Utilization of Big Data Analysis Through Public Video, Virus Data Cooperation, and Social Media as the Surveillance to COVID-19 in Indonesia

https://doi.org/10.22146/jsp.56491

Achmad Maulana Sirojjudin(1*)

(1) Department of Communication Studies, Faculty of Social and Political Sciences, University of Indonesia
(*) Corresponding Author

Abstract


This article discusses Big Data's use as a surveillance tool for the spread of Corona Virus Disease 2019 (COVID-19), both in Indonesia and the world. In Indonesia, the range of COVID-19 is increasingly sporadic, causing mass panic and Indonesia's geographical characteristics, which will be difficult when this spread could not control quickly. Researchers are conducting several studies to overcome this pandemic, including supervision, features, handling, mobility, patient interaction, treatment evaluation, and the biological structure. These studies become data and lead to Big Data. This article explores how to use Big Data analysis to monitor the spread of COVID-19 as a communication process that reflects mediated communication as a form of mobility and spatial relationships in communication practices. The method used in this article is a literature review and uses meta-synthesis techniques as its analysis. The literature sources used are articles in highly reputable international journals. Based on the reports, various ways to monitor the virus's spread, through public video data, GPS, and social media tracking, trace the patient's movement. Big Data can also provide data collaboration for viruses and pathogens for further research as digital mediated communication is anchored by the diversity of places and the mobility of people, data, and objects.


Keywords


big data; COVID-19; data cooperation; infectious disease; surveillance

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References

Adhinata, F. D., Ikhsan, M., & Wahyono, W. (2020). People counter on CCTV video using histogram of oriented gradient and Kalman filter methods. Jurnal Teknologi Dan Sistem Komputer, 8(3), 222–227. https:// doi.org/10.14710/jtsiskom.2020.13660

Al-Saggaf, Y., & Islam, M. Z. (2015). Data Mining and Privacy of Social Network Sites’ Users: Implications of the Data Mining Problem. Science and Engineering Ethics, 21(4), 941–966. https://doi.org/10.1007/s11948-014-9564-6

Arditi, D. (2019). Music Ev erywhere: Setting a Digital Music Trap. Critical Sociology, 45(4–5), 617–630. https://doi. org/10.1177/0896920517729192

Asih, D., Teofilus, Sutrisno, T. F. C. W., & Yoana, C. (2020). The effectiveness of social media based on photo and video sharing towards online purchase intention. Jurnal Siasat Bisnis, 24(2), 179–186. https://doi. org/10.20885/jsb.vol24.iss2.art7

Athanesious, J. J., Chakkaravarthy, S. S., Vasuhi, S., & Vaidehi, V. (2019). Trajectory based abnormal event detection in video traffic surveillance using general potential data field with spectral clustering. Multimedia Tools and Applications, 78(14), 19877–19903. https://doi.org/10.1007/s11042-019-7332-y

Bansal, S., Chow ell, G., Simonsen, L., Vespignani, A., & Viboud, C. (2016). Big Data for Infectious Disease Surveillance and Modeling. Journal of Infectious Diseases, 214(suppl 4), S375–S379. https://doi. org/10.1093/infdis/jiw400

Bowman, L., Baras, A., Bombien, R., Califf, R. M., Chen, Z., Gale, C. P., Gaziano, J. M., Grobbee, D. E., Maggioni, A. P., Muse, E. D., Roden, D. M., Schroeder, S., Wallentin, L., & Casadei, B. (2020). Understanding the use of observational and randomized data in cardiovascular medicine. European Heart Journal, 1–8. https://doi.org/10.1093/ eurheartj/ehaa020

Cahya, G. H. (2020). COVID-29 “could infect some 70,000” before Ramadan. In The Jakarta Post (p. 4).

