ANALISIS SPASIAL WILAYAH RAWAN BANJIR MENGGUNAKAN METODE SCORING DI DAS CIMANCEURI | Spatial Analysis of Flood-prone Areas Using the Scoring Method in the Cimanceuri Watershed

ABSTRAK MUHAMMAD NIZAR SOLIHIN. Analisis Spasial Wilayah Rawan Banjir Menggunakan Metode Scoring di DAS Cimanceuri. Dibimbing oleh HENDRAYANTO. Banjir merupakan bencana alam yang umum terjadi di Indonesia, didorong oleh faktor meteorologi, karakteristik DAS, dan aktivitas manusia. Menganalisis kerentanan banjir sangat penting untuk mitigasi bencana, perlindungan ekonomi, tata ruang, dan perumusan kebijakan publik. Metode penilaian dan pembobotan digunakan untuk menilai parameter terkait banjir seperti curah hujan, ketinggian, kemiringan, tutupan lahan, jenis tanah, dan kepadatan sungai. Teknik overlay kemudian digunakan untuk mengintegrasikan kumpulan data spasial dan menghasilkan peta kerentanan banjir. Hasil penelitian menunjukkan bahwa DAS Cimanceuri terbagi menjadi tiga kelas kerentanan banjir: tidak rawan (34 ha), rawan (16.683 ha), dan sangat rawan (32.332 ha). Desa Gorowong memiliki wilayah rawan banjir terbesar (826 ha), terutama dipengaruhi oleh curah hujan yang tinggi, karakteristik tanah, dan keberadaan permukiman. Studi ini memberikan informasi penting tentang distribusi spasial daerah rawan banjir, yang dapat mendukung sistem peringatan dini dan penanggulangan bencana banjir di DAS Cimanceuri Kata kunci: Banjir, Kerentanan, Skor, Tumpang Susun, Daerah Aliran Sungai Cimanceuri

ABSTRACT MUHAMMAD NIZAR SOLIHIN. Spatial Analysis of Flood-prone Areas Using the Scoring Method in the Cimanceuri Watershed. Supervised by HENDRAYANTO. Floods are a common natural disaster in Indonesia, driven by meteorological factors, watershed characteristics, and human activities. Analyzing flood vulnerability is crucial for disaster mitigation, economic protection, spatial planning, and public policy formulation. The scoring and weighted method was employed to assess flood-related parameters such as rainfall, elevation, slope, land cover, soil type, and river density. The overlay technique was then used to integrate spatial datasets and generate a flood vulnerability map. The results indicate that the Cimanceuri Watershed is divided into three classes of flood vulnerability: non vulnerable (34 ha), vulnerable (16.683 ha), and highly vulnerable (32.332 ha). Gorowong Village has the largest flood-prone area (826 ha), primarily influenced by high rainfall, soil characteristics, and the presence of settlements. This study provides essential information on the spatial distribution of flood-prone areas, which can support early warning systems and flood disaster management in the Cimanceuri Watershed Keywords: Flood, Vulnerability, Scoring, Overlay, Cimanceuri Watershed