Analisis Influencer Kesehatan Twitter Indonesia Berbasis Influence Score, Pemodelan Topik, Dan Degree Centrality

Agustine, Eva (2022) Analisis Influencer Kesehatan Twitter Indonesia Berbasis Influence Score, Pemodelan Topik, Dan Degree Centrality. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Twitter telah menjadi salah satu media sosial yang banyak digunakan oleh masyarakat Indonesia untuk berbagi informasi terkait kesehatan, terutama di masa pandemi COVID-19. Kehadiran influencer kesehatan memiliki peran penting dalam menyebarkan informasi kesehatan yang akurat di Twitter. Penelitian ini bertujuan untuk menganalisis influencer kesehatan di Twitter Indonesia berdasarkan tiga metrik yaitu influence score, pemodelan topik, dan degree centrality. Data dikumpulkan dari Twitter menggunakan kata kunci yang berkaitan dengan kesehatan selama periode tertentu. Influence score dihitung untuk mengukur tingkat pengaruh akun, pemodelan topik menggunakan Latent Dirichlet Allocation (LDA) untuk mengidentifikasi topik yang dibahas, dan degree centrality digunakan untuk melihat posisi akun dalam jaringan interaksi. Hasil penelitian menunjukkan bahwa terdapat beberapa influencer kesehatan yang memiliki pengaruh besar dengan topik pembahasan yang beragam seperti edukasi kesehatan, tips pencegahan penyakit, dan informasi mengenai vaksinasi. Analisis degree centrality mengungkapkan bahwa influencer kesehatan seringkali menjadi pusat informasi dalam jaringan interaksi Twitter.
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Twitter has become one of the social media platforms widely used by Indonesians to share health-related information, especially during the COVID-19 pandemic. The presence of health influencers plays a crucial role in disseminating accurate health information on Twitter. This study aims to analyze health influencers on Twitter Indonesia based on three metrics: influence score, topic modeling, and degree centrality. Data were collected from Twitter using health-related keywords over a specific period. The influence score was calculated to measure the level of influence of an account, topic modeling using Latent Dirichlet Allocation (LDA) was employed to identify the topics discussed, and degree centrality was used to observe the position of accounts within the interaction network. The results indicate that there are several health influencers who have significant influence with diverse discussion topics such as health education, disease prevention tips, and information regarding vaccination. Degree centrality analysis reveals that health influencers often become central points of information within the Twitter interaction network.

Item Type:	Thesis (Other)
Additional Information:	RSSI 004.678 Agu a-1 2022
Uncontrolled Keywords:	Analisis Influencer. Twitter Indonesia. Influence Score. Pemodelan Topik. Degree Centrality. Health Influencer Analysis. Twitter Indonesia. Influence Score. Topic Modeling. Degree Centrality.
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD30.213 Management information systems. Dashboards. Enterprise resource planning.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	03 Jun 2026 05:45
Last Modified:	03 Jun 2026 05:45
URI:	http://repository.its.ac.id/id/eprint/133521

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