Penentuan Faktor Dominan yang Mempengaruhi Pertumbuhan Ekonomi Sumatera Utara dengan Analisis Faktor

  Lampiran 1. Produk Domestik Regional Bruto (PDRB) Provinsi Sumatera Utara Menurut Kabupaten/Kota Atas Dasar Harga Konstan (ADHK) Tahun 2013 (Juta Rupiah) No. Kabupaten/Kota Pertanian Pertambangan dan Penggalian Industri Pengolahan

Listrik,

Gas, dan

  12 Deli Serdang 2731163.64 231332.64 6442205.22 40856.93 545943.44 3655447.10 1577841.64 655161.71 2529844.61

  22 Labuhan Batu Utara 1238607.76 47682.54 1381402.53 8825.36 96817.96 644078.62 110347.09 48537.92 224072.87

  21 Palas 526439.49 6729.38 39304.61 1144.95 97068.36 96873.43 26639.43 5831.80 100559.71

  20 Paluta 579280.14 4610.56 37902.38 952.65 92343.41 98935.33 11467.18 5191.44 114515.18

  19 Batu Bara 1315408.07 10305.48 4360267.06 56730.77 164002.76 2001227.59 196213.16 129387.56 149264.76

  18 Serdang Bedagai 2086274.91 79829.81 991861.34 36046.16 576633.29 862787.52 40440.18 194975.42 548368.28

  17 Samosir 851942.15 455.51 16238.40 1978.98 5167.55 112666.26 14521.51 27135.80 236452.87

  16 Pakpak Bharat 121257.03 114.47 481.49 467.03 22499.90 22645.85 2620.82 3163.27 22875.98

  15 Humbang Hasundutan 621517.00 2811.99 3300.05 4511.39 49070.91 226249.84 57501.24 40096.18 193296.30

  14 Nias Selatan 581195.31 18838.60 23884.70 3058.55 170568.67 334696.75 88445.71 66479.25 143861.90

  13 Langkat 4608280.78 452977.18 918648.77 29984.54 252325.74 1371969.16 194856.78 180625.08 517676.21

  11 Karo 2275338.99 15037.25 28331.69 11365.98 137721.42 605915.29 347903.94 69221.21 505878.48

  

Air

Bersih

Bangunan Perdagangan, Hotel, dan Restoran Pengangkutan dan Komunikasi Keuangan, Persewaan, dan Jasa Perusahaan Jasa-Jasa

  10 Dairi 1548461.19 1771.88 7712.33 7084.80 81586.62 376140.83 102151.47 28700.81 246842.28

  9 Simalungun 3699274.24 24649.75 952691.18 35569.31 123076.06 552217.62 171284.05 158879.20 814259.35

  8 Asahan 2160309.90 17023.78 2060305.06 82610.24 174710.27 1056042.90 243402.62 184419.71 366426.42

  7 Labuhan Batu 711872.36 63327.55 1688092.01 16180.36 123230.96 678768.48 166999.84 64112.42 366421.59

  6 Toba Samosir 719812.43 7096.05 707639.88 22666.12 100221.06 198296.94 74574.35 61143.43 166033.13

  5 Tapanuli Utara 963456.63 1579.06 38493.57 14372.10 141802.85 260000.29 72376.65 63173.66 359160.36

  4 Tapanuli Tengah 623843.98 10468.09 154863.13 10730.78 80279.08 185289.26 38500.42 53460.98 289938.58

  3 Tapanuli Selatan 632571.54 3706.94 773436.89 1399.86 95821.55 323453.31 40542.64 10577.12 217493.94

  2 Mandailing Natal 1084272.51 38746.69 80384.63 5859.22 293456.48 415825.57 97003.77 49172.27 383258.41

  1 Nias 306733.52 22666.52 6392.31 785.02 45742.37 37302.26 17564.16 23588.44 156098.09

  23 Labuhan Batu Selatan 716459.51 23541.73 1808479.30 5821.09 70346.05 521653.17 66172.07 35324.82 145778.76 Universitas Sumatera Utara

  24 Nias Utara 392458.13 20951.73 4714.35 800.37 25717.83 70439.45 15061.86 20873.75 37236.69 Lanjutan Lampiran 1.

  No. Kabupaten/Kota Pertanian Pertambangan dan Penggalian Industri Pengolahan Listrik, Gas, dan Air Bersih Bangunan Perdagangan, Hotel, dan Restoran Pengangkutan dan Komunikasi Keuangan, Persewaan, dan Jasa Perusahaan Jasa-Jasa

  25 Nias Barat 207580.93 10768.79 4243.19 352.27 9502.53 20439.46 8388.29 11053.07 28868.82

  26 Sibolga 205975.86 56.38 69298.51 5438.63 43097.82 174042.42 131697.89 76015.67 161205.91

  27 Tanjung Balai 337033.67 51476.21 265976.69 9611.70 131234.54 371415.30 131195.42 93413.28 215673.13

  28 Pematang Siantar 66941.92 409.80 259881.02 25554.42 185154.66 831156.64 392776.95 324464.12 316764.02

  29 Tebing Tinggi 20152.77 1149.15 194322.10 5749.80 120958.24 374276.81 254968.51 136077.17 311345.53

  30 Medan 834423.85 534.75 5332919.42 555266.22 4952401.41 12361898.76 7856490.90 6649690.62 4760330.46

  31 Binjai 164467.20 139758.12 494543.33 37337.27 251425.73 387382.23 131059.97 382258.05 437855.45

  32 Padang Sidempuan 158858.12 3047.15 110551.09 5844.76 67933.55 262748.43 107710.57 176439.32 224932.09

  33 Gunung Sitoli 92194.64 3019.07 24475.84 4736.92 154858.37 431285.42 188345.77 69526.52 76444.58 Universitas Sumatera Utara

