K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism
The more tourist objects are in an area, the more challenging it is for local governments to increase the selling value of these attractions. The government always strives to develop tourist attraction areas by prioritizing the beauty of tourist attractions. However, visitors often have difficulty i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Bina Nusantara University
2023-05-01
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| Series: | ComTech |
| Subjects: | |
| Online Access: | https://journal.binus.ac.id/index.php/comtech/article/view/7993 |
| _version_ | 1797682443236409344 |
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| author | Devie Rosa Anamisa Achmad Jauhari Fifin Ayu Mufarroha |
| author_facet | Devie Rosa Anamisa Achmad Jauhari Fifin Ayu Mufarroha |
| author_sort | Devie Rosa Anamisa |
| collection | DOAJ |
| description | The more tourist objects are in an area, the more challenging it is for local governments to increase the selling value of these attractions. The government always strives to develop tourist attraction areas by prioritizing the beauty of tourist attractions. However, visitors often have difficulty in determining tourist objects that match their criteria because of the many choices. The research developed a tourist attraction recommendation system for visitors by applying machine learning techniques. The machine learning technique used was the K-Nearest Neighbor (KNN) method. Several trials were conducted with a dataset of 315 records, consisting of 11 attributes and 21 tourist attractions. Based on the dataset, the preprocessing stage was previously carried out to improve the data format by selecting data where the data were separated based on existing criteria, then calculating the closest distance and determining the value of k in the KNN method. The results are divided into five folds for each classification method. The highest system accuracy obtained at KNN is 78% at k=1. It shows that the KNN method can provide recommendations for three tourist attraction classes in Bangkalan. Applying the KNN method in the recommendation system determines several alternative tourist objects that tourists can visit according to their criteria in natural, cultural, and religious tourist objects. |
| first_indexed | 2024-03-11T23:59:47Z |
| format | Article |
| id | doaj.art-18a564c8ba2743a89fededbc4af9e6fa |
| institution | Directory Open Access Journal |
| issn | 2087-1244 2476-907X |
| language | English |
| last_indexed | 2024-03-11T23:59:47Z |
| publishDate | 2023-05-01 |
| publisher | Bina Nusantara University |
| record_format | Article |
| series | ComTech |
| spelling | doaj.art-18a564c8ba2743a89fededbc4af9e6fa2023-09-18T04:50:05ZengBina Nusantara UniversityComTech2087-12442476-907X2023-05-01141334410.21512/comtech.v14i1.79937067K-Nearest Neighbors Method for Recommendation System in Bangkalan’s TourismDevie Rosa Anamisa0Achmad Jauhari1Fifin Ayu Mufarroha2University of Trunojoyo MaduraUniversity of Trunojoyo MaduraUniversity of Trunojoyo MaduraThe more tourist objects are in an area, the more challenging it is for local governments to increase the selling value of these attractions. The government always strives to develop tourist attraction areas by prioritizing the beauty of tourist attractions. However, visitors often have difficulty in determining tourist objects that match their criteria because of the many choices. The research developed a tourist attraction recommendation system for visitors by applying machine learning techniques. The machine learning technique used was the K-Nearest Neighbor (KNN) method. Several trials were conducted with a dataset of 315 records, consisting of 11 attributes and 21 tourist attractions. Based on the dataset, the preprocessing stage was previously carried out to improve the data format by selecting data where the data were separated based on existing criteria, then calculating the closest distance and determining the value of k in the KNN method. The results are divided into five folds for each classification method. The highest system accuracy obtained at KNN is 78% at k=1. It shows that the KNN method can provide recommendations for three tourist attraction classes in Bangkalan. Applying the KNN method in the recommendation system determines several alternative tourist objects that tourists can visit according to their criteria in natural, cultural, and religious tourist objects.https://journal.binus.ac.id/index.php/comtech/article/view/7993k-nearest neighbor (knn)recommendation systembangkalan’s tourism |
| spellingShingle | Devie Rosa Anamisa Achmad Jauhari Fifin Ayu Mufarroha K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism ComTech k-nearest neighbor (knn) recommendation system bangkalan’s tourism |
| title | K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism |
| title_full | K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism |
| title_fullStr | K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism |
| title_full_unstemmed | K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism |
| title_short | K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism |
| title_sort | k nearest neighbors method for recommendation system in bangkalan s tourism |
| topic | k-nearest neighbor (knn) recommendation system bangkalan’s tourism |
| url | https://journal.binus.ac.id/index.php/comtech/article/view/7993 |
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