Prediksi Kunjungan Wisata Kota Payakumbuh Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation
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Keywords:
Data Mining, FP-Growth, Association Rules, Rapid Miner, Stock Items
AbstractTourism is a whole related elements which consist of tourists, tourist destinations, travel, industry and so on which are tourism activities and abundant natural wealth. The tourism sector is a very important service-based sector. Tourism is the fastest growing, vibrant and strong economic sector development, it also contributes to Gross Domestic Product (GDP), job creation, social and economic development. Artificial Neural Networks are computer programs that can imitate thought processes and knowledge to solve a specific problem. One of which is applied by the Artificial Neural Network to predict tourist visits. By using the Backpropagation method, it will be known the prediction of the number of tourist visits. The Backpropagation method is very useful for Artificial Neural Networks predicting the number of tourist visits the following year. The data processed in this study were 12 data sourced from the tourism section of the Payakumbuh City Youth and Sports Tourism Office. Furthermore, the data is processed using Matlab software. The stages of backpropagation are initialization, activation, training and iteration. The calculation of the network pattern used and the accuracy level of the expected error is continued. The result of testing this method is that it can predict tourist visits. So the level of accuracy is 95%. The prediction process has been carried out to predict tourist visits to the city of Payakumbuh. With the level of accuracy obtained is met, it can be used to help the Payakumbuh City Tourism Office increase the number of tourist visits in the future and further improve tourism management. Downloads
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Published
2022-12-31
Section
Articles
How to Cite
Aulya, N. (2022). Prediksi Kunjungan Wisata Kota Payakumbuh Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation. Jurnal Informatika Ekonomi Bisnis, 4(4), 130-135. https://doi.org/10.37034/infeb.v4i4.157
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