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All Journal Jurnal Ilmu Pertanian Indonesia Indonesian Journal of Statistics and Its Applications
. Indahwati
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Agriculture, Biological Sciences & Forestry Computer Science & IT Mathematics Other

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IDENTIFIKASI PEUBAH PENCIRI RUMAH TANGGA MISKIN DAN RUMAH TANGGA YANG SEDIKIT DI ATAS GARIS KEMISKINAN . Indahwati
Jurnal Ilmu Pertanian Indonesia Vol. 11 No. 2 (2006): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1220.14 KB)

Abstract

Poverty still becomes main problem in this country. The categorization of the poor or not poor household based on the poverty line is difficult to be performed in practice. Therefore, it is needed to find other variables that could be used to characterize poor household. In addition, because the households that almost poor could become poor easily, it is also needed to analyze the probability of these household become poor household. This research use Susenas Data Kor 2003 from Badan Pusat Statistik for Jawa Barat province which includes explanatory variables: house physical condition, protein consumption, type of fuel/energy, ownership of asset, and also head of household characteristic. Result from logistic regression analysis shows some poor household characteristics: floor area per capita <= 8 m2, there's no closet, final place of feces exile is not tank, closet type is not goose neck, do not consume food with high protein, don't have motor vehicle or saving, electrics do not use gauge, head of household is a woman, amount of household members >= 5, head of household's age > 55 years. For the urban area, another characteristics are: don't have farmland, do not use gas, do not use electrics from PLN, using firewood, head of household's work status is erratic, head of household's education maximum is elementary school. For rural area, another characteristics are: house is not property of them selves; most of wall not made by cement; don't have precious goods, store, or productive asset; do not use kerosene. Ordinal logistic regression obtain model that explain relation between household status and its independent variables. However this model can not explain probability of almost poor household become poor household, because the household exactly have higher opportunity to be categorized as not poor. Probability of almost poor household categorized as poor household only 9.59% for urban area and 11.79% for rural area.
IDENTIFIKASI KARAKTERISTIK ANAK PUTUS SEKOLAH DI JAWA BARAT DENGAN REGRESI LOGISTIK Perhati, Tina Aris; Indahwati, .; Susetyo, Budi
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v1i1.51

Abstract

School dropouts are the problem in education which is the condition of children who do not have the opportunity to complete their education that they couldnt obtain degree certificate due to certain factors. Based on SUSENAS 2013, there is 2.15% of children aged 7-15 years old in West Java who dropped out of school. Three aspects that have great potential on the incidence of school dropouts are characteristic of social, economy, and demography. This study uses logistic regression analysis to determine the effect of school dropouts by the three aspects. The results of logistic regression analysis at 5% significance level indicates that the characteristics of social, economy, and demography that have significant effect on the incidence of school dropouts are the low education of household head, more than four household members, less than the poverty line household expenditure per capita, residence location in urban areas, and boys. The resulting model is sufficientfor estimation with the sensitivity value of 70.20% and the area under the ROC curve of 76.42%. Keywords: logistic regression, ROC curve, school children, sensitivity.
Co-Authors Budi Susetyo Perhati, Tina Aris