Chakraborty, G. (2019). Evolving profiles of financial risk management in the era of digitization: The tomorrow that began in the past. Journal of Public Affairs. 20(2). https://doi.org/10.1002/pa.2034

Cohen, J. E. (2014). Studying Law Studying Surveillance. Surveillance&Society, 13(1), 91–101. https://doi.org/10.24908/ss.v13i1.5160

Conteh, M.-A., Goldstein, S. T., Wurie, H. R., Gidudu, J., Lisk, D. R., Carter, R. J., Seward, J. F., Hampton, L. M., Wang, D., Andersen, L. E., Arvay, M., Schrag, S. J., Dawson, P., Fombah, A. E., Petrie, C. R., Feikin, D. R., Russell, J. B. W., Lindblad,
R., Kargbo, S. A. S., Mahon, B. E. (2018). Clinical Surveillance and Evaluation of Suspected Ebola Cases in a Vaccine Trial During an Ebola Epidemic: The Sierra Leone Trial to Introduce a Vaccine Against Ebola. The Journal of Infectious Diseases, 217(suppl_1), S33–S39. https:// doi.org/10.1093/infdis/jiy061

Couldry, N., & Mejias, U. A. (2019). Data Colonialism: Rethinking Big Data’ s Relation to the Contemporary Subject. Television & New Media, 20(4), 336–349. https://doi.org/10.1177/1527476418796632

de Orellana, P. (2019). Retrieving how diplomacy writes subjects, space and time: a methodological contribution. European Journal of International Relations , 2 (2), 469-494. https://doi. org/10.1177/1354066119868514

Diallo, A. O., Kiemtoré, T., Bicaba, B. W., Medah, I., Tarbangdo, T. F., Sanou, S., Soeters, H. M., Novak, R. T., & Aké, H. F. (2019). Development and Implementation of a Cloud-Based Meningitis Surveillance and Specimen Tracking System in Burkina Faso, 2018. The Journal of Infectious Diseases,
220(Supplement_4), S198–S205. https://doi.org/10.1093/infdis/jiz376

Frith, J. (2017). Big Data, Technical Com m unic ation, and the Sm art City. Journal of Business and Technical Communication, 31(2), 168–187. https://doi. org/10.1177/1050651916682285

Global Biodefense. (2020). COVID-19 | SARS-CoV-2 Coronavirus Portal. https:// globalbiodefense.com/novel-coronavirus- covid-19-portal/

Guo, L., Vargo, C. J., Pan, Z., Ding, W., & Ishwar, P. (2016). Big Social Data Analytics in Journalism and Mass Communication. Journalism & Mass Communic ation Quarterly, 93(2), 332–359. https://doi. org/10.1177/1077699016639231

Güvenç Paltun, B., Mamitsuka, H., & Kaski, S. (2019). Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. Briefings in Bioinformatics, 00(November), 1–14. https://doi.org/10.1093/bib/bbz153

Hasri, M. I. A., & Santosa, P. B. (2018). The use of Location Based Instagram Data for Tourism Potential Analysis in Kabupaten Gunung Kidul. JGISE: Journal of Geospatial Information Science and Engineering, 1(1). https://doi.org/10.22146/jgise.38469

Helles, R., & Ørmen, J. (2020). Big data and explanation: Reflections on the uses of big data in media and communication research. European Journal of Communication, 35(3), 290–300. https:// doi.org/10.1177/0267323120922088

Henwood, A. F. (2020). Coronavirus disinfection in histopathology. Journal of Histotechnology, 00(00), 1–3. https://doi.or g/10.1080/01478885.2020.1734718

Hu, X., Yuan, Y., Zhu, X., Yang, H., & Xie, K. (2019). Behavioral responses to pre- planned road capacity reduction based on smartphone GPS trajectory data: A functional data analysis approach. Journal of Intelligent Transportation Systems, 23(2), 133–143. https://doi.org/10.1080/15472450.2018.1488133

Huiberts, E. (2020). Watching Disaster News Online and Offline: Audiences Experiencing News about Far-away Disasters in a Postbroadcast Society. Television & New Media, 21(1), 41–59. https://doi.org/10.1177/1527476418821328

