Smart Crowdsensing Berbasis Smart Service untuk Pemulihan Sektor Kesehatan dan Ekonomi di Masa Pandemi
The use of intelligent systems and technology is very much needed during this Covid-19 pandemic. The reason is that there are many sectors that must be rescued immediately, such as health and the economy. On the one hand, the community must be saved from the ongoing epidemic, but on the other hand, economic activity must continue to be pursued even though it is not in ideal conditions. One solution to these problems is the design of Smart Crowdsensing based on smart services that can be used to support physical distancing but still stimulate economic activity to keep moving. In general, smart crowdsensing functions to (i) detect crowds using location data from the user's mobile device GPS, (ii) self-quarantine surveillance and contact tracing using position history in real time, and (iii) collect and process information from citizen interactions. on social media to find out citizen reports related to the pandemic. Smart crowdsensing is designed using a smart service engineering method that allows it to communicate with other services using a REST API.
J. M. Cecilia, J.-C. Cano, E. Hernández-Orallo, C. T. Calafate, dan P. Manzoni, “Mobile crowdsensing approaches to address the COVID-19 pandemic in Spain,” IET Smart Cities, vol. 2, no. 2, hal. 58–63, 2020, doi: 10.1049/iet-smc.2020.0037.
D. Doran, K. Severin, S. Gokhale, dan A. Dagnino, “Social media enabled human sensing for smart cities,” in AI Communications, Jan 2016, vol. 29, no. 1, hal. 57–75, doi: 10.3233/AIC-150683.
R. Jaiswal, A. Agarwal, dan R. Negi, “Smart solution for reducing the COVID-19 risk using smart city technology,” IET Smart Cities, vol. 2, no. 2, hal. 82–88, Jul 2020, doi: 10.1049/iet-smc.2020.0043.
D. Peraković, M. Periša, dan V. Sedlar, “Research of Iot Concept in Monitoring the Activities of the Elderly Person,” Pr, vol. 12, no. February 2016, hal. 66–75, 2015.
S. Barile dan F. Polese, “Smart service Systems and Viable Service Systems: Applying Systems Theory to Service Science,” Serv. Sci., vol. 2, no. 1–2, hal. 21–40, Jun 2010, doi: 10.1287/serv.2.1_2.21.
J. Becker, D. Beverungen, R. Knackstedt, M. Matzner, O. Müller, dan J. Poppelbub, “Bridging the gap between manufacturing and service through IT-based boundary objects,” IEEE Trans. Eng. Manag., vol. 60, no. 3, hal. 468–482, 2013, doi: 10.1109/TEM.2012.2214770.
D. Beverungen, O. Müller, M. Matzner, J. Mendling, dan J. vom Brocke, “Conceptualizing smart service systems,” Electron. Mark., vol. 29, no. 1, hal. 7–18, 2019, doi: 10.1007/s12525-017-0270-5.
B. Guo, “Mobile Crowd Sensing and Computing : The Review of an Emerging Human-Powered Sensing Paradigm Mobile Crowd Sensing and Computing : The Review of an Emerging Human-Powered Sensing Paradigm,” no. August, 2015, doi: 10.1145/2794400.
D. Zhang, B. Guo, dan Z. Yu, “The emergence of social and community intelligence,” Computer, vol. 44, no. 7. hal. 21–28, Jul 2011, doi: 10.1109/MC.2011.65.
D. C. Brabham, “Crowdsourcing as a model for problem solving: An introduction and cases,” Convergence, vol. 14, no. 1, hal. 75–90, 2008, doi: 10.1177/1354856507084420.
F. Burzlaff, N. Wilken, C. Bartelt, dan H. Stuckenschmidt, “Semantic Interoperability Methods for Smart service Systems: A Survey,” IEEE Trans. Eng. Manag., hal. 1–15, 2019, doi: 10.1109/TEM.2019.2922103.