  Lampiran 2. Produk Domestik Regional Bruto (PDRB) Provinsi Sumatera Utara Menurut Kabupaten/Kota Atas Dasar Harga Berlaku (ADHB) Tahun 2013 (Juta Rupiah) No. Kabupaten/Kota Pertanian Pertambangan dan Penggalian Industri Pengolahan Listrik, Gas, dan Air Bersih Bangunan Perdagangan, Hotel, dan Restoran Pengangkutan dan Komunikasi Keuangan, Persewaan, dan Jasa Perusahaan Jasa-Jasa

  13 Langkat 12582573.89 2577366.77 3248965.46 133426.87 879479.44 2701573.14 721305.69 524206.57 1820616.25

  23 Labuhan Batu Selatan 2425096.73 71243.84 4482174.10 16221.71 160399.78 1378543.72 177901.39 92397.63 385516.62

  22 Labuhan Batu Utara 3742163.87 136498.00 3525089.20 26977.32 233011.10 1784107.20 313374.99 118036.42 622339.58

  21 Palas 1519983.85 31263.37 87541.97 3326.92 169601.37 213950.62 71739.99 13687.03 222743.52

  20 Paluta 1749429.78 17484.12 75538.68 2736.62 145660.51 226027.68 28218.71 11782.92 231105.34

  19 Batu Bara 3473066.54 25334.83 12023518.52 154587.38 392167.61 4943400.43 665706.73 277636.66 463487.22

  18 Serdang Bedagai 5330373.44 139139.80 2818393.14 99622.97 1604955.57 2150703.35 114292.87 443794.15 1340517.74

  17 Samosir 1298429.06 1463.83 39300.53 4102.82 9801.49 221456.68 35557.20 56580.52 574068.56

  16 Pakpak Bharat 308299.69 220.38 951.45 1568.02 46666.56 55832.52 11244.09 7872.07 46804.64

  15 Humbang Hasundutan 2061777.28 8117.35 12987.07 15495.56 154847.77 546221.20 150068.09 139199.12 523516.16

  14 Nias Selatan 1159915.89 76293.82 91187.42 10943.95 345358.33 493662.91 236993.04 194005.84 339010.66

  12 Deli Serdang 6673888.04 531263.77 27527020.37 118279.58 1476225.91 12299745.79 3616863.16 1442563.05 6176901.81

  1 Nias 924102.51 60944.85 16462.67 2962.97 116595.98 68539.27 49683.36 66811.15 332731.09

  11 Karo 5781455.94 32055.31 67768.39 26339.68 335218.97 1102654.21 666202.96 152379.89 1386447.13

  10 Dairi 3125337.53 4769.31 19064.20 17538.53 215412.00 813556.84 307885.99 102389.11 739462.07

  9 Simalungun 7885206.06 65807.44 2444020.97 125456.45 294730.19 1312203.37 488852.31 359521.14 1718727.44

  8 Asahan 6340013.42 38402.29 5233611.43 245221.27 482164.38 2832538.59 764143.21 473496.58 1116032.61

  7 Labuhan Batu 2154581.66 178343.33 4771117.51 39595.13 262040.90 1802915.13 458528.10 153809.96 1073929.41

  6 Toba Samosir 1128935 22269 2196922 50380 388736 514416 147873 138852 422603

  5 Tapanuli Utara 2587168.99 7549.70 83058.98 46600.60 373573.54 814160.30 213788.90 183614.68 811587.82

  4 Tapanuli Tengah 1283302.62 20604.03 469681.07 30375.71 195538.97 403618.03 82510.04 116150.10 702500.73

  3 Tapanuli Selatan 2114841.87 15409.66 1106633.54 3885.85 150833.33 561520.50 119087.85 23147.29 390573.17

  2 Mandailing Natal 2546697.65 69061.38 196319.52 22203.19 502538.12 1025824.54 310599.73 111752.88 788136.48

  24 Nias Utara 1129731.82 59364.81 12207.69 4004.73 67955.80 121287.74 41067.60 94717.55 88149.37 Universitas Sumatera Utara

  Lanjutan Lampiran 2. No. Kabupaten/Kota Pertanian Pertambangan dan Penggalian Industri Pengolahan Listrik, Gas, dan Air Bersih Bangunan Perdagangan, Hotel, dan Restoran Pengangkutan dan Komunikasi Keuangan, Persewaan, dan Jasa Perusahaan Jasa-Jasa

  25 Nias Barat 630298.17 29786.36 9450.74 2079.42 23139.26 34407.06 22104.49 36336.78 56970.91

  26 Sibolga 474018.16 103.10 158689.42 20018.13 118892.42 504160.83 355410.16 191431.61 303122.20

  27 Tanjung Balai 765258.56 122533.98 756134.36 33169.09 473398.07 898394.81 278161.99 229592.25 468714.12

  28 Pematang Siantar 124962.05 1013.48 1035929.46 67498.16 241912.69 1922207.63 464009.29 735754.10 688084.06

  29 Tebing Tinggi 46543.17 2421.16 676170.13 14998.94 383994.65 768102.65 449187.19 436510.87 676057.18

  30 Medan 2719377.20 2916.32 16046201.20 1811079.87 13154699.59 30143108.24 24325828.32 18477629.04 13034642.44

  31 Binjai 359813.83 544565.92 1608364.41 151409.08 648332.63 1181868.56 377731.08 1543732.15 1013139.53

  32 Padang Sidempuan 448778.46 9865.10 337167.24 15996.98 157405.83 648064.35 341430.97 363922.49 543741.45

  33 Gunung Sitoli 351747.89 8353.20 95477.17 13231.36 403209.22 934974.78 511363.99 391998.40 216952.26 Universitas Sumatera Utara

  Lampiran 3. Hasil Print Out SPSSAnalisis Faktor PDRB

1. Uji Kecukupan Data dan Korelasi Antar Variabel

a. PDRB ADHB

  

KMO and Bartlett's Test

  Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .693 Bartlett's Test of Sphericity Approx. Chi-Square 720.427 df

