Clustering dengan Menggunakan K-Means untuk Analisa Dampak Banjir
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Banjir merupakan bencana yang sering terjadi di Indonesia dan menimbulkan dampak signifikan pada infrastruktur, ekonomi, dan kesehatan masyarakat. Analisis pola dampak banjir diperlukan untuk mendukung perencanaan mitigasi yang efektif. Penelitian ini bertujuan untuk mengelompokkan wilayah terdampak banjir berdasarkan tingkat dampaknya menggunakan algoritma K-Means Clustering. Penelitian menggunakan pendekatan kuantitatif dengan data sekunder dari Badan Nasional Penanggulangan Bencana (BNPB) periode 2019–2024. Sebanyak 150 kejadian banjir dianalisis berdasarkan lima variabel: kedalaman banjir, lama genangan, jumlah korban, kerusakan infrastruktur, dan kerugian ekonomi. Data diproses menggunakan algoritma K-Means untuk membentuk klaster dampak banjir. Analisis berhasil mengidentifikasi tiga klaster utama, yaitu dampak rendah (62 kejadian), dampak sedang (54 kejadian), dan dampak tinggi (34 kejadian). Klaster dampak tinggi ditandai dengan kedalaman banjir rata-rata 150 cm, lama genangan 6,5 hari, dan kerugian ekonomi rata-rata Rp 25 miliar. Kedalaman banjir dan lama genangan terbukti menjadi variabel paling dominan dalam menentukan tingkat dampak. K-Means Clustering efektif untuk memetakan tingkat dampak banjir secara multidimensi. Hasil penelitian dapat menjadi dasar bagi pemerintah dan pemangku kepentingan dalam menyusun kebijakan mitigasi yang tepat sasaran, khususnya dalam mengalokasikan sumber daya untuk wilayah dengan kategori dampak tinggi. Implikasinya, pendekatan ini dapat meningkatkan efektivitas penanggulangan banjir dan mendukung pembangunan berkelanjutan.
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