Backpropagation to Predict the Number of International Tourists to Indonesia
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Abstract
Developing areas that have tourism potential is an effort to increase sources of income for villagers. Areas that have tourist areas can be a vehicle that attracts the attention of the public, both domestically and abroad. Tourists who come can provide income for tourist areas or the community. Therefore, predicting the number of incoming tourists can be predicted based on data from previous years. The goal is to make predictions to improve infrastructure and all needs for tourists. The purpose of this study is to apply the Backpropagation method to predict the number of foreign tourist visits to Indonesia. The dataset used in this study is 6000 lines and is divided into 4800 lines of training data, and 1200 lines of test data. The dataset is taken from the bps website, with the input variables being month, year, country of origin, tourist entrance to Indonesia, and the output variable being the number of tourists. The model of Backpropagation is evaluated by calculating MAE, and the architecture built is 4-9-1, 4 input layer nodes, 9 hidden layer nodes, and 1 output layer node. The test results of the MAE value of the Backpropagation method in predicting the number of tourists to Indonesia are 0.247.
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