Penerapan Algoritma Artificial Neural Network Untuk Penentuan Kategori Desa Wisata Menggunakan Metode Backpropagation (Studi Kasus : Jawa Tengah)
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Institusion
Institut Teknologi Telkom Purwokerto
Author
Windi Solihatin, Wahidah
Subject
TA Engineering (General). Civil engineering (General)
Datestamp
2022-07-20 08:49:00
Abstract :
Central Java is one of the provinces on the island of Java that has generated potential potential villages that could be developed as travel destinations, including tourist villages. The assessment in determining the categorization of tourist villages in central Java will take several days because the assessment process is still done manually. Efforts to reduce time and minimize assessment errors are to create a system to support decisions. Decision-making systems can help decision-making processes faster, more precise, and more accurate. From the results of the first training, obtained for 7 extras using 1 hidden layers with 20 neurons, and output. Per input layer and hidden layer given 1 bias and 1. At the second training, learning rate is 0.5 and momentum 0.8. While the testing process had an mse yield of 0.00099899, with an accuracy of 84.84%, and an error of 15.16%. Before implementing methods on the website, a guide or GUI is created that can display the status results of the village. And the design of web-based systems is created as an aid in determining tourist villages especially in central Java. The decision maker will input weight on each criterion on the system and the system will process the data so that the results will be shown in the tourist village category. The researcher suggests that the study may be continued by adding other variables as well Keywords: Decision Support System, Tourism Village, Artificial Neural Network
Central Java is one of the provinces on the island of Java that has generated potential potential villages that could be developed as travel destinations, including tourist villages. The assessment in determining the categorization of tourist villages in central Java will take several days because the assessment process is still done manually. Efforts to reduce time and minimize assessment errors are to create a system to support decisions. Decision-making systems can help decision-making processes faster, more precise, and more accurate. From the results of the first training, obtained for 7 extras using 1 hidden layers with 20 neurons, and output. Per input layer and hidden layer given 1 bias and 1. At the second training, learning rate is 0.5 and momentum 0.8. While the testing process had an mse yield of 0.00099899, with an accuracy of 84.84%, and an error of 15.16%. Before implementing methods on the website, a guide or GUI is created that can display the status results of the village. And the design of web-based systems is created as an aid in determining tourist villages especially in central Java. The decision maker will input weight on each criterion on the system and the system will process the data so that the results will be shown in the tourist village category. The researcher suggests that the study may be continued by adding other variables as well Keywords: Decision Support System, Tourism Village, Artificial Neural Network
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