Sistem Informasi Geografis Pemetaan Banjir di Provinsi Jawa Tengah Tahun 2019-2021

Ahmad Subhan; Bambang Agus Herlambang; Ahmad Khoirul Anam

doi:10.62951/router.v3i4.718

Authors

Ahmad Subhan UPGRIS
Bambang Agus Herlambang Universitas PGRI Semarang
Ahmad Khoirul Anam Universitas PGRI Semarang

DOI:

https://doi.org/10.62951/router.v3i4.718

Keywords:

Central Java, Flood Mapping, Geogrhapical Information System, Spatial Analysis, Vulnerability Assessment

Abstract

Flooding is one of the most recurrent natural disasters in Central Java Province, particularly during the rainy season. Diverse geographical characteristics, high rainfall intensity, and rapid urban development contribute to the region’s high vulnerability to flood hazards. According to the Central Java Statistics Agency, a total of 414 flood events and 407,784 affected victims were recorded between 2019 and 2021. This study aims to develop a web-based Geographic Information System (GIS) capable of mapping the spatial distribution and impact levels of floods across Central Java. The methodology includes collecting flood event data from the Central Java Statistics Agency, processing spatial data such as administrative boundary shapefiles, performing attribute integration between spatial and non-spatial datasets, and creating thematic maps using QGIS. The visualization outputs were exported into an interactive web format using the qgis2web plugin and subsequently integrated into a website developed with HTML, CSS, and JavaScript. The results show that the GIS system successfully visualizes flood-prone areas, identifies regions with high flood intensity, and enables users to explore detailed information through interactive digital maps. Additional website features—such as historical flood data, statistical summaries, and descriptive impact indicators—enhance the system's usefulness for disaster analysis. This study demonstrates the crucial role of GIS in supporting disaster mitigation, spatial planning, and policy evaluation related to flood management. Future research is recommended to incorporate more recent datasets and additional non-spatial variables such as rainfall intensity and floodwater depth to improve the system’s analytical accuracy and comprehensiveness.

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