Identifikasi Faktor Penyebab Pengangguran di Kota Medan Kecamatan Medan Selayang dengan Menggunakan Metode Analisis Faktor

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X7 Pearson Correlation .111 .297** .127 .151 .225* .159 1 .014 .182 .010 .215* .524**

Sig. (2-tailed) .277 .003 .214 .138 .026 .118 .889 .072 .926 .034 .000

N 98 98 98 98 98 98 98 98 98 98 98 98

X8 Pearson Correlation -.018 .106 -.017 .128 .266** .141 .014 1 -.060 -.169 .098 .347**

Sig. (2-tailed) .860 .298 .871 .210 .008 .165 .889 .560 .096 .335 .000

N 98 98 98 98 98 98 98 98 98 98 98 98

X9 Pearson Correlation .211* .206* .316** .211* .270** .089 .182 -.060 1 .008 .295** .549**

Sig. (2-tailed) .037 .042 .002 .037 .007 .385 .072 .560 .942 .003 .000

N 98 98 98 98 98 98 98 98 98 98 98 98

X10 Pearson Correlation -.026 -.076 -.044 -.111 .083 .079 .010 -.169 .008 1 .127 .149

Sig. (2-tailed) .798 .460 .671 .276 .415 .442 .926 .096 .942 .211 .142

N 98 98 98 98 98 98 98 98 98 98 98 98

X11 Pearson Correlation .258* .068 .046 .210* .280** .086 .215* .098 .295** .127 1 .562**

Sig. (2-tailed) .010 .507 .652 .038 .005 .397 .034 .335 .003 .211 .000

N 98 98 98 98 98 98 98 98 98 98 98 98

Y Pearson Correlation .394** .475** .432** .413** .532** .443** .524** .347** .549** .149 .562** 1

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .142 .000

N 98 98 98 98 98 98 98 98 98 98 98 98

*. Correlation is significant at the 0.05 level (2-tailed).

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N 98 98 98 98 98 98 98 98 98 98 98

X8 Pearson Correlation -.018 .106 -.017 .128 .266** .141 .014 1 -.060 .098 .382**

Sig. (2-tailed) .860 .298 .871 .210 .008 .165 .889 .560 .335 .000

N 98 98 98 98 98 98 98 98 98 98 98

X9 Pearson Correlation .211* .206* .316** .211* .270** .089 .182 -.060 1 .295** .554**

Sig. (2-tailed) .037 .042 .002 .037 .007 .385 .072 .560 .003 .000

N 98 98 98 98 98 98 98 98 98 98 98

X11 Pearson Correlation .258* .068 .046 .210* .280** .086 .215* .098 .295** 1 .544**

Sig. (2-tailed) .010 .507 .652 .038 .005 .397 .034 .335 .003 .000

N 98 98 98 98 98 98 98 98 98 98 98

Y Pearson Correlation .403** .494** .445** .438** .523** .433** .527** .382** .554** .544** 1

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

N 98 98 98 98 98 98 98 98 98 98 98

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

CRONBACH ALPHA

Case Processing Summary

N %

Cases Valid 98 100.0

Excludeda 0 .0

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Case Processing Summary

N %

Cases Valid 98 100.0

Excludeda 0 .0

Total 98 100.0

a. Listwise deletion based on all

variables in the procedure.

Reliability Statistics

Cronbach's

Alpha

N of Items

.702

11 ANALISIS FAKTOR

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .601

Bartlett's Test of Sphericity Approx. Chi-Square 112.628

Df 45

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Communalities

Initial Extraction

X1 1.000 .597

X2 1.000 .723

X3 1.000 .672

X4 1.000 .608

X5 1.000 .618

X6 1.000 .542

X7 1.000 .424

X8 1.000 .739

X9 1.000 .507

X11 1.000 .545

Extraction Method: Principal

Component Analysis.

Total Variance Explained

Compon

ent

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

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2 1.426 14.259 37.928 1.426 14.259 37.928 1.559 15.588 31.825

3 1.182 11.817 49.745 1.182 11.817 49.745 1.484 14.837 46.662

4 1.002 10.018 59.763 1.002 10.018 59.763 1.310 13.102 59.763

5 .872 8.722 68.486

6 .822 8.223 76.708

7 .732 7.318 84.027

8 .672 6.718 90.745

9 .519 5.192 95.936

10 .406 4.064 100.000

Extraction Method: Principal Component Analysis.