  36 Sig. .000

b. PDRB ADHK

  

KMO and Bartlett's Test

  Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .681 Bartlett's Test of Sphericity Approx. Chi-Square 701.252 df

  36 Sig. .000

  Lanjutan Lampiran 3.

c. Korelasi Antar Variabel PDRB ADHB Correlation Matrix a

  1 X

  5 0.302 0.479 0.001 0.000 0.000 0.000 0.000 0.000

  1 0.000 0.016 0.252 0.302 0.133 0.344 0.426 0.070

  X

  2

  0.000 0.185 0.473 0.479 0.374 0.461 0.459 0.280

  X

  3

  0.016 0.185 0.002 0.001 0.000 0.001 0.003 0.000

  X

  4 0.252 0.473 0.002 0.000 0.000 0.000 0.000 0.000

  X

  X

  0.262 0.105 0.721 0.917 0.934 0.981 0.947 0.923 1.000 Sig. (1-tailed)

  6 0.133 0.374 0.000 0.000 0.000 0.000 0.000 0.000

  X

  7

  0.344 0.461 0.001 0.000 0.000 0.000 0.000 0.000

  X

  8

  0.426 0.459 0.003 0.000 0.000 0.000 0.000 0.000

  X

  9 0.070 0.280 0.000 0.000 0.000 0.000 0.000 0.000

  a. Determinant = 7,80E-012

  Universitas Sumatera Utara

  X

  9

  2 X

  X

  3 X

  4 X

  5 X

  6 X

  7 X

  8 X

  9 Correlation

  X

  1 1.000 0.683 0.374 0.121 0.094 0.200 0.073 0.034 0.262

  X

  2 0.683 1.000 0.161 0.012 0.010 0.058 -0.018 -0.019 0.105

  3 0.374 0.161 1.000 0.497 0.502 0.743 0.538 0.477 0.721

  X

  X

  4

  0.121 0.012 0.497 1.000 0.986 0.939 0.984 0.989 0.917

  X

  5 0.094 0.010 0.502 0.986 1.000 0.947 0.991 0.993 0.934

  X

  6 0.200 0.058 0.743 0.939 0.947 1.000 0.962 0.939 0.981

  X

  7 0.073 -0.018 0.538 0.984 0.991 0.962 1.000 0.994 0.947

  X

  8 0.034 -0.019 0.477 0.989 0.993 0.939 0.994 1.000 0.923

  X

  Lanjutan Lampiran 3.

d. Korelasi Antar Variabel PDRB ADHK Correlation Matrix a

  1 X

  5 0.407 0.498 0.000 0.000 0.000 0.000 0.000 0.000

  1 0.000 0.026 0.339 0.407 0.205 0.405 0.478 0.092

  X

  2

  0.000 0.058 0.462 0.498 0.291 0.494 0.453 0.167

  X

  3

  0.026 0.058 0.000 0.000 0.000 0.000 0.000 0.000

  X

  4 0.339 0.462 0.000 0.000 0.000 0.000 0.000 0.000

  X

  X

  0.238 0.173 0.748 0.896 0.915 0.957 0.945 0.914 1.000 Sig. (1-tailed)

  6 0.205 0.291 0.000 0.000 0.000 0.000 0.000 0.000

  X

  7

  0.405 0.494 0.000 0.000 0.000 0.000 0.000 0.000

  X

  8

  0.478 0.453 0.000 0.000 0.000 0.000 0.000 0.000

  X

  9 0.092 0.167 0.000 0.000 0.000 0.000 0.000 0.000

  a. Determinant = 1,54E-011

  Universitas Sumatera Utara

  X

  9

  2 X

  X

  3 X

  4 X

  5 X

  6 X

  7 X

  8 X

  9 Correlation

  X

  1 1.000 0.666 0.341 0.075 0.043 0.148 0.044 0.010 0.238

  X

  2 0.666 1.000 0.280 -0.017 -0.001 0.099 0.003 -0.021 0.173

  3 0.341 0.280 1.000 0.601 0.571 0.736 0.627 0.568 0.748

  X

  X

  4

  0.075 -0.017 0.601 1.000 0.985 0.968 0.976 0.987 0.896

  X

  5 0.043 -0.001 0.571 0.985 1.000 0.970 0.987 0.994 0.915

  X

  6 0.148 0.099 0.736 0.968 0.970 1.000 0.984 0.970 0.957

  X

  7 0.044 0.003 0.627 0.976 0.987 0.984 1.000 0.993 0.945

  X

  8 0.010 -0.021 0.568 0.987 0.994 0.970 0.993 1.000 0.914

  X

  Lanjutan Lampiran 3. Tabel Eigenvalue, Variance, Cumulative, PDRB ADHB

  Faktor (Component)

  % 1 6.310 70.109 70.109 6.310 70.109 70.109 2 1.798 19.975 90.084 1.798 19.975 90.084 3 0.461 5.128 95.211 4 0.334 3.710 98.921 5 0.076 0.845 99.767 6 0.010 0.113 99.879 7 0.008 0.094 99.973 8 0.002 0.018 99.990 9 0.001 0.010 100.000

  Variance Cumulative

  Total % of

  Cumulative %

  % of Variance

  Loadings Total

  Initial Eigenvalues Extraction Sums of Squared

  Tabel Eigenvalue, Variance, Cumulative, PDRB ADHK

  Faktor (Component)

  Extraction Method: Principal Component Analysis.

  % 1 6.233 69.257 69.257 6.233 69.257 69.257 2 1.775 19.722 88.979 1.775 19.722 88.979 3 0.656 7.294 96.273 4 0.279 3.100 99.373 5 0.038 0.422 99.795 6 0.009 0.102 99.897 7 0.007 0.074 99.971 8 0.002 0.018 99.989 9 0.001 0.011 100.000

  Variance Cumulative

  Total % of

  Cumulative %

  % of Variance

  Loadings Total

  Initial Eigenvalues Extraction Sums of Squared

  Extraction Method: Principal Component Analysis.

