Prediksi Kedatangan Turis Asing ke Indonesia Menggunakan Backpropagation Neural Networks - Haviluddin Haviluddin; Zainal Arifin; Awang Harsa Kridalaksana; Dedy Cahyadi (Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro, 2016)
In this paper, a backpropagation neural network (BPNN) method with time series data has been explored. The BPNN method to predict the foreign tourist’s arrival to Indonesia datasets have been implemented. The foreign tourist’s arrival datasets were taken from the central agency on statistics (BPS) Indonesia. The experimental results showed that the BPNN method with two hidden layers was able to forecast foreign tourist’s arrival to Indonesia. Where the mean square error (MSE) as forecasting accuracy has been indicated. In this study, the BPNN method is able and recommended to be alternative methods for predicting time series datasets. Also, the BPNN method showed that effective and easy to use. In other words, BPNN method is capable of producing a good value of forecasting.