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  <title>METODE REGRESI RIDGE UNTUK MENGATASI   MULTIKOLINIERITAS PADA INDIKATOR KONSTRUKSI DI PROVINSI ACEH</title>
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 <name type="Personal Name" authority="">
  <namePart>Siti Zakirah</namePart>
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   <roleTerm type="text">Primary Author</roleTerm>
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  <place>
   <placeTerm type="text">Banda Aceh</placeTerm>
   <publisher>Universitas Syiah Kuala</publisher>
   <dateIssued>2017</dateIssued>
  </place>
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  <languageTerm type="text">Indonesia</languageTerm>
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 <note>ABSTRAK&#13;
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Penelitian ini dilakukan untuk mengamati hubungan yang terjadi antara indikator-indikator konstruksi (sebagai variabel bebas), dan nilai konstruksi (sebagai variabel tak bebas). Pengujian asumsi klasik dasar dari kriteria BLUE (Best Linear Unbiased Estimator) yang dilakukan menunjukkan adanya unsur multikolinieritas. Prosedur regresi ridge dilakukan untuk menyelesaikan persoalan multikolinieritas. Dalam regresi ridge, prosedur meminimalkan varians estimator ? untuk mendapatkan hasil prediksi yang optimal dilakukan dengan menetapkan nilai ridge trace c = 0,02. Model regresi ridge yang diperoleh adalah: &#13;
 &quot;Y&quot;  ?^&quot;*&quot;   = 0,0978X1 + 0,6830X2 + 0,2298X3 + 0,0224X4 + 0,0116X5 + 0,0800X6, dan optimalitas model tersebut terpenuhi dengan memastikan bahwa semua nilai VIF (variance inflation factor) masing-masing variabel bebas telah bernilai lebih kecil dari pada 10.&#13;
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Kata kunci: multikolinieritas, VIF, regresi ridge, ridge trace.&#13;
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ABSTRACT&#13;
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This study was conducted to examine the relationship between indicators of construction (as independent variables), and the value of construction (as dependent variables). Classic assumption test basis of criteria BLUE (Best Linear Unbiased Estimator) conducted indicative of multicollinearity. Ridge regression procedure done to resolve the issue of multicollinearity. In ridge regression, procedures to minimize the variance estimator ? to get the optimal prediction is done by setting the value ridge trace c = 0,02. Ridge regression models obtained are: &quot;Y&quot;  ?^&quot;*&quot;   = 0,0978X1 + 0,6830X2 + 0,2298X3 + 0,0224X4 + 0,0116X5 + 0,0800X6, and the optimality of the model are met by ensuring that all VIF (variance inflation factor) of each independent variable was worth less than 10.&#13;
Keywords: multicollinearity, VIF, ridge regression, ridge trace.&#13;
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  <physicalLocation>ELECTRONIC THESES AND DISSERTATION Universitas Syiah Kuala</physicalLocation>
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