Peramalan Kunjungan Wisatawan Mancanegara Menggunakan Generalized Regression Neural Networks
Journal Title: Jurnal INFOTEL - Year 2016, Vol 8, Issue 1
Abstract
Forecasting tourism demand are very important for the government and industry, as forecasting the basis for effective policy planning. This research is using Generalized Regression Neural Network (GRNN) to forecasting tourism demand according 19 the main entrance and nationality, such as: Ngurah Rai, SoekarnoHatta, Batam, Tanjung Uban, Polonia, Juanda, Husein Sastranegara, Tanjung Balai Karimun, Tanjung Pinang, Tanjung Priok, Adi Sucipto, Minangkabau, Entikong, Adi Sumarmo, Sultan Syarif Kasim II, Sepinggan, Sam Ratulangi International Airport Lombok, and Makassar. GRNN has advantages not require the estimated number of network weights to get optimal network architecture, so it does not require setting parameters. Research trial conducted using a spread of 0.1 to 1.0. The experimental results show that the best forecasting performance with spread 0,1 for training and testing data
Authors and Affiliations
Sri Herawati
Keywords
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- EP ID EP195449
- DOI 10.20895/infotel.v8i1.49
- Views 135
- Downloads 0
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