Metode Hibrida FCM dan PSO-SVR untuk Prediksi Data Arus Lalu Lintas
Abstract: Traffic flow
forecasting is one important part in Intelligent Transportation System. There
are many methods had been developed for time series and traffic flow
forecasting such as: Autoregressive Moving Average (ARIMA), Artificial Neural
Network (ANN), and Support Vector Regression (SVR). SVR performance depend on
kernel function and parameters of those kernel and data characteristic used in
SVR as well. This research proposed hybrid method for traffic flow data
clustering and forecasting. Fuzzy C-means is used in order to minimize the variance in whole
dataset. Particle Swarm Optimization (PSO) is used in order to select the
appropriate parameters for SVR. Experimental result shows the proposed method
give MAPE below 4% in all test sites.
Keywords: fuzzy c-means,
particle swarm optimization, prediksi data lalu lintas, support vector
regression, time-series
Penulis: Agri Kridanto, Joko
Lianto Buliali
Kode Jurnal: jptinformatikadd150378
