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  <title>Perbandingan kinerja regresi logistik dan neural network dalam pengklasifikasian objek :</title>
  <subTitle>studi kasus klasifikasi angkatan kerja di Kabupaten Kepahiang Provinsi Bengkulu</subTitle>
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 <name type="Personal Name" authority="">
  <namePart>EKO FAJARIYANTO</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>2017</dateIssued>
  </place>
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  <languageTerm type="code">id</languageTerm>
  <languageTerm type="text">Indonesia</languageTerm>
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  <extent>xv,; 94 hlm,;29 cm</extent>
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 <note>Ketenagakerjaan merupakan permasalahan yang selalu mendapatkan perhatian &#13;
serius dari pemerintah. Permasalahan kualitas sumber daya manusia, motivasi &#13;
dan budaya kerja menjadi faktor yang berpengaruh dalam permasalahan &#13;
angkatan kerja. Klasifikasi menjadi penting sebagai alat evaluasi dan penarikan &#13;
kesimpulan bagi permasalahan angkatan kerja. Metode klasifikasi itu sendiri &#13;
terdiri dari metode konvensional yang membutuhkan asumsi serta advanced &#13;
method yang terlepas dari berbagai persyaratan asumsi. Penelitian ini &#13;
menggunakan beberapa metode klasifikasi antara lain Regresi Logistik, &#13;
algoritma Backpropagation dan Backpropagation dengan penambahan &#13;
momentum. Basil penelitian ini menunjukkan pemilihan variabel dan &#13;
penambahan momentum dapat meningkatkan ketepatan klasifikasi &#13;
Backpropagation dengan penambahan momentum. &#13;
&#13;
4. Abstract : &#13;
&#13;
Employment . is a problem that always get serious attention from the &#13;
government. The problem of human resources, motivation and work culture &#13;
became an influential factor in the problems of the labor force. Classification &#13;
became more important as a means of evaluation and conclusion for the &#13;
problems of the labor force. Classification method itself consists of &#13;
conventional methods that require assumptions as well as advanced methods &#13;
that apart from the various requirements of assumptions. This study uses &#13;
several methods of classification among others Logistic Regression, &#13;
Backpropagation algorithm and Backpropagation with momentum. The results &#13;
of this study indicate the selection of variables and addition of momentum can &#13;
improve the classification accuracy of Backpropagation with momentum. &#13;
&#13;
</note>
 <note type="statement of responsibility">Eko Fajariyanto</note>
 <subject authority="">
  <topic>Regresi Logistik   2. Backpropagation   3. Backpro</topic>
 </subject>
 <classification>519.5 Eko p</classification>
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  <physicalLocation>Perpustakaan Universitas Padjadjaran Kementerian Riset Teknologi dan Pendidikan Tinggi</physicalLocation>
  <shelfLocator>519.5 Eko p/R.14.27.2</shelfLocator>
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