  Lanjutan Lampiran 3. Tabel Faktor Loading Variabel PDRB ADHB Sebelum dirotasi

  5 0.983 -0.003

  1 0.089 0.920

  X

  2 -0.036 0.879

  X

  3

  0.621 0.409

  X

  4 0.976 0.011

  X

  X

  1

  6 0.983 0.137

  X

  7

  0.992 -0.019

  X

  8 0.982 -0.054

  X

  9 0.966 0.192 Extraction Method: Principal Component Analysis.

  Rotation Method: Varimax with Kaiser Normalization.

  2 X

  Variabel Faktor (Component)

  Variabel Faktor (Component)

  X

  1

  2 X

  1 0.204 0.902

  X

  2 0.075 0.877

  X

  3 0.668 0.327

  X

  4 0.970 -0.113

  5

  Tabel Faktor Loading Variabel PDRB ADHB Setelah dirotasi

  0.975 -0.127

  X

  6 0.992 0.011

  X

  7 0.982 -0.145

  X

  8 0.968 -0.178

  X

  9 0.982 0.068 Extraction Method: Principal Component Analysis.

  a. 2 components extracted.

  a. Rotation converged in 3 iterations. a. Rotation converged in 3 iterations.

  Rotation Method: Varimax with Kaiser Normalization.

  X

  1

  2 X

  1 0.052 0.902

  X

  2

  X

  3 0.678 0.431

  X

  4 0.981 -0.013

  5 0.985 -0.027

  Tabel Faktor Loading Variabel PDRB ADHK Setelah dirotasi

  X

  6 0.990 0.117

  X

  7

  0.995 -0.010

  X

  8 0.988 -0.054

  X

  9 0.948 0.216 Extraction Method: Principal Component Analysis.

  Variabel Faktor (Component)

  a. 2 components extracted.

  Lanjutan Lampiran 3. Tabel Faktor Loading Variabel PDRB ADHK Sebelum dirotasi

  4 0.972 -0.133

  Variabel Faktor (Component)

  1

  2 X

  1 0.162 0.889

  X

  2 0.102 0.890

  X

  3 0.726 0.344

  X

  X

  9 0.968 0.098 Extraction Method: Principal Component Analysis.

  5

  0.974 -0.148

  X

  6 0.997 -0.006

  X

  7 0.986 -0.132

  X

  8 0.974 -0.175

  X

• 0.008 0.896

  Universitas Sumatera Utara

  X

  0.976 0.954

  X

  8 0.038 -0.083 0.588 0.958 0.966 0.958 0.976 0.968 a

  0.938

  X

  9

  0.262 0.134 0.678 0.945 0.949 0.975 0.954 0.938 0.969

  a

  Residual

  b

  X

  1 -0.123 -0.058 0.024 0.009 -0.013 0.002 -0.004 0.000

  X

  2

  3 -0.058 -0.176 -0.114 -0.107 0.077 -0.071 -0.111 0.043

  X

  0.002 0.035 -0.071 0.015 0.015 -0.011 0.018 -0.007

  b. Residuals are computed between observed and reproduced correlations. There are 9 (25,0%) nonredundant residuals with absolute values greater than 0.05.

  a. Reproduced communalities

  9 0.000 -0.028 0.043 -0.028 -0.014 0.006 -0.007 -0.015 Extraction Method: Principal Component Analysis.

  X

  8

  X

  7

  X

  X

  6 -0.013 -0.027 0.077 -0.021 -0.018 -0.011 -0.019 0.006

  X

  5 0.009 0.048 -0.107 0.026 -0.018 0.015 0.026 -0.014

  X

  4 0.024 0.038 -0.114 0.026 -0.021 0.015 0.031 -0.028

  7 0.070 -0.053 0.608 0.968 0.976 0.972 0.985 a

  0.972 0.958 0.975

  Lanjutan Lampiran 3. Tabel Residual antara Observed Correlation dan Reproduced Correlationi Variabel PDRB ADHB

Reproduced Correlations

  X

  X

  1 X

  2 X

  3 X

  4 X

  5 X

  6 X

  7 X

  8 X

  9 Reproduced

  Correlation

  X

  1 0.855 a

  0.806 0.432 0.096 0.084 0.213 0.070 0.038 0.262

  2 0.806 0.774 a

  6 0.213 0.085 0.666 0.961 0.966 0.984 a

  0.096 -0.026 0.610 0.953

  X

  0.966 0.976 0.966 0.949

  5 0.084 -0.038 0.609 0.960 0.967 a

  X

  0.960 0.961 0.968 0.958 0.945

  a

  4

  0.337 -0.026 -0.038 0.085 -0.053 -0.083 0.134

  X

  0.610 0.609 0.666 0.608 0.588 0.678

  a

  0.432 0.337 0.553

  3

  X

• 0.123 -0.176 0.038 0.048 -0.027 0.035 0.064 -0.028
• 0.004 0.064 -0.111 0.031 0.026 -0.019 0.018 -0.015

  Universitas Sumatera Utara

  X

  8 0.002 -0.057 0.646 0.970 0.975 0.972 0.983 0.979 a

  0.925

  X

  9

  0.244 0.186 0.736 0.928 0.928 0.964 0.941 0.925 0.946

  a

  Residual

  b

  X

  1 -0.142 -0.082 0.036 0.016 -0.008 0.001 0.008 -0.006

  X

  2

  X

  3

  4 0.036 0.002 -0.058 0.018 -0.002 0.000 0.017 -0.032

  0.983 0.941

  X

  5 0.016 0.031 -0.085 0.018 -0.002 0.007 0.020 -0.013

  X

  6 -0.008 0.003 0.014 -0.002 -0.002 0.000 -0.002 -0.008

  X

  7

  0.001 0.020 -0.043 0.000 0.007 0.000 0.009 0.004

  X

  8

  0.008 0.035 -0.079 0.017 0.020 -0.002 0.009 -0.011

  X

  9 -0.006 -0.013 0.012 -0.032 -0.013 -0.008 0.004 -0.011 Extraction Method: Principal Component Analysis.

  a. Reproduced communalities

  b. Residuals are computed between observed and reproduced correlations. There are 6 (16,0%) nonredundant residuals with absolute values greater than 0.05.

