Analisis Prediksi Banjir di Indonesia Menggunakan Algoritma Support Vector Machine dan Random Forest
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Indarto Aditya Purnomo
*
Universitas Buana Perjuangan Karawang, Karawang,
Indonesia
- Jamaludin Indra Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- Elsa Elvira Awal Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- Tatang Rohana Universitas Buana Perjuangan Karawang, Karawang, Indonesia (*) Corresponding Author
Abstract
Natural disasters frequently occur in Indonesia, such as floods, landslides, and volcanic eruptions. Geological factors, such as the convergence of four major tectonic plates, make Indonesia vulnerable to natural disasters. Statistical data from the National Disaster Management Agency show an increase in flood occurrences each year, peaking in 2021 with 1,794 incidents. Early anticipation is necessary to minimize the impact of natural disasters, and predictive patterns are becoming new knowledge for preventing and managing these disasters. This study applies the Support Vector Machine and Random Forest algorithms. The results of this study predict that the largest number of floods from 2024 to 2026 in Indonesia will occur in Aceh with 240 floods, North Sumatra with 215 floods, West Java with 210 floods, and Central Java with 160 floods. The best algorithm comparison results were achieved with Random Forest, which had an accuracy of 99.6% and an average RMSE value of 3.834.
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