Visualization of Covid-19 Data in Indonesia in 2022 through the Google Data Studio Dashboard
DOI:
https://doi.org/10.56313/jictas.v2i1.237Abstract
The COVID-19 pandemic has presented significant challenges to governments, researchers and the general public in understanding and monitoring the spread of this disease. In an effort to analyse the spread of COVID-19 disease in Indonesia effectively, this study uses Google Data Studio as a tool for data visualization and better understanding. This review is based on collecting data on the spread of COVID-19 disease in Indonesia which has been collected from various reliable sources. , including the World Health Organization (WHO) and national health agencies. This data is then processed and processed using Google Data Studio to produce informative visualizations. The results of the study show that Google Data Studio can be used effectively to analyse the spread of the COVID-19 disease in Indonesia. Through the use of available features, such as interactive graphs, maps, and tables, researchers can easily describe patterns of disease spread, infection rates, and recovery rates from an area or country. The quality of data collected from different sources may vary, and this can affect the accuracy and reliability of the resulting visualizations. Elements of the Scorecard that displays some important information related to the Covid-19 pandemic from 1 January 2019 to 31 January 2022. Information regarding the Covid-19 displayed on the Scorecard is as follows. The total survivors of the Covid-19 disease are 3,234,336,858 people. This indicates the number of people who have successfully recovered and recovered from infection with the Covid-19 virus during the period in question. The total number of deaths due to Covid-19 is 89,398,496 people. This reflects the number of people who died due to complications caused by the Covid-19 virus in that period.
References
L. L. Yang and T. Yang, “Pulmonary rehabilitation for patients with coronavirus disease 2019 (COVID-19),” Chronic Diseases and Translational Medicine, vol. 6, no. 2. 2020. doi: 10.1016/j.cdtm.2020.05.002.
S. Arlotto et al., “Life-years lost by COVID-19 patients in public hospitals of Marseille (APHM-South-Eastern France): A limited death toll: A retrospective analysis,” BMJ Open, vol. 11, no. 12, 2021, doi: 10.1136/bmjopen-2021-049475.
M. K. Lo et al., “Remdesivir (GS-5734) protects African green monkeys from Nipah virus challenge,” Sci. Transl. Med., vol. 11, no. 494, 2019, doi: 10.1126/scitranslmed.aau9242.
N. Wang, L. Qin, L. Ma, and H. Yan, “Effect of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) on reproductive system,” Stem Cell Research, vol. 52. 2021. doi: 10.1016/j.scr.2021.102189.
R. Shivakumar and M. Pradeepkumar, “Implementation and effective utilization of analytical tools and techniques in knowledge management,” Int. J. Recent Technol. Eng., vol. 8, no. 2, 2019, doi: 10.35940/ijrte.A9488.078219.
J. Jufri, M. D. H. Gamal, H. Deswita, B. Yanto, and F. Erwis, “Determining Trinomial Coefficient With Ladder Multiplication and Its Construction in Circle and Cone,” 2019, doi: 10.4108/eai.18-7-2019.2288520.
F. L. Gaol, L. Abdillah, and T. Matsuo, “Adoption of Business Intelligence to Support Cost Accounting Based Financial Systems-Case Study of XYZ Company,” Open Eng., vol. 11, no. 1, 2020, doi: 10.1515/eng-2021-0002.
A. Setiawan, B. Yanto, and K. Yasdomi, “The prototype of decision support system in condition infant detection with Fuzzy Tsukamoto,” Int. J. Heal. Sci. Technol., vol. 1, no. 2, 2019, doi: 10.31101/ijhst.v1i2.1100.
H. Krenz, J. Gromoll, T. Darde, F. Chalmel, M. Dugas, and F. Tüttelmann, “The male fertility gene atlas: A web tool for collecting and integrating OMICS data in the context of male infertility,” Hum. Reprod., vol. 35, no. 9, 2020, doi: 10.1093/humrep/deaa155.
R. A. Atmoko, S. R. Asriningtias, and M. B. Persijn, “Training on Using Google Data Studio for Real-Time and Interactive Management of Beji Village Data and Information,” J. Pengabdi. Masy. Bestari, vol. 1, no. 8, 2022, doi: 10.55927/jpmb.v1i8.1710.
D. Apriani, M. Aan, and W. E. Saputra, “Data Visualization Using Google Data Studio,” Int. J. Cyber IT Serv. Manag., vol. 2, no. 1, 2022, doi: 10.34306/ijcitsm.v2i1.68.
A. Husnayain, E. Shim, A. Fuad, and E. C. Y. Su, “Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea from 2020 to 2021: Infodemiology Study,” J. Med. Internet Res., vol. 23, no. 12, 2021, doi: 10.2196/34178.
Z. Csépe et al., “The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region,” Aerobiologia (Bologna)., vol. 36, no. 2, 2020, doi: 10.1007/s10453-019-09615-w.
S. K. Choubey and H. Naman, “A review on use of data science for visualization and prediction of the covid-19 pandemic and early diagnosis of covid-19 using machine learning models,” Stud. Big Data, vol. 80, 2020, doi: 10.1007/978-981-15-8097-0_10.
J. Trujillo-Diaz, C. Amaya, C. González-Uribe, E. Hernandez, A. Herrera, and N. Velasco, “Tracing and measuring the COVID-19 Colombian vaccination network,” in IFAC-PapersOnLine, 2022, vol. 55, no. 10. doi: 10.1016/j.ifacol.2022.10.209.
