Optimasi Prediksi Bencana Banjir menggunakan Algoritma SVM untuk penentuan Daerah Rawan Bencana Banjir
Keywords:
SVM Algorithm, PSO, FloodAbstract
Flooding is one factor that can cause an economic slowdown in the affected area. Bandung is known as the "City of Flowers" and "City of Fashion," since the term refers to the city's many different modes that sprout up in various locations to facilitate the purchasing and selling process. Bandung not only gave rise to fashion trends that would become popular year after year, but it also had a plethora of traditional cuisine dishes that were both unique and exciting. Because of this, Bandung Regency is now one of the flood-prone areas in the country. It will be easier to deliver information to communities in the Bandung Regency that are included in Flood-Prone Areas or Non-Flood-Prone Areas if a model of flood-prone areas is created. The SVM method is a technique that may be used in classification and regression and has recently become quite popular. In terms of functions and conditions of problems that can be solved, SVM is similar to Artificial Neural Network (ANN), and to improve its accuracy, it uses what can be optimized with PSO (Particle Swarm Optimization), where the test data used is BNPB official website data, BPS Bandung District, and BMKG that has been processed. Data included in the classification criteria for Flood Prone Areas are Rainfall Intensity, Water Discharge, Area, Length of Rain, and Population Density. The accuracy rate generated by using the SVM algorithm is 85.71% and the AUC generated is 0.841 while the accuracy rate generated by using the PSM-based SVM algorithm is 97.62%. and AUC produced at 1,000
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