Analysis of Visitor Satisfaction Levels Using the K-Nearest Neighbor Method

Abstract

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.

Analysis of Student Excellence Classes in Data Mining Using the KNN Method

“…Then training data is data that is used to assist the data processing process in data mining using the knn method. This stage is a very important stage and is also explained in the research conducted (Violita et al, 2023) that training data is used to help the data classification process using methods that are appropriate to the classification model, if the training data does not exist then the research cannot This is done because there is no target for the classification to be carried out. Next is the stage of designing and testing the model that has been created in data mining, which is the process of creating a system and testing the system.…”

“…LITERATURE REVIEW The K-Nearest Neighbor (KNN) method is a simple but very effective machine learning algorithm, used in classification and regression (Zai, Sirait, Nainggolan, Sihombing, & Banjarnahor, 2023) (Istiadi, Rahman, & Wisnu, 2023). This algorithm works by predicting new data labels based on similarities with already known data (Adjani, Fauzia, & Juliane, 2023) (Arifuddin, Pinastawa, Anugraha, & Pradana, 2023) (Violita, Yanris, & Hasibuan, 2023). In principle, KNN identifies the 'k' nearest neighbors of the data points to be classified, based on a certain distance metric, such as Euclidean distance.…”

“…In research conducted by (Irmayani & Sinaga, 2023) the knn method can be used to classify data to determine the level of community satisfaction and also in research by (Pratama, Yanris, Nirmala, & Hasibuan, 2023) that the knn method can be used to conduct research on satisfaction levels as well. The knn method was also used in research conducted by (Triani, Dar, & Yanris, 2023) that the knn method can be used to determine the level of community satisfaction and in research conducted by (Violita et al, 2023) also used the knn method for level classification customer satisfaction. The average accuracy obtained from this research is also very good, because the average accuracy results obtained are more than 90%.…”

Implementation of Data Mining to Determine Public Interest in Automatic Motorcycles

“…This method works based on the principle of proximity between data. In KNN, data will be classified based on how often the labels of its k-nearest neighbors appear (Violita, Yanris, & Hasibuan, 2023) (Istiadi, Rahman, & Wisnu, 2023). K here is a parameter that determines the number of nearest neighbors to be considered (Adjani, Fauzia, & Juliane, 2023) (Arifuddin, Pinastawa, Anugraha, & Pradana, 2023).…”