ANALISIS FAKTOR KEMISKINAN KABUPATEN/KOTA DI KALIMANTAN, SULAWESI, BALI DAN NUSA TENGGARA

Derita Lamtiar Pasaribu, Fajar Restuhadi, Evy Maharani
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Abstract

Poverty alleviation planning should be started with data analysis in advance. One of the poverty data sources available in Indonesia is the Regency/City Poverty Data and Information Catalog, published by the Central Statistics Agency (BPS). From the catalog published in the time series can be observed where the poverty rate decreases along with the increasing budget for poverty reduction. In 2005, there were 35.1 million people (15.97%) of the country living under the poverty line and in 2015 reduced to be 28.51 million people which equaled 11.13% of the total population of Indonesia. This research aims to analyze poverty factors in 175 regents and cities located on the islands of Kalimantan, Sulawesi, Bali, and Nusa Tenggara using data from BPS. The principal component analysis (PCA) is the main analytical instrument that was used in this research. The poverty data from BPS has 9 aspects/factors and PCA analysis results in the same number of main components/factors. The difference in the result of these two observations is seen in variable members in each component that could be occurred because BPS conducts grouping of variables before the population data collection gets started, while PCA classifies variables based on data that has been collected or after the population data collection is completed. PCA results can be utilized for further research purposes such as regional clustering, implementation of evaluation, and planning. Meanwhile, the BPS poverty aspect displayed in a more structured arrangement, makes it is easier to observe for publications and more practical to use when conducting population data collection.
扶贫规划要从数据分析入手。印度尼西亚现有的贫困数据来源之一是中央统计局(BPS)出版的《摄政/城市贫困数据和信息目录》。从时间序列中公布的目录可以观察到,哪里的贫困率随着减贫预算的增加而下降。2005年,全国有3510万人(15.97%)生活在贫困线以下,2015年减少到2851万人,相当于印度尼西亚总人口的11.13%。本研究旨在利用BPS的数据分析加里曼丹岛、苏拉威西岛、巴厘岛和努沙登加拉岛175个县和城市的贫困因素。主成分分析(PCA)是本研究中使用的主要分析工具。BPS的贫困数据有9个方面/因素,PCA分析得出的主要成分/因素数量相同。这两种观察结果的差异可以从每个组件中的变量成员中看到,因为BPS在开始总体数据收集之前对变量进行分组,而PCA根据已收集的数据或在总体数据收集完成后对变量进行分类。PCA的结果可以用于进一步的研究目的,如区域聚类、实施评估和规划。同时,BPS贫困方面以更结构化的安排显示,使出版物更容易观察,在进行人口数据收集时更实际使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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