  X

  7 0.043 -0.017 0.670 0.976 0.980 0.984 0.990 a

  Lanjutan Lampiran 3. Tabel Residual antara Observed Correlation dan Reproduced Correlationi Variabel PDRB ADHK

Reproduced Correlations

  X

  X

  1 X

  2 X

  3 X

  4 X

  5 X

  6 X

  7 X

  8 X

  9 Reproduced

  Correlation

  X

  1 0.816 a

  0.807 0.424 0.039 0.027 0.157 0.043 0.002 0.244

  2 0.807 0.803 a

  X

  0.967 0.970 0.976 0.970 0.928

  0.984 0.972 0.964

  6 0.157 0.097 0.722 0.970 0.973 0.995 a

  X

  0.973 0.980 0.975 0.928

  5 0.027 -0.032 0.656 0.967 0.971 a

  X

  a

  0.381 -0.019 -0.032 0.097 -0.017 -0.057 0.186

  0.039 -0.019 0.660 0.963

  4

  X

  0.660 0.656 0.722 0.670 0.646 0.736

  3 0.424 0.381 0.645 a

  X

• 0.142 -0.101 0.002 0.031 0.003 0.020 0.035 -0.013
• 0.082 -0.101 -0.058 -0.085 0.014 -0.043 -0.079 0.012
• 0.005 0.006 0.005 0.000 0.009 -0.002 0.001 -0.001 0.000

  Universitas Sumatera Utara

  a

  Anti-image Correlation

  X

  1 0.220 a

  X

  2 -0.767 0.237 a

  X

  3

  0.340 -0.138 0.594

  a

  X

  4

  0.034 -0.002 0.229 -0.827 0.690

  X

  X

  5 -0.157 0.112 0.341 0.034 0.927 a

  X

  6 -0.114 -0.026 -0.919 -0.002 -0.361 0.793 a

  X

  7 -0.174 0.349 0.437 0.229 0.150 -0.591 0.835 a

  X

  8 0.825 -0.639 0.310 -0.827 -0.285 -0.050 -0.480 0.663 a

  X

  9

  a

  a. Measures of Sampling Adequacy(MSA)

  9 -0.026 0.026 -0.005 0.006 0.000 -8.830E-5 0.000 -0.003 0.015

  8 0.009 -0.013 0.002 -0.002 -0.001 -9.151E-5 0.000 0.001 -0.003

  Lanjutan Lampiran 3. Tabel Anti-image Matrices Untuk Data PDRB ADHB

  X

  X

  1 X

  2 X

  3 X

  4 X

  5 X

  6 X

  7 X

  8 X

  9 Anti-image

  Covariance

  1 0.091 -0.127 0.015 -0.018 -0.005 -0.002 -0.003 0.009 -0.026

  X

  X

  2 -0.127 0.300 -0.011 0.023 0.006 0.000 0.010 -0.013 0.026

  X

  3 0.015 -0.011 0.020 -0.003 0.005 -0.006 0.003 0.002 -0.005

  X

  4 -0.018 0.023 -0.003 0.005 0.000 -6.868E-6 0.001 -0.002 0.006

  X

  5

  X

  6 -0.002 0.000 -0.006 -6.868E-6 -0.002 0.002 -0.002 -9.151E-5 -8.830E-5

  X

  7 -0.003 0.010 0.003 0.001 0.001 -0.002 0.003 0.000 0.000

• 0.767 0.340 -0.832 -0.157 -0.114 -0.174 0.825 -0.699
• 0.138 0.583 0.112 -0.026 0.349 -0.639 0.381
• 0.251 0.341 -0.919 0.437 0.310 -0.308
• 0.832 0.583 -0.251 0.680
• 0.361 0.150 -0.285 -0.014
• 0.591 -0.050 -0.014
• 0.480 -0.017
• 0.556
• 0.699 0.381 -0.308 0.690 -0.014 -0.014 -0.017 -0.556 0.769

  Universitas Sumatera Utara

  Universitas Sumatera Utara

  4

  Anti-image Correlation

  X

  1 0.257 a

  X

  2 -0.367 0.338 a

  0.179 0.461 0.242 -0.407 0.518 -0.453 -0.163

  X

  3

  0.599 0.179 0.532

  a

  X

  a

  X

  X

  5 0.133 0.242 0.482 -0.037 0.815 a

  X

  6 -0.442 -0.407 -0.884 0.042 -0.540 0.694 a

  X

  7 0.218 0.518 0.523 0.221 0.476 -0.745 0.710 a

  X

  8 0.507 -0.453 0.254 -0.754 -0.414 0.058 -0.543 0.762 a

  X

  9

  a

  a. Measures of Sampling Adequacy(MSA)

