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  <title>Analisis Data kemiskinan Provinsi DKI Jakarta Jawa Barat dengan Metode geographically Weighted Logistic Regression (GWLR)</title>
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 <name type="Personal Name" authority="">
  <namePart>RESIWATI FAJRINA MUSTIQA ZAIN</namePart>
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   <roleTerm type="text">Primary Author</roleTerm>
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  <place>
   <placeTerm type="text">Bandung</placeTerm>
   <publisher>Magister Statistika Terapan</publisher>
   <dateIssued>2016</dateIssued>
  </place>
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  <languageTerm type="code">id</languageTerm>
  <languageTerm type="text">Indonesia</languageTerm>
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  <extent>xix, 200 hlm. Ilus ; 29 cm</extent>
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 <note>Kemiskinan adalah masalah sentral yang mendunia dan hingga kini telah &#13;
menjadi isu sentral di belahan bumi manapun termasuk Indonesia, sehingga &#13;
penting untuk mengetahui variabel-variabel apa yang dapat mempengaruhi &#13;
kemiskinan suatu wilayah. &#13;
&#13;
Dalam menentukan suatu wilayah tergolong miskin atau tidak, analisis yang &#13;
digunakan biasanya masih bersifat global. Sementara kondisi kemiskinan &#13;
suatu wilayah sangat mungkin dipengaruhi oleh lokasi atau kondisi geografis &#13;
wilayah tersebut, termasuk posisinya terhadap wilayah lain di sekitamya. &#13;
Salah satu metode statistika yang dapat digunakan untuk menganalisis &#13;
heterogenitas spasial tersebut adalah Geographically Weighted Logistic &#13;
Regression (GWLR). &#13;
&#13;
Dalam penelitian ini diperolah bahwa model Geographically Weighted &#13;
Logistic Regression (GWLR) dengan pembobot adaptive kernel gaussian &#13;
lebih cocok untuk memodelkan kemiskinan di Provinsi DKI Jakarta dan Jawa &#13;
Barat dibandingkan model regresi logistik global. Dengan model GWLR &#13;
terlihat bahwa pengaruh variabel penjelas terhadap kemiskinan bervariasi &#13;
pada setiap wilayah. &#13;
&#13;
4. Abstract : &#13;
&#13;
Poverty is a central issue in worldwide, including in Indonesia, so it is &#13;
important to identify the variables that can affect the poverty of a region. &#13;
The determination poverty level in a region, are usually based on the global &#13;
analysis. While the poverty level of a region very likely influenced by the &#13;
location or geographical conditions of the region, including the position of &#13;
the other areas in the vicinity. One statistical method that can be used to &#13;
analyze the spatial heterogeneity is Geographically Weighted Logistic &#13;
Regression(G WLR). &#13;
&#13;
In this research obtained that the Geographically Weighted Logistic &#13;
Regression (GWLR) model with an adaptive gaussian kernel weighting matrix &#13;
more suitable for modeling poverty in Jakarta and West Java than global &#13;
logistic regression model. GWLR model shows that the influence of &#13;
explanatory variables on poverty varies in each region. &#13;
&#13;
</note>
 <note type="statement of responsibility">Resiwati Fajrina Mustiqa Zain</note>
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  <topic>Kemiskinan adalah masalah sentral yang mendunia da</topic>
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 <classification>519.5 Res a</classification>
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