印度尼西亚中爪哇省多维贫困的聚类分析

Yuniasih Purwanti
{"title":"印度尼西亚中爪哇省多维贫困的聚类分析","authors":"Yuniasih Purwanti","doi":"10.56655/jid.v2i2.132","DOIUrl":null,"url":null,"abstract":"In Indonesia, poverty continues to be a major issue. According to Statistics Indonesia, there were 9.78% of the population living in poverty in 2020, with Java Island accounting for 50% of the nation’s total poor people. Furthermore, poverty reduction is the primary concern due to Central Java’s high poverty rate, which is still higher than the national average, so it has become a shared challenge. This study measured poverty by clusters based on a variety of deprivations that residents of 35 regencies or municipalities in Central Java Province experienced. Additionally, 16 poverty indicators based on the criteria and income of the poor and underprivileged from the Ministry of Social Affairs comprised the variables used in this study. Moreover, by selecting the optimal cluster, characteristic poverty was obtained employing fuzzy C-means (FCM) as cluster analysis. In addition, each municipality/regency that shares a similarity indicator with another municipality/regency was categorized into one cluster. The clusters were fundamental to understanding the determinants of poverty and poverty alleviation programs. According to the clustering results, there were four clusters considered the best cluster, and it demonstrated that the indicators most associated with poverty were non-food expenditure, drinking water adequacy, access to sanitation facilities, the open unemployment rate, and television ownership.","PeriodicalId":485399,"journal":{"name":"Jurnal Inovasi Daerah","volume":" 28","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CLUSTERING ANALYSIS OF MULTIDIMENSIONAL POVERTY IN CENTRAL JAVA PROVINCE, INDONESIA\",\"authors\":\"Yuniasih Purwanti\",\"doi\":\"10.56655/jid.v2i2.132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In Indonesia, poverty continues to be a major issue. According to Statistics Indonesia, there were 9.78% of the population living in poverty in 2020, with Java Island accounting for 50% of the nation’s total poor people. Furthermore, poverty reduction is the primary concern due to Central Java’s high poverty rate, which is still higher than the national average, so it has become a shared challenge. This study measured poverty by clusters based on a variety of deprivations that residents of 35 regencies or municipalities in Central Java Province experienced. Additionally, 16 poverty indicators based on the criteria and income of the poor and underprivileged from the Ministry of Social Affairs comprised the variables used in this study. Moreover, by selecting the optimal cluster, characteristic poverty was obtained employing fuzzy C-means (FCM) as cluster analysis. In addition, each municipality/regency that shares a similarity indicator with another municipality/regency was categorized into one cluster. The clusters were fundamental to understanding the determinants of poverty and poverty alleviation programs. According to the clustering results, there were four clusters considered the best cluster, and it demonstrated that the indicators most associated with poverty were non-food expenditure, drinking water adequacy, access to sanitation facilities, the open unemployment rate, and television ownership.\",\"PeriodicalId\":485399,\"journal\":{\"name\":\"Jurnal Inovasi Daerah\",\"volume\":\" 28\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jurnal Inovasi Daerah\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.56655/jid.v2i2.132\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Inovasi Daerah","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.56655/jid.v2i2.132","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

在印度尼西亚,贫困仍然是一个主要问题。根据印尼统计局的数据,2020 年印尼贫困人口占总人口的 9.78%,其中爪哇岛的贫困人口占全国贫困人口总数的 50%。此外,由于中爪哇的贫困率居高不下,且仍高于全国平均水平,因此减贫成为首要关注的问题,这也成为一个共同的挑战。本研究根据中爪哇省 35 个县或市的居民所经历的各种贫困状况,对贫困进行了分组测量。此外,社会事务部根据穷人和弱势群体的标准和收入制定的 16 项贫困指标也构成了本研究的变量。此外,通过选择最佳聚类,利用模糊 C-means (FCM) 聚类分析法获得了贫困特征。此外,与另一个市/区有相似指标的每个市/区都被归入一个聚类。聚类对于了解贫困的决定因素和扶贫计划至关重要。根据聚类结果,有四个聚类被认为是最佳聚类,这表明与贫困最相关的指标是非食品支出、饮用水充足率、卫生设施使用率、公开失业率和电视机拥有率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CLUSTERING ANALYSIS OF MULTIDIMENSIONAL POVERTY IN CENTRAL JAVA PROVINCE, INDONESIA
In Indonesia, poverty continues to be a major issue. According to Statistics Indonesia, there were 9.78% of the population living in poverty in 2020, with Java Island accounting for 50% of the nation’s total poor people. Furthermore, poverty reduction is the primary concern due to Central Java’s high poverty rate, which is still higher than the national average, so it has become a shared challenge. This study measured poverty by clusters based on a variety of deprivations that residents of 35 regencies or municipalities in Central Java Province experienced. Additionally, 16 poverty indicators based on the criteria and income of the poor and underprivileged from the Ministry of Social Affairs comprised the variables used in this study. Moreover, by selecting the optimal cluster, characteristic poverty was obtained employing fuzzy C-means (FCM) as cluster analysis. In addition, each municipality/regency that shares a similarity indicator with another municipality/regency was categorized into one cluster. The clusters were fundamental to understanding the determinants of poverty and poverty alleviation programs. According to the clustering results, there were four clusters considered the best cluster, and it demonstrated that the indicators most associated with poverty were non-food expenditure, drinking water adequacy, access to sanitation facilities, the open unemployment rate, and television ownership.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信