  9 -0.037 -0.013 -0.018 0.005 -0.004 0.004 -0.004 0.000 0.020

  8 0.008 -0.010 0.002 -0.002 -0.001 0.000 0.000 0.002 0.000

  Lanjutan Lampiran 3. Tabel Anti-image Matrices Untuk Data PDRB ADHK

  1 0.168 -0.085 0.046 -0.023 0.005 -0.008 0.004 0.008 -0.037

  X

  1 X

  2 X

  3 X

  4 X

  5 X

  6 X

  7 X

  8 X

  9 Anti-image

  Covariance

  X

  X

  X

  2 -0.085 0.315 0.019 0.022 0.011 -0.011 0.012 -0.010 -0.013

  X

  3 0.046 0.019 0.035 -0.006 0.007 -0.008 0.004 0.002 -0.018

  X

  4 -0.023 0.022 -0.006 0.007 0.000 0.000 0.001 -0.002 0.005

  X

  5

  0.005 0.011 0.007 0.000 0.007 -0.002 0.002 -0.001 -0.004

  X

  6 -0.008 -0.011 -0.008 0.000 -0.002 0.002 -0.001 0.000 0.004

  X

  7 0.004 0.012 0.004 0.001 0.002 -0.001 0.002 0.000 -0.004

• 0.367 0.599 -0.668 0.133 -0.442 0.218 0.507 -0.646
• 0.375 0.482 -0.884 0.523 0.254 -0.702
• 0.668 0.461 -0.375 0.758
• 0.037 0.042 0.221 -0.754 0.421
• 0.540 0.476 -0.414 -0.376
• 0.745 0.058 0.575
• 0.543 -0.626
• 0.088
• 0.646 -0.163 -0.702 0.421 -0.376 0.575 -0.626 -0.088 0.713

  Lanjutan Lampiran 3. Tabel Component Transformation Matrix Data PDRB ADHB

  Component

  1

  2 1 0.992 0.126 2 -0.126 0.992 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

  Tabel Component Transformation Matrix Data PDRB ADHB

  Component

  1

  2 1 0.992 0.123 2 -0.123 0.992 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

1. PDRB ADHB

  6 X

  2

  X

  1

  9 X

  8 X

  7 X

  5 X

  3

  

4

X

  3 X

  2 X

  1 X

  X

  

Matriks Korelasi Parsial

  X

  0.340 -0.138 -0.251 0.341 -0.919 0.437 0.310 -0.308

  9

  0.349 0.437 0.229 0.150 -0.591 -0.480 -0.017

  Universitas Sumatera Utara

  9

  X

  0.825 -0.639 0.310 -0.827 -0.285 -0.050 -0.480 -0.556

  8

  X

  7

  X

  X

  6

  X

  0.112 0.341 0.034 -0.361 0.150 -0.285 -0.014

  5 -0.157

  4

  0.262 0.105 0.721 0.917 0.934 0.981 0.947 0.923 1,000

  X

  Lampiran 4. Perhitungan Analisis Faktor Dengan Menggunakan Matriks

  6 X

  X

  1,000 0.683 0.374 0.121 0.094 0.200 0.073 0.034 0.262

  1

  9 X

  8 X

  7 X

  5 X

  0.683 1,000 0.161 0.012 0.010 0.058 -0.018 -0.019 0.105

  4 X

  3 X

  2 X

  1 X

  X

  

Matriks Korelasi Sederhana

  2

  X

  0.034 -0.019 0.477 0.989 0.993 0.939 0.994 1,000 0.923

  6

  8

  X

  0.073 -0.018 0.538 0.984 0.991 0.962 1,000 0.994 0.947

  7

  X

  0.200 0.058 0.743 0.939 0.947 1,000 0.962 0.939 0.981

  X

  3

  0.094 0.010 0.502 0.986 1,000 0.947 0.991 0.993 0.934

  5

  0.121 0.012 0.497 1,000 0.986 0.939 0.984 0.989 0.917 Σ = X

  4

  X

  0.374 0.161 1,000 0.497 0.502 0.743 0.538 0.477 0.721

• 0.767 0.340 -0.832 -0.157 -0.114 -0.174 0.825 -0.699
• 0.767 -0.138 0.583 0.112 -0.026 0.349 -0.639 0.381
• 0.832 0.583 -0.251 0.034 -0.002 0.229 -0.827 0.690 A = (a ij ) = X
• 0.114 -0.026 -0.919 -0.002 -0.361 -0.591 -0.050 -0.014
• 0.174
• 0.699 0.381 -0.308 0.690 -0.014 -0.014 -0.017 -0.556 Untuk Menghitung nilai Kaiser Meyer Olkin (KMO) maka maka nilai – nilai dari matriks korelasi sederhana dan matriks korelasi parsial harus dikuadratkan terlebih dahulu. Hasil dari kuadrat nilai – nilai tersebut adalah sebagai berikut : Lanjutan Lampiran 4.

  Universitas Sumatera Utara

  X

  5 0.025 0.013 0.116 0.001 0.130 0.023 0.081 0.000

  ij) = X

  2

  D = (a

  4 0.692 0.340 0.063 0.001 0.000 0.052 0.684 0.476 2.308

  X

  3 0.116 0.019 0.063 0.166 0.845 0.191 0.096 0.095 1.591

  2 0.588 0.019 0.340 0.013 0.001 0.122 0.408 0.145 1.636

  X

  X

  1 0.588 0.116 0.692 0.025 0.013 0.030 0.680 0.489 2.633

  X

  9 Jumlah

  8 X

  7 X

  6 X

  0.389

  6 0.013 0.001 0.845 0.000 0.130 0.349 0.003 0.000 1.341

  4 X

  ∑ ∑ r

  = 0.692 Dengan demikian, diperoleh nilai KMO yaitu sebesar 0.692 sehingga dapat disimpulkan bahwa analisis faktor tepat untuk digunakan

  33.514 48.414

  33.514 + 14.900 =

  = 33.514

  ≠i n i n j ≠i n i

  2 ij n j

  2 ij

  ≠i n i

  X

  2 ij n j

  ∑ ∑ r

  14.900 Setelah nilai – nilai dari matriks korelasi sederhana dan matriks korelasi parsial dikuadratkan dan kemudian dilakukan penjumlahan, maka tahap selanjutnya adalah menghitung nilai Kaiser Meyer Olkin (KMO). Proses perhitungannya adalah sebagai berikut : KMO =

