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All Journal Media Komunikasi Geografi GEOGRAPHY : Jurnal Kajian, Penelitian dan Pengembangan Pendidikan
Hermawan Setiawan
UIN Syarif Hidayatullah Jakarta
Earth & Planetary Sciences Education Social Sciences

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SPATIAL ANALYSIS OF FLOOD SUSCEPTIBILITY AND ITS IMPACT ON EDUCATIONAL FACILITIES IN TANGERANG CITY Hermawan Setiawan; Andri Noor Ardiansyah; Syairul Bahar
GEOGRAPHY : Jurnal Kajian, Penelitian dan Pengembangan Pendidikan Vol 14, No 1 (2026): APRIL
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/geography.v14i1.36733

Abstract

Abstrak:  Banjir merupakan bencana hidrometeorologi yang menyebabkan kerusakan luas serta menimbulkan kerugian di bidang ekonomi, kesehatan, dan pendidikan. Penelitian ini menganalisis distribusi spasial kerawanan banjir dan kaitannya dengan lokasi fasilitas pendidikan di Kota Tangerang. Pendekatan kuantitatif diterapkan dengan menggunakan metode Weighted Sum yang terintegrasi dalam Sistem Informasi Geografis (SIG) serta melibatkan berbagai parameter fisik dan antropogenik. Hasil analisis menunjukkan bahwa kelas kerawanan sedang dan tinggi mendominasi, masing-masing mencakup sekitar 6.300 ha dan 6.574 ha, sedangkan kelas sangat tinggi mencapai 2.022 ha. Sekitar 78,6% fasilitas pendidikan berada pada wilayah dengan tingkat risiko banjir sedang hingga sangat tinggi. Kecamatan dengan tingkat kerawanan tertinggi meliputi Tangerang, Ciledug, dan Pinang, sedangkan Batuceper, Neglasari, dan Benda tergolong rendah hingga sangat rendah. Temuan ini menegaskan bahwa curah hujan, topografi, jenis tanah, dan lahan terbangun berperan penting dalam menentukan kerawanan banjir, serta menjadi dasar mitigasi risiko fasilitas pendidikan dan kebijakan tata ruang wilayah. Abstract:  Floods are hydrometeorological disasters that cause extensive damage and lead to economic, health, and educational losses. This study analyzes the spatial distribution of flood susceptibility and its relationship with educational facilities in Tangerang City. A quantitative approach was applied using the Weighted Sum method integrated with Geographic Information Systems (GIS) and multiple physical and anthropogenic parameters. The results show that moderate and high susceptibility classes dominate, covering approximately 6,300 ha and 6,574 ha, while the very high class reaches 2,022 ha. About 78.6% of educational facilities are located in areas with moderate to very high flood risk. Districts with the highest susceptibility include Tangerang, Ciledug, and Pinang, whereas Batuceper, Neglasari, and Benda show low to very low risk. These findings highlight the interaction of rainfall, topography, soil type, and built-up land in shaping flood Susceptibility and provide a basis for educational facility risk mitigation and spatial planning policies.
Trend of Flood Susceptibility Mapping using Remote Sensing Approach: A Bibliometric Analysis Hermawan Setiawan
Media Komunikasi Geografi Vol. 27 No. 1 (2026)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/mkg.v27i1.113622

Abstract

Flood disasters generate substantial socio-economic and environmental impacts, increasing the need for accurate Flood Susceptibility Mapping (FSM). This study provides a comprehensive bibliometric analysis of FSM integrated with Multi-Criteria Decision Making (MCDM) and remote sensing techniques. Using the PRISMA framework, a total of 1,234 scientific articles were collected from the Scopus database and analyzed using VOSviewer to identify research trends, collaboration networks, and thematic clusters. The findings indicate a significant increase in publications since 2015, reaching their peak in 2025, with major contributions originating from highly flood-prone countries such as India, Iran, and China. In addition, Geocarto International and Biswajeet Pradhan were identified as the most cited journal source and author, respectively. Furthermore, cluster analysis reveals that the integration of the Analytic Hierarchy Process (AHP) and GIS has increasingly been combined with land-use and river discharge modeling through ensemble learning approaches, including statistical models and AdaBag Rotation Forest. On the other hand, dynamic and cloud-based automated approaches remain relatively underexplored. Network and density analyses further emphasize the importance of optimizing high spatial resolution using hybrid algorithms, such as Genetic Algorithm–Multilayer Perceptron, implemented within cloud-based platforms like Google Earth Engine (GEE) to dynamically predict the impacts of climate change on urban flooding. These findings provide a strategic direction for supporting adaptive spatial planning and strengthening climate resilience in disaster risk management.
Co-Authors Andri Noor Ardiansyah, Andri Noor Syairul Bahar