Visualisasi dan Analisis Klaster COVID-19 Tahun 2020 di Indonesia : Studi Berbasis QGIS - Michael Dolly Sianturi; Mery Christyn Lubis; Jogi Nicolas Manihuruk; Gizka Triyunita Sinaga (Pusat riset dan Inovasi Nasional, 2025)
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.