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  <title>Perbandingan Backproagation Neural Network Dan Learning vector Quantization :</title>
  <subTitle>Studi Kasus Klasifikasi Daerah tertinggal di Indonesia Tahun 2015</subTitle>
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 <name type="Personal Name" authority="">
  <namePart>ELIJOI NAIBAHO</namePart>
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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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 <note>Kemajuan pembangunan daerah dan peningkatan kesejahteraan rakyat di &#13;
Indonesia tidak selalu sama dan merata. Perbedaan kondisi geografis, sumber &#13;
daya alam, infrastruktur, sosial budaya dan kapasitas sumber daya manusia &#13;
menyebabkan masih adanya kesenjangan an tar wilayah. Tantangan &#13;
pengembangan wilayah di Indonesia adalah percepatan pembangunan &#13;
kabupaten tertinggal dan mengurangi ketimpangan antar wilayah. Penetapan &#13;
daerah tcrtinggal atau tidak tertinggal merupakan salah satu kasus klasifikasi. &#13;
Salah satu metode untuk klasifikasi adalah Neural Network. Penelitian ini &#13;
membandingkan ketepatan klasifikasi antara Back Propagation Neural &#13;
Network (BPNN) dan Learning Vector Quantization (L VQ). Hasil penelitian &#13;
ini menunjukkan bahwa LVQ menghasilkan ketepatan yang lebih besar dalam &#13;
menentukan daerah tertinggaI dibandingkan metode BPNN. &#13;
&#13;
4. Abstract &#13;
&#13;
The progress of regional development and the improvement of people's &#13;
welfare in Indonesia is not always same and even. The differences in &#13;
geography, natural resources, infrastructures, social cultures, and the &#13;
capacity of human resources causing disparities between regions . The &#13;
challenges of regional development is to accelerate the development of &#13;
underdeveloped regency, and to reduce regional disparities. Determining a &#13;
regency as underdeveloped or developed regency is one of the classification &#13;
cases. One of the main methods for classification is Neural Network. The aim &#13;
of this study was to compare the accuracy of classification between Back &#13;
Propagation Neural Network (BPNN) and Learning Vector Quantization &#13;
(LVQ). The result of this study shows that LVQ produce more accuracy in &#13;
clasiffying underdeveloped regency compares to BPNN method. &#13;
&#13;
</note>
 <note type="statement of responsibility">Elijoi Naibaho</note>
 <subject authority="">
  <topic>Studi Kasus: Klasifikasi Daerah Tertinggal di Indo</topic>
 </subject>
 <classification>5195</classification>
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