 on Twitter conversation [12], [13], to analyze a brand marketing network in economical issue [4], [14], etc. So, in this study, degree centrality, betweenness centrality, and closeness centrality were applied in the case of influencer identification.…”

Section: Introductionmentioning

confidence: 99%

Social Network Analysis of Twitter Users on BTS Topic Using Degree Centrality, Betweenness Centrality, and Closeness Centrality

Adniati,

Irwansyah,

Awanis

2023

InPrime:Ind.Jour.Pure.Applied.Math

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Nowadays, a trademark is starting to be built through content on social media by involving influencers whose roles are increasingly needed in digital marketing. Hence, finding them on social media networks is an important thing. In brand recognition, BTS has a great influence where a brand they collaborate with gets an enthusiastic response from fans who participate in disseminating information and recommending it to others via Twitter. Therefore, this study aims to identify the potential influencer on the delivery of information on the topic of BTS on Twitter using social network analysis. Social network analysis applies the concept of graph theory where the potential influencer which is denoted by the central vertex is measured by measures of centrality, namely degree centrality, betweenness centrality, and closeness centrality. The result of the network consists of 649 vertices and 730 directed edges that form a disconnected and directed network with 67 weakly connected components. This study indicates that the influencers in the network can be fan accounts or fanbase accounts.Keywords: BTS; centrality; central vertex; influencer; social network analysis; Twitter. AbstrakDewasa ini, suatu merek dagang mulai dibangun melalui konten di media sosial dengan melibatkan pemengaruh yang perannya semakin dibutuhkan pada pemasaran digital sehingga menemukan mereka di jaringan media sosial adalah suatu hal yang penting. Dalam pengenalan merek, BTS memberikan pengaruh yang besar dimana suatu merek yang berkolaborasi dengan mereka mendapat respon antusias dari penggemar yang ikut menyebarluaskan informasi dan merekomendasikannya kepada orang lain melalui Twitter. Oleh karena itu, penelitian ini bertujuan untuk mengidentifikasi pemengaruh potensial dalam penyampaian informasi pada topik BTS di Twitter menggunakan analisis jaringan sosial. Analisis jaringan sosial menerapkan konsep teori graf dimana simpul sentral diukur dengan ukuran sentralitas, yaitu sentralitas derajat, sentralitas keantaraan, dan sentralitas kedekatan. Diperoleh jaringan dengan 649 simpul dan 730 sisi berarah yang membentuk jaringan berarah tak terhubung yang terdiri atas 67 komponen terhubung lemah. Adapun hasil dari penelitian ini menunjukkan bahwa simpul sentral atau pemengaruh dalam jaringan dapat berupa akun personal dari pengemar (fan account) atau akun basis penggemar (fanbase).Kata Kunci: analisis jaringan sosial, BTS, pemengaruh , sentralitas, simpul sentral, Twitter. 2020MSC: 05C90, 91D30.

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Analisis Pengguna Media Sosial Terhadap Isu UU Cipta Kerja Menggunakan SNA dan Naive Bayes

Cited by 4 publications

References 6 publications

Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method

Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method

Identification of Netizen Communication regarding the Issue of the National Capital Relocation Plan # IKN Nusantara on Twitter

Social Network Analysis of Twitter Users on BTS Topic Using Degree Centrality, Betweenness Centrality, and Closeness Centrality

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