Kampf, G., Todt, D., Pfaender, S., & Steinmann, E. (2020). Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents. Journal of Hospital Infection, 104(3), 246–251. https://doi. org/10.1016/j.jhin.2020.01.022

Kraidy, M. M. (2018). Terror, Territoriality, Temporality: Hypermedia Events in the Age of Islamic State. Television & New Media, 19(2), 170–176. https://doi. org/10.1177/1527476417697197

Leal, L. G., David, A., Jarvelin, M.-R., Sebert, S., Männikkö, M., Karhunen, V., Seaby, E., Hoggart, C., & Sternberg, M. J. E. (2019). Identification of disease-associated loci using machine learning for genotype and network data integration. Bioinformatics, 35(24), 5182–5190. https://doi.org/10.1093/bioinformatics/btz310

Leary, H., & Walker, A. (2018). Meta-Analysis and Meta-Synthesis Methodologies: Rigorously Piecing Together Research. TechTrends, 62(5), 525–534. https://doi. org/10.1007/s11528-018-0312-7

Lehtiniemi, T., & Haapoja, J. (2020). Data agency at stake: MyData activism and alternative frames of equal participation. New Media & Society, 22(1), 87–104. https:// doi.org/10.1177/1461444819861955

Lim, M. (2017). Freedom to hate: social media, algorithmic enclaves, and the rise of tribal nationalism in Indonesia. Critical Asian Studies, 49(3), 411–427. https://doi.org/10.1080/14672715.2017.1341188

Madianou, M. (2019). The Biometric Assemblage: Surveillance, Experimentation, Profit, and the Measuring of Refugee Bodies. Television & New Media, 20(6), 581–599. https://doi.org/10.1177/1527476419857682

Madianou, M., & Miller, D. (2013). Polymedia: Towards a new theory of digital media in interpersonal communication. International Journal of Cultural Studies, 16(2), 169–187. https://doi.org/10.1177/1367877912452486

Madjido, M., Espressivo, A., Maula, A. W., Fuad, A., & Hasanbasri, M. (2019). Health Information System Research Situation in Indonesia: A Bibliometric Analysis. Procedia Computer Science, 161, 781–787. https://doi.org/10.1016/j.procs.2019.11.183

Mann, M., & Daly, A. (2019). (Big) Data and the North- in -South: Australia’s Informational Imperialism and Digital Colonialism. Television & New Media, 20(4), 379–395.https://doi.org/10.1177/1527476418806091

Maras, M.-H., & Wandt, A. S. (2019). Enabling mass surveillance: data aggregation in the age of big data and the Internet of Things. Journal of Cyber Policy, 4(2), 160–177. https://doi.org/10.1080/23738871.2019.1590437

Massie, R. G. A. (2019). Akses Pelayanan Kesehatan yang Tersedia pada Penduduk Lanjut Usia W ilay ah P erkotaan di Indonesia. Jurnal Penelitian dan Pengembangan Pelayanan Kesehatan, 46–56. https://doi.org/10.22435/jpppk.v3i1.130

McMahon, A., Buyx, A., & Prainsack, B. (2019). Big Data Governance Needs More Collective Responsibility: The Role of Harm Mitigation in the Governance of Data Use in Medicine and Beyond. Medical Law Review, 0(0), 1–28. https://doi. org/10.1093/medlaw/fwz016

Miller, A. C., Peterson, R. A., Singh, I., Pilewski, S., & Polgreen, P. M. (2019). Improving State-Level Influenza Surveillance by Incorporating Real-Time Smartphone- Connect ed Thermomet er Reading s Across Different Geographic Domains. Open Forum Infectious Diseases. https://doi. org/10.1093/ofid/ofz455

Nakano, D., & Muniz Jr., J. (2018). Writing the literature review for empirical papers. Production , 28 . https://doi. org/10.1590/0103-6513.20170086

