Visualisasi dan Analisis Klaster COVID-19 Tahun 2020 di Indonesia : Studi Berbasis QGIS
DOI:
https://doi.org/10.55606/jurritek.v4i1.4388Keywords:
COVID-19, Spatial Visualization, QGIS, Cluster AnalysisAbstract
This study aims to analyze and visualize the distribution of COVID-19 cases in Indonesia throughout 2020 with a spatial-based quantitative approach. The data used was obtained from the official report of the Ministry of Health of the Republic of Indonesia as of December 30, 2020, including the number of confirmed cases, recovered, and died. The analysis was carried out by integrating clustering methods and Geographic Information Systems (GIS) using Quantum GIS (QGIS) software. The visualization results show significant spatial variations between provinces, where provinces with high population density such as DKI Jakarta, West Java, East Java, and Central Java are recorded as areas with the highest caseload. In addition, areas with limited health facilities also show a high potential risk of transmission and death. Cluster patterns of positive and cured cases generally show similarities, while mortality rates show spatial inequalities that are important to look at. These findings emphasize the importance of spatial data integration in area-based policy planning for pandemic control. Spatial visualization not only facilitates understanding of distribution patterns, but also supports more effective and targeted decision-making.
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References
Dahlia, S. (2021). Analisis pola spasial pesebaran kasus Covid-19 menggunakan sistem informasi geografis di DKI Jakarta. Jurnal Geografi, Edukasi dan Lingkungan (JGEL), 5(2), 101–108. https://doi.org/10.22236/jgel.v5i2.7098
Devi, A. M., Adisanjaya, N. N., & Wasita, R. R. R. (2024). Pemetaan kasus Covid-19 dengan menggunakan sistem informasi geografis di Kabupaten Badung Provinsi Bali tahun 2021. Jurnal Bidang Ilmu Kesehatan, 14(1), 14–21. https://doi.org/10.52643/jbik.v14i1.3113
Fahri, M. U. (2020). Melihat peta penyebaran pasien Covid-19 dengan kombinasi QGIS dan framework Laravel. Jurnal Teknologi Terpadu, 6(1), 25–30.
Ferryza Nurwahyu, Riyandi, S., Nifansa, A. R., & Irsyad, A. (2023). Sistem informasi geografis pemetaan Indomaret di Kecamatan Tenggarong berbasis Quantum GIS. Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi (PETISI), 1(2), 51–54.
Hakim, H., Sarjan, M., & Jalal, S. (2021). Sistem informasi sebaran data pemantauan COVID-19 berbasis GIS di Kabupaten Majene. Peguruang: Conference Series, 3(2), 615–619.
Hastuti, N., & Djanah, S. N. (2020). Studi tinjauan pustaka: Penularan dan pencegahan penyebaran Covid-19. An-Nadaa: Jurnal Kesehatan Masyarakat, 7(2), 70–76. https://doi.org/10.31602/ann.v7i2.2984
Ibnu Praditya, M., Apdila, I., Irsyad, A., & Ibrahim, M. R. (2023). Implementasi QGIS dalam pemetaan sebaran mall di Kota Samarinda. Kreatif Teknologi dan Sistem Informasi (KRITISI), 1(1), 19–22. https://doi.org/10.30872/kretisi.v2i1.1063
Islam, A., Sayeed, M. A., Rahman, M. K., Ferdous, J., Islam, S., & Hassan, M. M. (2021). Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh. Transboundary and Emerging Diseases, 68(6), 3643–3657. https://doi.org/10.1111/tbed.13973
Kandou, G. D., Ratag, B. T., Sekeon, S. A. S., & Kandou, P. C. (2021). Spatial analysis of COVID-19 in North Minahasa Regency of North Sulawesi Province. International Journal of Community Medicine and Public Health, 9(1), 1–5. https://doi.org/10.18203/2394-6040.ijcmph20214836
Oktaviandra, R., & Yulfa, A. (2022). Pengelolahan data pasien positif COVID-19 di Kota Bukittinggi berbasis WEBGIS. Jurnal Buana, 6(3), 717–730.
Rembulan, G. D., Wijaya, T., Palullungan, D., Alfina, K. N., & Qurthuby, M. (2020). Kebijakan pemerintah mengenai Coronavirus Disease (COVID-19) di setiap provinsi di Indonesia berdasarkan analisis klaster. JIEMS (Journal of Industrial Engineering and Management Systems), 13(2), 74–86. https://doi.org/10.30813/jiems.v13i2.2280
Safitri, D. R., Sarwani, D., Rejeki, S., Nurlaela, S., & Jayanti, R. D. (2025). Mapping and clustering COVID-19 cases in Kudus District. Disease Prevention and Public Health Journal, 19(1), 1–10.
Styawan Agus, D. (2020). Pandemi COVID-19 dalam perspektif demografi. Dalam Seminar Nasional Official Statistics (hlm. 182–189).
Sun, M., & Jiao, X. (2022). Identification of spaces with cluster infection risks in small cities in China based on spatial syntax and GIS. Journal of Computational Methods in Sciences and Engineering, 22(4), 1081–1097. https://doi.org/10.3233/JCM-226080
Syukira, N., Utomo, S. S., Rakhmayanti, D., Herdyan, R. D., Rohmat, X. A., Rifki, M. F. N., Saputra, A., Fatiyah, S., & Liyantono. (2024). Pemetaan lahan pertanian berbasis data spasial menggunakan aplikasi QGIS di Desa Mojorembun Kecamatan Rejoso. Jurnal Pusat Inovasi Masyarakat, 6(2), 146–154. https://doi.org/10.30872/kretisi.v2i1.1063
Tanjung, M. S., & Sitepu, R. (2021). Epidemiologi deskriptif Coronavirus Disease 2019 (Covid-19) di Indonesia pada tahun 2020. Ibnu Sina: Jurnal Kedokteran dan Kesehatan - Fakultas Kedokteran Universitas Islam Sumatera Utara, 20(2), 179–191. https://doi.org/10.30743/ibnusina.v20i2.190
Widiawaty, M. A., Lam, K. C., Dede, M., & Asnawi, N. H. (2022). Spatial differentiation and determinants of COVID-19 in Indonesia. BMC Public Health, 22(1), 1–16. https://doi.org/10.1186/s12889-022-13316-4
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