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Journal : Jurnal Teknologi dan Sistem Komputer

Prediksi Kejadian Banjir dengan Ensemble Machine Learning Menggunakan BP-NN dan SVM Ike Fitriyaningsih; Yuniarta Basani
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 3, Year 2019 (July 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (211.983 KB) | DOI: 10.14710/jtsiskom.7.3.2019.93-97

Abstract

This study aims to examine the prediction of rainfall and river water debit using the Back Propagation Neural Network (BP-NN) method. Prediction results are classified using the Support Vector Machine (SVM) method to predict flooding. The parameters used to predict rainfall with BP-NN are minimum, maximum and average temperature, average relative humidity, sunshine duration, and average wind speed. The debit of Ular Pulau Tagor river is predicted by BP-NN. BPNN and SVM modeling using software R. Daily climate data from 2015-2017 were taken from three stations, namely Sampali climatology station, Kualanamu meteorological station, and Tuntung geophysics station. Prediction of river water debit is for 6 days and 30 days in the future. The best dataset is a 6 day prediction with a combination of 60% training and 40% testing. Flood prediction accuracy with SVM was 100% in predicting flood events for the next 6 days.