Nielsen, R. K., & Schrøder, K. C. (2014). The Relative Importance of Social Media for Accessing, Finding, and Engaging with News. Digital Journalism, 2(4), 472–489. https://doi.org/10.1080/21670811.2013.872420

Nugroho, A. P., Shalih, S. M., Purwantana, B., Sutiarso, L., Radi, Markumningsih, S., Masithoh, R. E., Yun, J. H., Kim, K. U., Yeo, I. C., & Lee, D. G. (2019). Development of GPS-based Tracking System to Evaluate the Effectiveness of Tillage using Four- wheel Tractor. IOP Conference Series: Earth and Environmental Science, 355, 1-26. https://doi.org/10.1088/1755-1315/355/1/012014

Orisa, M., Auliasari, K., & El Maghfiroh, R. (2019). TEKNOLOGI MOTION-BASED TRACKING UNTUK PENGEMBANGAN APLIKASI KEAMANAN. Jurnal Teknologi Informasi Dan Terapan, 4(2), 119–124. https://doi.org/10.25047/jtit.v4i2.69

Perlman, S. (2020). Another Decade, Another Coronavirus. New England Journal of Medicine, 382(8), 760–762. https://doi. org/10.1056/NEJMe2001126

Poom, A., Järv, O., Zook, M., & Toivonen, T. (2020). COVID-19 is spatial: Ensuring that mobile Big Data is used for social good. Big Data & Society, 7(2), 1-7. https://doi. org/10.1177/2053951720952088

Roy, N. (2019). Reviewing ocean governance in Asia. Asian Journal of Comparative Pol itic s , 5 (4), 437-448. https ://d oi. org/10.1177/2057891119883127

Sagala, J. P., Candradewi, I., & Harjoko, A. (2020). Penggunaan Deteksi Gerak untuk P engurangan Ukuran Data Rekaman Video Kamera CCTV. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 10(1), 99. https:// doi.org/10.22146/ijeis.35983

Sapountzi, A., & Psannis, K. E. (2018). Social networking data analysis tools & challenges. Future Generation Computer S ystems , 86 , 893–913. https://doi. org/10.1016/j.future.2016.10.019

Schofield, P. N., Kulka, U., Tapio, S., & Grosche, B. (2019). Big data in radiation biology and epidemiology; an overview of the historical and contemporary landscape of data and biomaterial archives. International Journal of Radiation Biology, 95(7), 861–878. https://doi.org/10.1080/09553002.2019.1589026

Shao, Z., Cai, J., & Wang, Z. (2018). Smart Monitoring Cameras Driven Intelligent Processing to Big Surveillance Video Data. IEEE Transactions on Big Data, 4(1), 105–116. https://doi.org/10.1109/TBDATA.2017.2715815

Simonsen, L., Gog, J. R., Olson, D., & Viboud, C. (2016). Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems. Journal of Infectious Diseases, 214(suppl 4), S380– S385. https://doi.org/10.1093/infdis/jiw376

Sinapuelas, I. C., & Ho, F. N. (2019). Information exchange in social networks for health care. Journal of Consumer Marketing, 36(5), 692–702. https://doi.org/10.1108/JCM-12-2017-2470

Singh, N. (2019). Big data technology: developments in current research and emerging landscape. Enterprise Information Systems, 13(6), 801–831. https://doi.org/10.1080/17517575.2019.1612098

Skare, E . (2019). Digital Surv eillance/ Militant Resistance: Categorizing the “Proto-state Hacker.” Television & New Media, 20(7), 670–685. https://doi. org/10.1177/1527476418793509

Strang, K. D., & Sun, Z. (2017). Analyzing Relationships in Terrorism Big Data Using Hadoop and Statistics. Journal of Computer Information Systems, 57(1), 67–75. https:// doi.org/10.1080/08874417.2016.1181497

Subudhi, B. N., Rout, D. K., & Ghosh, A. (2019). Big data analytics for video surveillance. Multimedia Tools and Applications, 78(18), 26129–26162. https://doi.org/10.1007/s11042-019-07793-w

