{"title":"K-Means在明古鲁省旅游景点组团旅游中的应用","authors":"Harjoni Saputra, H. Sari, Lena Elfianty","doi":"10.53697/jkomitek.v2i1.790","DOIUrl":null,"url":null,"abstract":"The management of attractions needs the support of Bengkulu Province Tourism Office, which is one of the government agencies in Bengkulu province. Every month a data collection on the number of visits to each tourist attraction in Bengkulu province will be carried out. From the large number of visit data that is calculated every year, Bengkulu provincial tourism office has difficulty in knowing the number of tourists and also difficulties in identifying which tourist objects are visited the most and least visited. The application for grouping tourist visits to tourist objects in Bengkulu province was made using the Visual Basic.net programming language and SQL Server 2008 data base by applying the Clustering Method, namely K-Means, the grouping was based on data on the number of tourist attraction visits for 12 months from January 2019 to with December 2019 which was divided into 4 attributes, namely Individual Adults (DP), Group Adults (DR), Individual Children (AP), and Group Children (AR). The data clustering process was carried out by dividing into 3 groups, namely Cluster 1 (high number of tourist visits), Cluster 2 (medium number of tourists) and cluster 3 (low number of tourists). Based on the results of the tests that have been carried out, the application for grouping tourist visits to tourist attractions in Bengkulu province has been successfully carried out, and can provide information based on 3 groups, namely Cluster C1 (high), Cluster C2 (medium) and Cluster C3 (low), as well as functionalities of the application. has worked as expected.","PeriodicalId":371693,"journal":{"name":"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)","volume":"140 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of K-Means In Grouping Tourist Visits To Bengkulu Province Tourist Attractions\",\"authors\":\"Harjoni Saputra, H. 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The application for grouping tourist visits to tourist objects in Bengkulu province was made using the Visual Basic.net programming language and SQL Server 2008 data base by applying the Clustering Method, namely K-Means, the grouping was based on data on the number of tourist attraction visits for 12 months from January 2019 to with December 2019 which was divided into 4 attributes, namely Individual Adults (DP), Group Adults (DR), Individual Children (AP), and Group Children (AR). The data clustering process was carried out by dividing into 3 groups, namely Cluster 1 (high number of tourist visits), Cluster 2 (medium number of tourists) and cluster 3 (low number of tourists). 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引用次数: 0
摘要
景点的管理需要明库鲁省旅游局的支持,这是明库鲁省的政府机构之一。每个月将收集到明古鲁省每个旅游景点的参观人数的数据。从每年计算的大量旅游数据来看,明库鲁省旅游局很难知道游客的数量,也很难确定哪些旅游景点参观得最多,哪些旅游景点参观得最少。利用Visual Basic.net编程语言和SQL Server 2008数据库,采用K-Means聚类方法对明古鲁省旅游景点的游客访问量进行分组,分组基于2019年1月至2019年12月12个月的旅游景点访问量数据,分为4个属性,即成人个体(DP)、成人群体(DR)、儿童个体(AP)和儿童群体(AR)。将数据聚类过程分为3组,即集群1(高游客访问量)、集群2(中等游客数量)和集群3(低游客数量)。根据已经进行的测试结果,明古鲁省旅游景点分组旅游应用程序已经成功实施,可以提供基于3组的信息,即C1组(高)、C2组(中)和C3组(低),以及应用程序的功能。效果如预期。
Application of K-Means In Grouping Tourist Visits To Bengkulu Province Tourist Attractions
The management of attractions needs the support of Bengkulu Province Tourism Office, which is one of the government agencies in Bengkulu province. Every month a data collection on the number of visits to each tourist attraction in Bengkulu province will be carried out. From the large number of visit data that is calculated every year, Bengkulu provincial tourism office has difficulty in knowing the number of tourists and also difficulties in identifying which tourist objects are visited the most and least visited. The application for grouping tourist visits to tourist objects in Bengkulu province was made using the Visual Basic.net programming language and SQL Server 2008 data base by applying the Clustering Method, namely K-Means, the grouping was based on data on the number of tourist attraction visits for 12 months from January 2019 to with December 2019 which was divided into 4 attributes, namely Individual Adults (DP), Group Adults (DR), Individual Children (AP), and Group Children (AR). The data clustering process was carried out by dividing into 3 groups, namely Cluster 1 (high number of tourist visits), Cluster 2 (medium number of tourists) and cluster 3 (low number of tourists). Based on the results of the tests that have been carried out, the application for grouping tourist visits to tourist attractions in Bengkulu province has been successfully carried out, and can provide information based on 3 groups, namely Cluster C1 (high), Cluster C2 (medium) and Cluster C3 (low), as well as functionalities of the application. has worked as expected.