Component Matrixa Component

1 2 3 4

X1 .446 -.290 -.476 .296

X2 .490 -.404 .565 .018

X3 .468 -.608 .188 .220

X4 .427 .377 -.081 -.526

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X6 .405 .352 .475 -.169

X7 .559 -.043 .141 -.301

X8 .192 .557 .312 .543

X9 .649 -.158 -.223 -.106

X11 .561 .201 -.433 -.053

a. 4 components extracted.

Rotated Component Matrixa Component

1 2 3 4

X1 .737 .170 -.159 -.003

X2 -.053 .826 .184 .067

X3 .290 .757 -.114 -.052

X4 .149 -.156 .749 .003

X5 .432 -.014 .145 .640

X6 -.198 .222 .558 .377

X7 .179 .337 .527 -.014

X8 -.084 -.005 -.013 .856

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X11 .623 -.113 .357 .132

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 6 iterations.

Component Transformation Matrix

Compo

nent 1 2 3 4

1 .612 .487 .548 .296

2 -.157 -.639 .398 .639

3 -.744 .577 .158 .297

4 .216 .144 -.718 .645

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Anti-image Matrices

X1 X2 X3 X4 X5 X6 X7 X8 X9 X11

Anti-image Covariance X1 .852 .011 -.175 -.014 -.060 .082 -.025 .028 -.049 -.171

X2 .011 .728 -.245 .073 .136 -.119 -.220 -.132 -.096 -.003

X3 -.175 -.245 .755 .046 -.007 -.010 .012 .047 -.154 .060

X4 -.014 .073 .046 .842 .087 -.155 -.117 -.123 -.166 -.113

X5 -.060 .136 -.007 .087 .745 -.138 -.165 -.236 -.185 -.092

X6 .082 -.119 -.010 -.155 -.138 .871 -.032 -.054 .008 -.013

X7 -.025 -.220 .012 -.117 -.165 -.032 .801 .120 .012 -.091

X8 .028 -.132 .047 -.123 -.236 -.054 .120 .852 .135 -.038

X9 -.049 -.096 -.154 -.166 -.185 .008 .012 .135 .732 -.130

X11 -.171 -.003 .060 -.113 -.092 -.013 -.091 -.038 -.130 .809

Anti-image Correlation X1 .677a .014 -.218 -.017 -.075 .095 -.030 .033 -.062 -.206

X2 .014 .529a -.331 .093 .184 -.149 -.289 -.168 -.132 -.003

X3 -.218 -.331 .610a .058 -.010 -.013 .016 .058 -.206 .077

X4 -.017 .093 .058 .575a .109 -.181 -.142 -.145 -.212 -.136

X5 -.075 .184 -.010 .109 .540a -.172 -.214 -.296 -.251 -.118

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X7 -.030 -.289 .016 -.142 -.214 -.038 .629a .145 .016 -.113

X8 .033 -.168 .058 -.145 -.296 -.062 .145 .696a .171 -.046

X9 -.062 -.132 -.206 -.212 -.251 .010 .016 .171 .660a -.169

X11 -.206 -.003 .077 -.136 -.118 -.015 -.113 -.046 -.169 .729a

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94

5

4

5

4

5

1

5

95

5

4

3

4

3

5

96

5

4

3

2

3

4

5

3

5

97

5

3

4

2

5

3

5

1

5

98

3

2

5

4

3

2

5

Correlation Matrixa

x1 x2 x3 x4 x5 x6 x7 x8 x9 x11

Sig. (1-tailed) x1 .180 .005 .253 .076 .374 .114 .407 .016 .005

x2 .180 .000 .403 .495 .038 .001 .193 .018 .236

x3 .005 .000 .477 .281 .275 .101 .312 .002 .326

x4 .253 .403 .477 .187 .017 .038 .100 .009 .011

x5 .076 .495 .281 .187 .017 .011 .005 .003 .006

x6 .374 .038 .275 .017 .017 .064 .067 .138 .156

x7 .114 .001 .101 .038 .011 .064 .415 .032 .016

x8 .407 .193 .312 .100 .005 .067 .415 .294 .206

x9 .016 .018 .002 .009 .003 .138 .032 .294 .002

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Correlation Matrixa

x1 x2 x3 x4 x5 x6 x7 x8 x9 x11

Sig. (1-tailed) x1 .180 .005 .253 .076 .374 .114 .407 .016 .005

x2 .180 .000 .403 .495 .038 .001 .193 .018 .236

x3 .005 .000 .477 .281 .275 .101 .312 .002 .326

x4 .253 .403 .477 .187 .017 .038 .100 .009 .011

x5 .076 .495 .281 .187 .017 .011 .005 .003 .006

x6 .374 .038 .275 .017 .017 .064 .067 .138 .156

x7 .114 .001 .101 .038 .011 .064 .415 .032 .016

x8 .407 .193 .312 .100 .005 .067 .415 .294 .206

x9 .016 .018 .002 .009 .003 .138 .032 .294 .002

x11 .005 .236 .326 .011 .006 .156 .016 .206 .002

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PERHITUNGAN KMO DAN MSA

Untuk menghitung KMO dan MSA maka diperlukan matriks korelasi sederhana dan matriks korelasi parsial yang semua entrinya telah