  9 0.489 0.145 0.095 0.476 0.000 0.000 0.000 0.309 1.514 Jumlah

  X

  8 0.680 0.408 0.096 0.684 0.081 0.003 0.230 0.309 2.491

  X

  7 0.030 0.122 0.191 0.052 0.023 0.349 0.230 0.000 0.997

  5 X

  3 X

  X

  9 Jumlah

  3 0.140 0.026 0.247 0.252 0.552 0.289 0.228 0.520 2.254

  X

  2 0.466 0.026 0.000 0.000 0.003 0.000 0.000 0.011 0.506

  X

  0.466 0.140 0.015 0.009 0.040 0.005 0.001 0.069 0.745

  1

  X

  8 X

  4

  7 X

  6 X

  5 X

  4 X

  3 X

  2 X

  1 X

  X

  0.015 0.000 0.247 0.972 0.882 0.968 0.978 0.841 4.903 Σ = (r

  2 X

  0.001 0.000 0.228 0.978 0.986 0.882 0.988 0.852 4.915

  1 X

  X

  33.514

  Jumlah

  0.069 0.011 0.520 0.841 0.872 0.962 0.897 0.852 5.024

  9

  X

  8

  2 ij ) =

  X

  7 0.005 0.000 0.289 0.968 0.982 0.925 0.988 0.897 5.054

  X

  6 0.040 0.003 0.552 0.882 0.897 0.925 0.882 0.962 5.143

  X

  0.009 0.000 0.252 0.972 0.897 0.982 0.986 0.872 4.970

  5

  X

• ∑ ∑ a
• 5)

  Lanjutan Lampiran 4.

  6 � |0,0000000000078|

  2

hitung

  > χ

  2 (0.05),(36)

  = 50.998 d. Perhitungan χ

  2

  = − �(33 − 1) −

  (2(9) + 5)

  = −[32 − 3.83](−25,577)

  = 50.998 c. Kriteria pengujian : Tolak H jika

  = −(28,17)(−25,577)

  = 720.504 e. Kesimpulan

  Dari hasil perhitungan diperoleh χ

  2 hitung

  = 720.504 > χ

  2 (0.05),(36)

  = 50.998, sehingga H ditolak. Dengan demikian, matriks korelasi sederhana bukan merupakan matriks identitas.

  χ

  2 (0.05),(36)

  Tahapan selanjutnya adalah melakukan uji Bartlett dengan pendekatan statistik chi square untuk menguji apakah matriks korelasi bukan merupakan suatu matriks identitas. Langkah – langkah pengujiannya adalah sebagai berikut : a.

  χ

  Hipotesis H : Matriks korelasi merupakan matriks identitas H

  1 : Matriks korelasi bukan merupakan matriks identitas

  Statistik uji,

  2

  = − �( − 1) −

  (2

  6 � |R| b.

  2 hitung

  χ

  > χ

  2

  α, p(p − 1)/2 α = 5% = 0.05 p = 9 df = p(p

  − 1)

  2 =

  9(9 − 1)

  2 = 36

2 Det (R) = 0,0000000000078

2. PDRB ADHK

  X

  

Matriks Korelasi Parsial

  X

  1 X

  2 X

  3 X

  4 X

  5 X

  6 X

  7 X

  8 X

  9 X

  1

  X

  2

  3

  9

  0.218 0.518 0.523 0.221 0.476 -0.745 -0.543 -0.626

  Universitas Sumatera Utara

  9

  X

  0.507 -0.453 0.254 -0.754 -0.414 0.058 -0.543 -0.088

  8

  X

  7

  0.599 0.179 -0.375 0.482 -0.884 0.523 0.254 -0.702

  X

  6

  X

  5 0.133 0.242 0.482 -0.037 -0.540 0.476 -0.414 -0.376

  4

  X

  0.238 0.173 0.748 0.896 0.915 0.957 0.945 0.914 1,000

  X

  Lanjutan Lampiran 4. Perhitungan Analisis Faktor Dengan Menggunakan Matriks

  6 X

  X

  1,000 0.666 0.341 0.075 0.043 0.148 0.044 0.010 0.238

  1

  9 X

  8 X

  7 X

  5 X

  0.666 1,000 0.280 -0.017 -0.001 0.099 0.003 -0.021 0.173

  4 X

  3 X

  2 X

  1 X

  X

  

Matriks Korelasi Sederhana

  2

  X

  0.010 -0.021 0.568 0.987 0.994 0.970 0.993 1,000 0.914

  6

  8

  X

  0.044 0.003 0.627 0.976 0.987 0.984 1,000 0.993 0.945

  7

  X

  0.148 0.099 0.736 0.968 0.970 1,000 0.984 0.970 0.957

  X

  3

  0.043 -0.001 0.571 0.985 1,000 0.970 0.987 0.994 0.915

  5

  0.075 -0.017 0.601 1,000 0.985 0.968 0.976 0.987 0.896 Σ = X

  4

  X

  0.341 0.280 1,000 0.601 0.571 0.736 0.627 0.568 0.748

• 0.367 0.599 -0.668 0.133 -0.442 0.218 0.507 -0.646
• 0.367 0.179 0.461 0.242 -0.407 0.518 -0.453 -0.163
• 0.668 0.461 -0.375 -0.037 0.042 0.221 -0.754 0.421 A = (a ij ) = X
• 0.442 -0.407 -0.884 0.042 -0.540 -0.745 0.058 0.575
• 0.646 -0.163 -0.702 0.421 -0.376 0.575 -0.626 -0.088 Untuk Menghitung nilai Kaiser Meyer Olkin (KMO) maka maka nilai – nilai dari matriks korelasi sederhana dan matriks korelasi parsial harus dikuadratkan terlebih dahulu. Hasil dari kuadrat nilai – nilai tersebut adalah sebagai berikut :

