Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, Indonesia
BibTex Citation Data :
@article{JTSISKOM13142, author = {Ike Fitriyaningsih and Yuniarta Basani}, title = {Prediksi Kejadian Banjir dengan Ensemble Machine Learning Menggunakan BP-NN dan SVM}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {7}, number = {3}, year = {2019}, keywords = {ensemble machine learning; flood prediction, BP-NN and SVM; rainfall prediction; river water debit prediction}, 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.}, issn = {2338-0403}, pages = {93--97} doi = {10.14710/jtsiskom.7.3.2019.93-97}, url = {https://jtsiskom.undip.ac.id/article/view/13142} }
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