Tourism Object Recognition System of Kabupaten Tuban Using Deep Learning with Convolutional Neural Network (CNN)

Safitri, Yella Ariska (2022) Tourism Object Recognition System of Kabupaten Tuban Using Deep Learning with Convolutional Neural Network (CNN). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Tourist objects are places where people vacation with family or alone. This place can also be used as a good business area if accompanied by proper management. The purpose of this research is to create a tourist attraction recognition system that can perform image searches. The method used is Deep Learning with Convolutional Neural Network (CNN) algorithm. The application of the CNN method is carried out using the Python programming language using a dataset which is grouped into two parts, training data and test data. In the training process, the data used are 720 images consisting of 12 classes. As for the test, the data used are 10 images for each class, so that in total there are 120 images. The data is taken from one point of view so that the image that can be identified is also only from that point of view. After the test is carried out, accuracy calculations and conclusions were drawn. From the test results, the precision value is 66.7%, recall is 79,5%, F1 score is 72,5% and accuracy is 66.7%

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