Komparasi model support vector machine dan backpropagation dalam peramalan jumlah wisatawan mancanegara di provinsi Bali

Imelda Alvionita Tarigan (Department of Information Technology, Universitas Udayana, Bukit Jimbaran, Badung, Bali 80361|Universitas Udayana)
I Putu Agung Bayupati (Department of Information Technology, Universitas Udayana, Bukit Jimbaran, Badung, Bali 80361|Universitas Udayana)
Gusti Agung Ayu Putri (Department of Information Technology, Universitas Udayana, Bukit Jimbaran, Badung, Bali 80361|Universitas Udayana)

Jurnal Teknologi dan Sistem Komputer
Volume 9, Issue 2, Year 2021 (April 2021)

Tourism in Bali is one of the major industries which play an important role in developing the global economy in Indonesia. Good forecasting of tourist arrival, especially from foreign countries, is needed to predict the number of tourists based on past information to minimize the prediction error rate. This study compares the performance of SVM and Backpropagation to find the model with the best prediction algorithm using data from foreign tourists in Bali Province. The results of this study recommend the best forecasting using the SVM model with the radial kernel function. The best accuracy of the SVM model obtained the lowest error values of MSE 0.0009, MAE 0.0186, and MAPE 0.0276, compared to Backpropagation which obtained MSE 0.0170, MAE 0.1066, and MAPE 0.1539.