  Universitas Sumatera Utara

  X

  5 0.018 0.059 0.232 0.001 0.292 0.227 0.171 0.141

  ij) = X

  2

  D = (a

  4 0.446 0.213 0.141 0.001 0.002 0.049 0.569 0.177 1.598

  X

  3 0.359 0.032 0.141 0.232 0.781 0.274 0.065 0.493 2.377

  2 0.135 0.032 0.213 0.059 0.166 0.268 0.205 0.027 1.105

  X

  X

  1 0.135 0.359 0.446 0.018 0.195 0.048 0.257 0.417 1.875

  X

  9 Jumlah

  8 X

  7 X

  6 X

  1.141

  6 0.195 0.166 0.781 0.002 0.292 0.555 0.003 0.331 2.325

  4 X

  ∑ ∑ r

  = 0.681 Dengan demikian, diperoleh nilai KMO yaitu sebesar 0.681 sehingga dapat disimpulkan bahwa analisis faktor tepat untuk digunakan

  34.376 50.464

  34.376 + 16.088 =

  = 34.376

  ≠i n i n j ≠i n i

  2 ij n j

  2 ij

  ≠i n i

  X

  2 ij n j

  ∑ ∑ r

  16.088 Setelah nilai – nilai dari matriks korelasi sederhana dan matriks korelasi parsial dikuadratkan dan kemudian dilakukan penjumlahan, maka tahap selanjutnya adalah menghitung nilai Kaiser Meyer Olkin (KMO). Proses perhitungannya adalah sebagai berikut : KMO =

  9 0.417 0.027 0.493 0.177 0.141 0.331 0.392 0.008 1.986 Jumlah

  X

  8 0.257 0.205 0.065 0.569 0.171 0.003 0.295 0.008 1.573

  X

  7 0.048 0.268 0.274 0.049 0.227 0.555 0.295 0.392 2.108

  5 X

  3 X

  Lanjutan Lampiran 4.

  9 Jumlah

  3 0.116 0.078 0.361 0.326 0.542 0.393 0.323 0.560 2.699

  X

  2 0.444 0.078 0.000 0.000 0.010 0.000 0.000 0.030 0.562

  X

  0.444 0.116 0.006 0.002 0.022 0.002 0.000 0.057 0.649

  1

  X

  8 X

  4

  7 X

  6 X

  5 X

  4 X

  3 X

  2 X

  1 X

  X

  X

  0.006 0.000 0.361 0.970 0.937 0.953 0.974 0.803 5.004 Σ = (r

  2 X

  0.000 0.000 0.323 0.974 0.988 0.941 0.986 0.835 5.047

  1 X

  X

  34.376

  Jumlah

  0.057 0.030 0.560 0.803 0.837 0.916 0.893 0.835 4.931

  9

  X

  8

  2 ij ) =

  X

  7 0.002 0.000 0.393 0.953 0.974 0.968 0.986 0.893 5.169

  X

  6 0.022 0.010 0.542 0.937 0.941 0.968 0.941 0.916 5.277

  X

  0.002 0.000 0.326 0.970 0.941 0.974 0.988 0.837 5.038

  5

  X

• ∑ ∑ a
• 5)

  Lanjutan Lampiran 4.

  6 � |0,0000000000154|

  2

hitung

  > χ

  2 (0.05),(36)

  = 50.998 d. Perhitungan χ

  2

  = − �(33 − 1) −

  (2(9) + 5)

  = −[32 − 3.83](−24,897)

  = 50.998 c. Kriteria pengujian : Tolak H jika

  = −(28,17)(−24,897)

  = 701.348 e. Kesimpulan

  Dari hasil perhitungan diperoleh χ

  2 hitung

  = 701.348 > χ

  2 (0.05),(36)

  = 50.998, sehingga H ditolak. Dengan demikian, matriks korelasi sederhana bukan merupakan matriks identitas.

  χ

  2 (0.05),(36)

  Tahapan selanjutnya adalah melakukan uji Bartlett dengan pendekatan statistik chi square untuk menguji apakah matriks korelasi bukan merupakan suatu matriks identitas. Langkah – langkah pengujiannya adalah sebagai berikut : a.

  χ

  Hipotesis H : Matriks korelasi merupakan matriks identitas H

  1 : Matriks korelasi bukan merupakan matriks identitas

  Statistik uji,

  2

  = − �( − 1) −

  (2

  6 � |R| b.

  2 hitung

  χ

  > χ

  2

  α, p(p − 1)/2 α = 5% = 0.05 p = 9 df = p(p

  − 1)

  2 =

  9(9 − 1)

  2 = 36

2 Det (R) = 0,0000000000154

1. PDRB ADHB

  5 X

  0.093 -0.018 0.065 -0.043 -0.029 -0.721 -0.005 0.677 0.082

  1

  9 X

  8 X

  7 X

  6 X

  4 X

  2

  3 X

  2 X

  1 X

  X

  6.233

Matriks Eigen Vector

  0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

  X

  X

  X

  0.063 0.610 0.659 0.042 0.068 -0.009 -0.131 -0.108 0.393

  Universitas Sumatera Utara

  9

  X

  0.791 -0.367 -0.034 0.141 -0.066 0.022 -0.216 -0.133 0.388

  8

  X

  7

  3

  X

  6

  X

  5

  4

  X

  0.187 0.185 -0.117 -0.031 -0.252 0.277 0.803 0.246 0.268

  9

  0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.775 0.000

  Lanjutan Lampiran 4.

  5 X

  X

  1 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

  9 X

  8 X

  7 X

  6 X

  4 X

  0.000

  3 X

  2 X

  1 X

  X

  

Matriks Eigen Value

  Nilai eigen value dan nilai eigen vector dari matriks korelasi sederhana (Σ) diperoleh dengan menggunakan software MATLAB R2010a. Hasilnya adalah sebagai berikut :

  2

  0.001

  8

  X

  X

  0.000 0.000 0.000 0.000 0.000 0.000 0.656 0.000 0.000

  7

  X

  0.000 0.000 0.000 0.000 0.000 0.279 0.000 0.000 0.000

  6

  0.000 0.000 0.000 0.000 0.038 0.000 0.000 0.000 0.000

  0.000 0.000 0.000 0.000 0.000 0.000 0.000

  5

  0.000 0.000 0.000 0.009 0.000 0.000 0.000 0.000 0.000 λ = X

  4