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ANALISIS FAKTOR KEMISKINAN KABUPATEN/KOTA DI KALIMANTAN, SULAWESI, BALI DAN NUSA TENGGARA
Author(s) -
Derita Lamtiar Pasaribu,
Fajar Restuhadi,
Evy Maharani
Publication year - 2021
Publication title -
dinamika pertanian/dinamika pertanian
Language(s) - English
Resource type - Journals
eISSN - 2549-7960
pISSN - 0215-2525
DOI - 10.25299/dp.2019.vol35(2).7697
Subject(s) - poverty , population , geography , data collection , poverty reduction , socioeconomics , principal component analysis , agency (philosophy) , economic growth , statistics , demography , economics , mathematics , sociology , social science
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.

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