Visualisasi dan Analisis Klaster COVID-19 Tahun 2020 di Indonesia : Studi Berbasis QGIS

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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Publication Details

Journal
JURAL RISET RUMPUN ILMU TEKNIK
Published
2025-04-21
DOI
https://doi.org/10.55606/jurritek.v4i1.4388
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

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
JURAL RISET RUMPUN ILMU TEKNIK
Data Mining and Machine Learning Applications
article

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
article en

Abstract

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.

JURAL RISET RUMPUN ILMU TEKNIKVol. 4(1)
State University of Medan (ID)
Openalex Percentile: Top 88%
Data Mining and Machine Learning Applications
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