BUSINESS INTELLIGENCE (BI) PRESIDENTIAL CANDIDATES BASED ON SOCIAL NETWORK ANALYSIS (SNA) WITH TWITTER DATA
DOI:
https://doi.org/10.22487/27765660.2024.v4.i2.17143Keywords:
centrality, degree, density, SNAAbstract
The twitter social network is widely used to discuss all kinds of topics, including those related to politics. Analyzing online conversations on Twitter to map the popularity of political figures as candidates for the Indonesian presidential election is a popular and challenging research area. In the Twitter network, citizens can express themselves and communicate with political figures. The conversational data in Twitter is very complex, so Business Intelligence is needed to transform raw data into meaningful and useful information to see the popularity of Indonesian presidential election candidates. The analysis used is Social Network Analysis (SNA) by measuring Degree Centrality, Eigenvector Centrality, Betweenness Centrality, Closeness Centrality. The presidential candidates in this study, Ganjar Pranowo with a twitter account “ganjarpranowoâ€, Puan Maharani with a twitter account “puanmaharani_riâ€, and Anies Baswedan with a twitter account “aniesbaswedanâ€. The actor "aniesbaswedan" excels in the value of degree centrality and betweenness centrality. The “aniesbaswedan†account is the actor who has the most influence on social network interactions based on the total number of interactions generated, then the account also becomes a bridge or liaison in the interactions of other actors in the network.
References
Eriyanto. (2020). Analisis jaringan media sosial. Kencana.
Farisa, F. C. (2019, August 31). Hasil lengkap perolehan kursi DPR 2019-2024. Kompas.com. Retrieved from https://nasional.kompas.com/read/2019/08/31/11152361/hasil-lengkap-perolehan-kursi-dpr-2019-2024
Fatoni, A., & Anestha, P. (2021). Analisis jaringan komunikasi pada percakapan #TetapDukungPSBB di Twitter pada penerapan ke dua PSBB DKI Jakarta. Jurnal Spektrum Komunikasi, 8(2), 177–200. https://doi.org/10.37826/spektrum.v8i2.115
Fitriani, N., Sembiring, I., & Hartomo, K. D. (2020). Evaluasi jaringan dan interaksi tim sukses pileg tahun 2019 melalui social network analysis (SNA). InfoTekJar: Jurnal Nasional Teknologi dan Informasi, 2(14). Retrieved from https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/2348
Hansen, D. L., Shneiderman, B., & Smith, M. A. (2011). Analyzing social media networks with NodeXL. https://doi.org/10.1016/C2009-0-64028-9
Inayah, D., & Purba, F. L. (2020). Implementasi social network analysis dalam penyebaran informasi virus corona (COVID-19) di Twitter. Seminar Nasional Official Statistics 2020: Statistics in the New Normal: A Challenge of Big Data and Official Statistics.
Kamil, I. (2022, June 9). Elektabilitas Ganjar Pranowo teratas, unggul signifikan dari Prabowo dan Anies. Kompas.com. Retrieved from https://nasional.kompas.com/read/2022/06/09/15093321/survei-smrc-elektabilitas-ganjar-pranowo-teratas-unggul-signifikan-dari
Kimball, R., & Ross, M. (2010). The Kimball group reader: Relentlessly practical tools for data warehousing and business intelligence.
Kurniawan, D., Iriani, A., & Manongga, D. (2020). Pemanfaatan social network analysis untuk menganalisis kolaborasi karyawan pada PT. Arum Mandiri Group. Jurnal Transformasi, 17(2), 149. https://doi.org/10.26623/transformatika.v17i2.1646
Loya, T., & Carden, G. (2018). Business intelligence and analytics. https://doi.org/10.4324/9781315206455-12
Maclean, F., Jones, D., Carin-Levy, G., & Hunter, H. (2013). Understanding Twitter. British Journal of Occupational Therapy, 76(6), 295–298. https://doi.org/10.4276/030802213X13706169933021
Needham, M., & Amy, H. E. (2003). Graph algorithms. https://doi.org/10.1142/9789812791245_0012
Pappi, F. U., & Scott, J. (1993). Social network analysis: A handbook. Contemporary Sociology, 22(1), 128. https://doi.org/10.2307/2075047
Salem, M., Aljarrah, E., Alqaraleh, M., & Ali, S. (2021). NodeXL tool for social network analysis. Journal of Information Technology, 12(14), 202–216.
Setatama, M. S., & Tricahyono, D. (2017). Implementasi social network analysis pada penyebaran country branding 'Wonderful Indonesia.' Indonesian Journal of Computing, 2(2), 91. https://doi.org/10.21108/indojc.2017.2.2.183
Susanto, B., Lina, H., & Chrismanto, A. R. (2012). Penerapan social network analysis dalam penentuan centrality studi kasus social network Twitter. Jurnal Informatika, 8(1). https://doi.org/10.21460/inf.2012.81.111
Tahalea, S. P., & SN, A. (2019). Central actor identification of crime group using semantic social network analysis. Indonesian Journal of Information Systems, 2(1), 24–32. https://doi.org/10.24002/ijis.v2i1.2354
Tuhuteru, H., & Iriani, A. (2018). Analisis kolaborasi penelitian ilmiah dosen Fakultas X dengan social network analysis (SNA). Jurnal Teknik Informatika dan Sistem Informasi, 4(1), 149–158. https://doi.org/10.28932/jutisi.v4i1.758
Vercellis, C. (2009). Business intelligence, data mining and optimization for decision making. John Wiley & Sons. https://doi.org/10.1017/CBO9781107415324.004
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press. Retrieved from http://books.google.com
We Are Social. (2021). Digital 2021. Global Digital Insights, p. 103.
Yanuarti, R. (2021). Analisis media sosial Twitter terhadap topik vaksinasi COVID-19. Jurnal Sistem dan Teknologi Informasi, 6(2). Retrieved from http://jurnal.unmuhjember.ac.id /index.php/JUSTINDO
Yuliana, I., Santosa, P. I., & Setiawan, N. A. (2015). Analisis jejaring media sosial untuk pemetaan pada komunitas online.
Yusainy, C., Chawa, A. F., & Kholifah, S. (2017). Social data analytics sebagai metode alternatif dalam riset psikologi. Buletin Psikologi, 25(2), 67–75. https://doi.org/10.22146/buletinpsikologi.27751
Zusrony, E., Purnomo, H. D., & Prasetyo, S. Y. J. (2019). Analisis pemetaan jaringan komunikasi karyawan menggunakan social network analysis pada perusahaan multifinance. INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, 3(2), 145. https://doi.org/10.29407/intensif.v3i2.12786
