{"title":"K-Means 算法在雅加达省人口密度聚类中的应用","authors":"Frisma Handayanna, S. Sunarti","doi":"10.52158/jacost.v5i1.477","DOIUrl":null,"url":null,"abstract":"DKI Jakarta Province is an attraction for immigrants. If the population increases, if it cannot be resolved and managed well, it will result in bad things such as increasing the number of unemployed and affecting economic growth. Population data is used to help group regions based on population density in DKI Jakarta Province in 2019-2022 using the K-Means clustering method. From the results of the research, it provides a solution for the government to pay attention to population groups with the aim of preventing population density because it causes bad effects, so that community welfare is more guaranteed, so grouping (clustering) of provinces in DKI Jakarta is needed to provide information for people who wish to live in the Province DKI Jakarta. The research proves that the test results carried out clustering iterations of population density data were obtained in three iterations. For the results obtained by calculations using the K-Means method and using the rapidminer application, the results obtained were of the same value, namely the cluster with the highest population density of three districts/cities, namely South Jakarta, East Jakarta and West Jakarta whose population density continues to increase.","PeriodicalId":151855,"journal":{"name":"Journal of Applied Computer Science and Technology","volume":" 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Penerapan Algoritma K-Means Untuk Mengelompokkan Kepadatan Penduduk Di Provinsi DKI Jakarta\",\"authors\":\"Frisma Handayanna, S. Sunarti\",\"doi\":\"10.52158/jacost.v5i1.477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"DKI Jakarta Province is an attraction for immigrants. If the population increases, if it cannot be resolved and managed well, it will result in bad things such as increasing the number of unemployed and affecting economic growth. Population data is used to help group regions based on population density in DKI Jakarta Province in 2019-2022 using the K-Means clustering method. From the results of the research, it provides a solution for the government to pay attention to population groups with the aim of preventing population density because it causes bad effects, so that community welfare is more guaranteed, so grouping (clustering) of provinces in DKI Jakarta is needed to provide information for people who wish to live in the Province DKI Jakarta. The research proves that the test results carried out clustering iterations of population density data were obtained in three iterations. For the results obtained by calculations using the K-Means method and using the rapidminer application, the results obtained were of the same value, namely the cluster with the highest population density of three districts/cities, namely South Jakarta, East Jakarta and West Jakarta whose population density continues to increase.\",\"PeriodicalId\":151855,\"journal\":{\"name\":\"Journal of Applied Computer Science and Technology\",\"volume\":\" 8\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Computer Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52158/jacost.v5i1.477\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Computer Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52158/jacost.v5i1.477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
DKI 雅加达省是一个吸引移民的地方。如果人口增加,如果不能很好地解决和管理,将会导致失业人数增加、影响经济增长等不良后果。利用 K-Means 聚类方法,根据 2019-2022 年 DKI 雅加达省的人口密度,使用人口数据帮助进行地区分组。从研究结果来看,它为政府提供了一个解决方案,即关注人口分组,目的是防止人口密度过高造成不良影响,使社区福利更有保障,因此需要对 DKI 雅加达省进行分组(聚类),为希望在 DKI 雅加达省生活的人们提供信息。研究证明,对人口密度数据进行聚类迭代的测试结果是在三次迭代中获得的。对于使用 K-Means 方法和 rapidminer 应用程序计算得出的结果,所得到的结果具有相同的价值,即雅加达南部、雅加达东部和雅加达西部这三个地区/城市的人口密度最高的聚类,其人口密度持续增加。
Penerapan Algoritma K-Means Untuk Mengelompokkan Kepadatan Penduduk Di Provinsi DKI Jakarta
DKI Jakarta Province is an attraction for immigrants. If the population increases, if it cannot be resolved and managed well, it will result in bad things such as increasing the number of unemployed and affecting economic growth. Population data is used to help group regions based on population density in DKI Jakarta Province in 2019-2022 using the K-Means clustering method. From the results of the research, it provides a solution for the government to pay attention to population groups with the aim of preventing population density because it causes bad effects, so that community welfare is more guaranteed, so grouping (clustering) of provinces in DKI Jakarta is needed to provide information for people who wish to live in the Province DKI Jakarta. The research proves that the test results carried out clustering iterations of population density data were obtained in three iterations. For the results obtained by calculations using the K-Means method and using the rapidminer application, the results obtained were of the same value, namely the cluster with the highest population density of three districts/cities, namely South Jakarta, East Jakarta and West Jakarta whose population density continues to increase.