dikuadratkan. Berikut ini akan disajikan matriks korelasi sederhana dan matriks korelasi parsial yang semua entrinya telah dikuadratkan.

MATRIKS KORELASI SEDERHANA

�

r

ij

�

X1 X2 X3 X4 X5 X6 X7 X8 X9 X11

X1

1.000

0.180

0.005

0.253

0.076

0.374

0.114

0.407

0.016

0.005

X2

0.180

1.000

0.000

0.403

0.495

0.038

0.001

0.193

0.018

0.236

X3

0.005

0.000

1.000

0.477

0.281

0.275

0.101

0.312

0.002

0.326

X4

0.253

0.403

0.477

1.000

0.187

0.017

0.038

0.100

0.009

0.011

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MATRIKS KORELASI PARSIAL

X1 X2 X3 X4 X5 X6 X7 X8 X9 X11

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Kuadrat Matriks Korelasi Sederhana

X1 X2 X3 X4 X5 X6 X7 X8 X9 X11 Jumlah

X1 0.0324 0.000025 0.064009 0.005776 0.139876 0.01300 0.165649 0.000256 0.000025 1.421016 X2 0.03240 0.000000 0.162409 0.245025 0.001444 0.00000 0.037249 0.000324 0.055696 1.534547 X3 0.00003 0.000000 0.227529 0.078961 0.075625 0.01020 0.097344 0.000004 0.106276 1.595969

X4 0.06401 0.162409 0.227529 0.034969 0.000289 0.00144 0.01 0.000081 0.000121 1.500848

Σ = (

�

��2) = X5 0.00578 0.245025 0.078961 0.034969 0.000289 0.00012 0.000025 0.000009 0.000036 1.365214

X6 0.13988 0.001444 0.075625 0.000289 0.000289 0.00410 0.004489 0.019044 0.024336 1.269496 X7 0.01300 0.000001 0.010201 0.001444 0.000121 0.004096 0.172225 0.001024 0.000256 1.202368 X8 0.16565 0.037249 0.097344 0.01 0.000025 0.004489 0.17223 0.086436 0.042436 1.615859

X9 0.00026 0.000324 0.000004 0.000081 0.000009 0.019044 0.00102 0.086436 0.000004 1.107182

X11 0.00003 0.055696 0.106276 0.000121 0.000036 0.024336 0.00026 0.042436 0.000004 1.229195

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Kuadrat Matriks Korelasi Parsial

X1 X2 X3 X4 X5 X6 X7 X8 X9 X11 Jumlah

X1 0.000324 0.073984 0.0004 0.009025 0.0121 0.00137 0.001521 0.006241 0.061504 0.16647 X2 0.000324 0.198916 0.014161 0.0625 0.034969 0.14288 0.045796 0.032761 0.000016 0.53232 X3 0.073984 0.198916 0.005184 0.000169 0.000256 0.00040 0.005329 0.077284 0.009801 0.37132 X4 0.0004 0.014161 0.005184 0.019044 0.044521 0.02993 0.029241 0.0729 0.027225 0.24261

D = (

�

_��2) = X5 _0.009025 _0.0625 _0.000169 _0.019044 _0.045369 _0.07618 _0.138384 _0.114921 _0.023104 0.48870 X6 0.0121 0.034969 0.000256 0.044521 0.045369 0.00203 0.005329 0.000169 0.000324 0.14507 X7 0.001369 0.142884 0.0004 0.029929 0.076176 0.002025 0.030625 0.000441 0.019881 0.30373 X8 0.001521 0.045796 0.005329 0.029241 0.138384 0.005329 0.00031 0.046656 0.003136 0.27570 X9 0.006241 0.032761 0.077284 0.0729 0.114921 0.000169 0.00044 0.046656 0.0484 0.39977 X11 0.061504 0.000016 0.009801 0.027225 0.023104 0.000324 0.01988 0.003136 0.0484 0.19339

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