Analysis of Visitor Satisfaction Levels Using the K-Nearest Neighbor Method
DOI:
10.33395/sinkron.v8i2.12257Keywords:
Classification, Confusion Matrix, Data Mining, K-Nearest Neighbor (kNN), Orange, Roc Analysis, SatisfiedAbstract
Visitors are people who come to a place, entertainment, shopping, and tourism. Visitors are one of the important factors for the progress and development of a place. With visitors, an entertainment, tourism and shopping area can progress and develop. Therefore researchers will make a study of the level of visitor satisfaction. This research aims to improve the quality of an entertainment venue, shopping and increase the quantity of visitors. This research was conducted using the K-Nearest Neighbor method. The K-Nearest Neighbor method is a classification method based on training data (dataset). The data used by researchers is 45 visitor data. The classification carried out using the K-Nearest Neighbor method aims to classify data of satisfied visitors and dissatisfied visitors at an entertainment or tourism place. In using the K-Nearest Neighbor method, the first stage is selecting sample data, the data to be selected, then preprocessing, then designing the widget with the K-Nearest Neighbor method and finally testing data mining using the K-Nearest Neighbor method. The K-Nearest Neighbor Method. This visitor data was obtained by researchers through a questionnaire and the results of the questionnaire that 41 visitors were satisfied. After classifying visitor data using the K-Nearest Neighbor method, the classification results were 41 satisfied visitors. The conclusion is that many visitors are satisfied.
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Copyright (c) 2023 Putri Violita, Gomal Juni Yanris, Mila Nirmala Sari Hasibuan
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Muhammad Khoiruddin harahap
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