Pemodelan Risiko Banjir Akibat Curah Hujan Ekstrem di Kabupaten Mojokerto Menggunakan Non-Stationary Extreme Value

Nuzula, Firdausi (2025) Pemodelan Risiko Banjir Akibat Curah Hujan Ekstrem di Kabupaten Mojokerto Menggunakan Non-Stationary Extreme Value. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Banjir merupakan salah satu bencana alam yang paling sering terjadi di Indonesia. Faktor utama penyebab banjir meliputi curah hujan yang tinggi, deforestasi, buruknya sistem drainase, dan sedimentasi sungai yang mengurangi kapasitas aliran air. Penelitian ini berfokus pada Kabupaten Mojokerto, salah satu wilayah dengan tingkat risiko banjir yang tinggi di Provinsi Jawa Timur. Sebagai langkah mitigasi untuk mengurangi dampak kerugian material seperti kehilangan tempat tinggal, gangguan aktivitas ekonomi, hingga masalah kesehatan masyarakat akibat penyebaran penyakit, diperlukan analisis risiko banjir berbasis data. Risiko banjir dihitung dengan menggabungkan probabilitas temporal dan probabilitas spasial. Probabilitas temporal dianalisis menggunakan data curah hujan per jam selama lima tahun terakhir yang diperoleh dari situs resmi NASA. Metode Non-Stationary Extreme Value dengan pendekatan distribusi Generalized Extreme Value (GEV) digunakan karena mampu menangkap pola perubahan data akibat pengaruh temporal, spasial, dan perubahan iklim. Pengujian tren dilakukan terlebih dahulu menggunakan Mann Kendall Trend Test untuk memastikan adanya pola tren temporal. Hasil perhitungan menunjukkan bahwa probabilitas terjadinya banjir meningkat seiring bertambahnya periode waktu N tahun ke depan yang dianalisis. Sementara itu, probabilitas spasial diperoleh dari data kerentanan wilayah berupa shapefile yang diunduh dari website InaRISK milik BNPB. Analisis dilakukan melalui proses ekstraksi dan statistik spasial. Hasil analisis menunjukkan bahwa 1,46% wilayah Kabupaten Mojokerto termasuk dalam kategori kerentanan rendah, 46,61% dalam kategori kerentanan sedang, dan 51,93% dalam kategori kerentanan tinggi.
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Flooding is one of the most common natural disasters in Indonesia. The main factors causing flooding include heavy rainfall, deforestation, poor drainage systems, and river sedimentation that reduces water flow capacity. This research focuses on Mojokerto District, one of the high flood risk areas in East Java Province. As a mitigation measure to reduce the impact of material losses such as loss of housing, disruption of economic activities, and public health problems due to the spread of diseases, a data-driven flood risk analysis is needed. Flood risk is calculated by combining temporal probability and spatial probability. Temporal probability is analyzed using hourly rainfall data for the last five years obtained from the official NASA website. The Non-Stationary Extreme Value method with the Generalized Extreme Value (GEV) distribution approach was used because it is able to capture patterns of data changes due to temporal, spatial, and climate change influences. Trend testing was conducted first using the Mann Kendall Trend Test to confirm the existence of a temporal trend pattern. The calculation results show that the probability of flooding increases as the N-year time period analyzed increases. Meanwhile, spatial probabilities were obtained from shapefile vulnerability data downloaded from BNPB's InaRISK website. The analysis was conducted through an extraction process and spatial statistics. The analysis was conducted through extraction and spatial statistics. The results of the analysis show that 1.46% of Mojokerto Regency is classified as low vulnerability, 46.61% as moderate vulnerability, and 51.93% as high vulnerability.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Banjir, Non-Stationary Extreme Value, Risiko, Curah Hujan, Kerentanan, Flood, Non-Stationary Extreme Value, Risk, Rainfall, Vulnerability
Subjects:	Q Science Q Science > QA Mathematics Q Science > QA Mathematics > QA614.58 Catastrophes Q Science > QC Physics > QC866.5 Climatology--Forecasting. Q Science > QC Physics > QC925 Rain and rainfall
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Actuaria > 94203-(S1) Undergraduate Thesis
Depositing User:	Firdausi Nuzula
Date Deposited:	31 Jul 2025 03:22
Last Modified:	31 Jul 2025 03:22
URI:	http://repository.its.ac.id/id/eprint/124726

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