班腾省贫困星团分析采用了k -手段

Matius Tadi, B. Ningsi
{"title":"班腾省贫困星团分析采用了k -手段","authors":"Matius Tadi, B. Ningsi","doi":"10.46306/lb.v4i1.224","DOIUrl":null,"url":null,"abstract":"Poverty is a special problem that needs to be discussed in Indonesia. Because poverty that occurs in a certain area will have an impact on development in Indonesia. Banten province has a percentage of 6.04 percent with a total poverty of 576.62 thousand poor people. Given this, the Provincial Government of Banten requires knowledge of district/city socio-economic data in Banten Province. This study aims to cluster regencies/cities in Banten Province using the K-Means method. The K-Means method is a clustering technique that aims to group data based on the same characteristics. The variables observed in this study were the percentage of poor people aged 15 and over who had graduated from elementary/junior high school, the percentage of poor people aged 15 and over who had finished high school and above, the percentage of poor people aged 15 and over by district/city not working, the percentage of poor people aged 15 Years and over by district/city working in the informal sector, percentage of poor population aged 15 and over by district/city working in the formal sector, percentage of expenditure per capita for food by district/city and poor status. the results of the analysis showed that the district/city poverty clustering in Banten Province using the K-means method obtained 2 clusters, namely cluster 1 consisting of Tangerang City, Cilegon City and South Tangerang City and cluster 2 consisting of Pandeglang Regency, Lebak Regency, Tangerang, Serang Regency, Serang City. And formation 2 cluster has a good structural test value based on the results of the silhouette index so that 2 cluster has the best accuracy value compared to 3 clusters and 4 cluster.","PeriodicalId":31699,"journal":{"name":"JMPM Jurnal Matematika dan Pendidikan Matematika","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ANALISIS KLASTER KEMISKINAN KABUPATEN KOTA DI PROVINSI BANTEN MENGGUNAKAN METODE K-MEANS\",\"authors\":\"Matius Tadi, B. Ningsi\",\"doi\":\"10.46306/lb.v4i1.224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Poverty is a special problem that needs to be discussed in Indonesia. Because poverty that occurs in a certain area will have an impact on development in Indonesia. Banten province has a percentage of 6.04 percent with a total poverty of 576.62 thousand poor people. Given this, the Provincial Government of Banten requires knowledge of district/city socio-economic data in Banten Province. This study aims to cluster regencies/cities in Banten Province using the K-Means method. The K-Means method is a clustering technique that aims to group data based on the same characteristics. The variables observed in this study were the percentage of poor people aged 15 and over who had graduated from elementary/junior high school, the percentage of poor people aged 15 and over who had finished high school and above, the percentage of poor people aged 15 and over by district/city not working, the percentage of poor people aged 15 Years and over by district/city working in the informal sector, percentage of poor population aged 15 and over by district/city working in the formal sector, percentage of expenditure per capita for food by district/city and poor status. the results of the analysis showed that the district/city poverty clustering in Banten Province using the K-means method obtained 2 clusters, namely cluster 1 consisting of Tangerang City, Cilegon City and South Tangerang City and cluster 2 consisting of Pandeglang Regency, Lebak Regency, Tangerang, Serang Regency, Serang City. And formation 2 cluster has a good structural test value based on the results of the silhouette index so that 2 cluster has the best accuracy value compared to 3 clusters and 4 cluster.\",\"PeriodicalId\":31699,\"journal\":{\"name\":\"JMPM Jurnal Matematika dan Pendidikan Matematika\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMPM Jurnal Matematika dan Pendidikan Matematika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46306/lb.v4i1.224\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMPM Jurnal Matematika dan Pendidikan Matematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46306/lb.v4i1.224","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

贫困是印尼需要讨论的一个特殊问题。因为发生在某个地区的贫困会对印尼的发展产生影响。万丹省的贫困率为6.04%,贫困人口总数为576.62万人。鉴于此,万丹省政府需要了解万丹省的地区/城市社会经济数据。本研究旨在使用K-Means方法对万丹省的县域/城市进行聚类。K-Means方法是一种聚类技术,旨在根据相同的特征对数据进行分组。本研究中观察到的变量包括15岁及以上小学/初中毕业的贫困人口比例、15岁及以上高中毕业的贫困人口比例、各地区/城市15岁及以上不工作的贫困人口比例、各地区/城市15岁及以上在非正规部门工作的贫困人口比例、按地区/城市分列的在正规部门工作的15岁及以上贫困人口百分比、按地区/城市分列的人均粮食支出百分比和贫困状况。分析结果表明,采用K-means方法对万丹省的区/市贫困进行聚类得到2个聚类,即聚类1由坦格朗市、奇列根市和南坦格朗市组成,聚类2由盘德朗县、勒巴克县、坦格朗县、雪朗县、雪朗市组成。基于轮廓指数结果,2型聚类具有较好的结构测试值,因此2型聚类相对于3型聚类和4型聚类具有最好的精度值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ANALISIS KLASTER KEMISKINAN KABUPATEN KOTA DI PROVINSI BANTEN MENGGUNAKAN METODE K-MEANS
Poverty is a special problem that needs to be discussed in Indonesia. Because poverty that occurs in a certain area will have an impact on development in Indonesia. Banten province has a percentage of 6.04 percent with a total poverty of 576.62 thousand poor people. Given this, the Provincial Government of Banten requires knowledge of district/city socio-economic data in Banten Province. This study aims to cluster regencies/cities in Banten Province using the K-Means method. The K-Means method is a clustering technique that aims to group data based on the same characteristics. The variables observed in this study were the percentage of poor people aged 15 and over who had graduated from elementary/junior high school, the percentage of poor people aged 15 and over who had finished high school and above, the percentage of poor people aged 15 and over by district/city not working, the percentage of poor people aged 15 Years and over by district/city working in the informal sector, percentage of poor population aged 15 and over by district/city working in the formal sector, percentage of expenditure per capita for food by district/city and poor status. the results of the analysis showed that the district/city poverty clustering in Banten Province using the K-means method obtained 2 clusters, namely cluster 1 consisting of Tangerang City, Cilegon City and South Tangerang City and cluster 2 consisting of Pandeglang Regency, Lebak Regency, Tangerang, Serang Regency, Serang City. And formation 2 cluster has a good structural test value based on the results of the silhouette index so that 2 cluster has the best accuracy value compared to 3 clusters and 4 cluster.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
10 weeks
期刊最新文献
Analisis Pemetaan Isomorfisma Untuk Menentukan Dimensi Ruang Vektor Ł Perbandingan Metode Exponential Smoothing Event Based dengan Metode Winter Exponential Smoothing pada Peramalan Harga Cabai Merah di Kota Medan Investigasi pemanfaatan geogebra untuk pembelajaran matematika di Indonesia: Sebuah analisis bibliometrik Hubungan self regulated learning dan hasil belajar matematika peserta didik kelas V SDN Ceger 02 Self-Efficacy dan sikap mahasiswa terhadap matematika
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1