Vavliakis, K. N., Symeonidis, A. L., & Mitkas, P. A. (2013). Event identification in web s o c ial m ed ia thro ugh nam ed entity recognition and topic modeling. Data & Knowledge Engineering, 88, 1–24. https:// doi.org/10.1016/j.datak.2013.08.006

Wahyuri, W., Athiyah, U., Puspitasari, I., & Nita, Y. (2019). Clustering of Drug Sampling Data to Determine Drug Distribution Patterns with K-Means Method: Study on Central Kalimantan Province, Indonesia. Journal of Information Systems Engineering and Business Intelligence, 5(2), 208. https://doi.org/10.20473/jisebi.5.2.208-218

Wijaya, S. P., Christyono, Y., & Sukiswo, S. (2010). Alat Pelacak Lokasi Berbasis GPS Via Komunikasi Seluler. Transmisi: Jurnal Ilmiah Teknik Elektro, 12(2), 82–86. https://doi.org/https://doi.org/10.12777/ transmisi.12.2.82-86

Witjas-Paalberends, E. R., van Laarhoven, L. P. M., van de Burgwal, L. H. M., Feilzer, J., de Swart, J., Claassen, E., & Jansen, W. T. M. (2018). Challenges and best practices for big data-driven healthcare innovations conducted by profit–non- profit partnerships – a quantitative prioritization. International Journal of Healthcare Management, 11(3), 171–181. https://doi.org/10.1080/20479700.2017.1 371367

Wong, A., Ho, S., Olusanya, O., Antonini, M. V., & Lyness, D. (2020). The use of social media and online communications in times of pandemic COVID-19. Journal of the Intensive Care Society, 175114372096628. https://doi.org/10.1177/1751143720966280

Wu, D., & Lambert, J. H. (2020). Engineering Systems and Risk Analytics. Risk Analysis, 40 (1), 1–7. https://doi.org/10.1111/risa.13433

Wu, J., Li, H., Lin, Z., & Goh, K.-Y. (2017). How big data and analytics reshape the wearable device market – the context of e-health. International Journal of Production Research, 55(17), 5168–5182. https://doi.or g/10.1080/00207543.2015.1059521

Xie, Z. (2021). Mobile communicating place and place-inscribed communicative mobilities: Shaping alternative consumer cultures in mobile media communication. Mobile Media & Communication, 9(1), 51–77. https://doi.org/10.1177/2050157920927451

Yeung, K. (2017). ‘Hypernudge’’: Big Data as a mode of regulation by design.’ Information, Communication & Society, 20(1), 118–136. https://doi.org/10.1080/1369118X.2016.1186713

Yulianto, B., & Layona, R. (2017). An Implement at ion of Locat ion Based Service (LBS) for Community Tracking. ComTech: Computer, Mathematics and Engineering Applications, 8(2), 69. https:// doi.org/10.21512/comtech.v8i2.3749

Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010). Social Media Analytics and Intelligence. IEEE Intelligent Systems, 25(6), 13–16. https://doi.org/10.1109/MIS.2010.151

Zetino, J., & Mendoza, N. (2019). Big Data and Its Utility in Social Work: Learning from the Big Data Revolution in Business and Healthcare. Social Work in Public Health, 34(5), 409–417. https://doi.org/10.1080/19371918.2019.1614508

Zhao, S., Cao, P., Gao, D., Zhuang, Z., Cai, Y., Ran, J., Chong, M. K. C., Wang, K., Lou, Y., Wang, W., Yang, L., He, D., & Wang, M. H. (2020). Serial interval in determining the estimation of reproduction number of the novel coronavirus disease (COVID-19) during the early outbreak. Journal of Travel Medicine, 1–7. https://doi.org/10.1093/jtm/ taaa033

Zhuang, W., & Ismail, M. (2012). Cooperation in wireless communication networks. IEEE Wireless Communications , 19(2), 10–20. https://doi.org/10.1109/MWC.2012.6189408



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