TY - GEN
T1 - Design of experiment to optimize the architecture of wavelet neural network for forecasting the tourist arrivals in Indonesia
AU - Otok, Bambang W.
AU - Suhartono,
AU - Ulama, Brodjol S.S.
AU - Endharta, Alfonsus J.
PY - 2011
Y1 - 2011
N2 - Wavelet Neural Network (WNN) is a method based on the combination of neural network and wavelet theories. The disadvantage of WNN is the lack of structured method to determine the optimum level of WNN factors, which are mostly set by trial and error. The factors affecting the performance of WNN are the level of MODWT decomposition, the wavelet family, the lag inputs, and the number of neurons in the hidden layer. This research presents the use of design of experiments for planning the possible combination of factor levels in order to get the best WNN. The number of tourist arrivals in Indonesia via Soekarno-Hatta airport in Jakarta and via Ngurah Rai airport in Bali is used as case study. The result shows that design of experiments is a practical approach to determine the best combination of WNN factor level. The best WNN for data in Soekarno-Hatta airport is WNN with level 4 of MODWT decomposition, Daubechies wavelet, and 1 neuron in the hidden layer. Whereas, the best WNN for data in Ngurah Rai airport is WNN with MODWT decomposition level 3 and using input proposed by Renaud, Starck, and Murtagh [11] and seasonal lag input addition.
AB - Wavelet Neural Network (WNN) is a method based on the combination of neural network and wavelet theories. The disadvantage of WNN is the lack of structured method to determine the optimum level of WNN factors, which are mostly set by trial and error. The factors affecting the performance of WNN are the level of MODWT decomposition, the wavelet family, the lag inputs, and the number of neurons in the hidden layer. This research presents the use of design of experiments for planning the possible combination of factor levels in order to get the best WNN. The number of tourist arrivals in Indonesia via Soekarno-Hatta airport in Jakarta and via Ngurah Rai airport in Bali is used as case study. The result shows that design of experiments is a practical approach to determine the best combination of WNN factor level. The best WNN for data in Soekarno-Hatta airport is WNN with level 4 of MODWT decomposition, Daubechies wavelet, and 1 neuron in the hidden layer. Whereas, the best WNN for data in Ngurah Rai airport is WNN with MODWT decomposition level 3 and using input proposed by Renaud, Starck, and Murtagh [11] and seasonal lag input addition.
KW - design of experiments
KW - neural network
KW - tourist arrival
KW - wavelet
UR - http://www.scopus.com/inward/record.url?scp=82955172924&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25462-8_2
DO - 10.1007/978-3-642-25462-8_2
M3 - Conference contribution
AN - SCOPUS:82955172924
SN - 9783642254611
T3 - Communications in Computer and Information Science
SP - 14
EP - 23
BT - Informatics Engineering and Information Science - International Conference, ICIEIS 2011, Proceedings
T2 - International Conference on Informatics Engineering and Information Science, ICIEIS 2011
Y2 - 14 November 2011 through 16 November 2011
ER -