Prediction of Arrival of Archipelago Tourists and Abroad Based on Regions Using Neural Network Algorithm Based on Genetic Algorithm - Abas, Mohamad Ilyas; Lasarudin, Alter (Indonesian Journal of Artificial Intelligence and Data Mining, 2018)
Tourists are an integral part of the world of tourism. Generally tourists visit to see the diversity of an area. In Gorontalo, several tourist attractions have been visited by domestic and foreign tourists. This is certainly a large amount so that it can help improve economic growth in Gorontalo from the tourism sector. Therefore the need for knowledge of the number of tourists for the coming year. So that, it can provide an analysis of the consideration of the decision to the government to be able to prepare steps in building the economy of the tourism sector. The number of tourists can be made a prediction using the method in data mining namely the Neural Network. Neural Network is a good method for predicting non-linear datasets such as number of tourists. with the Neural Network method it can be done. Not only that, Genetic Algorithm will be used to optimize the parameters of the Neural Network so that it can increase the accuracy value that can be measured with the Root Mean Square Error (RMSE) value. The results of this study indicate that the value of RMSE for domestic tourist data as follows: Gorontalo City: 0.116, Gorontalo Regency: 0.220, Boalemo: 0.073, Pohuwato: 0.142, Bone Bolango: 0.078, North Gorontalo: 0.093. For foreign tourists, Gorontalo City: 0.117, Gorontalo Regency: 0.178, Boalemo: 0.075, Pohuwato: 0.099, Bone Bolango: 0.124, North Gorontalo: 0.155.