Lampiran 1. Peta administrasi Riau dan plotting stasiun pengamatan wilayah Riau

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LAMPIRAN

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Lampiran 1. Peta administrasi Riau dan plotting stasiun pengamatan wilayah Riau

Sumber : Badan Koordinasi Survei dan Pemetaan Nasional (Bakosurtanal)

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Lampiran 2. Proses pengolahan data hujan satelit dari data CMORPH

Data CMORPH memberikan gambaran curah hujan estimasi secara global. Informasi numerik tersebut diperoleh dengan mengkonversi informasi hujan format shapefile menjadi format text sehingga akan diperoleh nilai curah hujan yang diinginkan. Berikut ini langkah pengolahan data hujan satelit dari data CMORPH (Oktavariani 2008) :

1. Data CMORPH yang sudah diekstrak dari format zip, kemudian dibuka menggunakan Arcview 3.3 dengan mengaktifkan extention 3D-Analysi, Grid Analyst, dan Spatial Analyst.

2. Open basemap Indonesia, kemudian cropping wilayah kajian – save – ok.

3. Open data CMORPH.

4. Drid Analyst – extract grid theme using polygon – pilih hasil cropping pada langkah sebelumnya.

5. Aktifkan theme hasil extract – convert grid theme to XYZ text file.

Maka akan diperoleh informasi hujan global sesuai dengan koordinat lintang dan bujur yang

tersimpan dalam file .txt.

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Lampiran 3. Uji dua regresi Stasiun Pekanbaru 2.1 Musim Hujan

Regression Analysis: Y versus X

The regression equation is Y = 0.960 X

Predictor Coef SE Coef T P Noconstant

X 0.96003 0.05279 18.19 0.000 S = 60.0985

Analysis of Variance

Source DF SS MS F P Regression 1 1194507 1194507 330.72 0.000 Residual Error 107 386466 3612

Total 108 1580973

2.2 Musim Kemarau

Regression Analysis: Y versus X

The regression equation is Y = 1.08 X

Predictor Coef SE Coef T P Noconstant

X 1.07893 0.07389 14.60 0.000

S = 52.6893

Analysis of Variance

Source DF SS MS F P Regression 1 591987 591987 213.24 0.000 Residual Error 107 297050 2776

Total 108 889037

Z = 0.8261 dengan α = 0.7967, dimana α > taraf nyata sehingga persamaan musim hujan dan

musim kemarau tidak berbeda nyata.

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Lampiran 4. Uji dua regresi Stasiun Japura Rengat 3.1 Musim Hujan

Regression Analysis: Y versus X

The regression equation is Y = 0.495 X

Predictor Coef SE Coef T P Noconstant

X 0.49482 0.03271 15.13 0.000 S = 32.3208

Analysis of Variance

Source DF SS MS F P Regression 1 239077 239077 228.86 0.000 Residual Error 107 111776 1045

Total 108 350853

3.2 Musim Kemarau

Regression Analysis: Y versus X

The regression equation is Y = 0.595 X

Predictor Coef SE Coef T P Noconstant

X 0.59536 0.05101 11.67 0.000 S = 29.9739

Analysis of Variance

Source DF SS MS F P Regression 1 122398 122398 136.24 0.000 Residual Error 107 96132 898

Total 108 218531

Z = 0.7392 dengan α = 0.7704, dimana α > taraf nyata sehingga persamaan musim hujan dan

musim kemarau tidak berbeda nyata.

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Lampiran 5. Uji dua regresi Stasiun Tanjung Pinang 4.1 Musim Hujan

Regression Analysis: Y versus X

The regression equation is Y = 0.826 X

Predictor Coef SE Coef T P Noconstant

X 0.8259 0.1009 8.19 0.000 S = 53.8562

Analysis of Variance

Source DF SS MS F P Regression 1 194322 194322 67.00 0.000 Residual Error 107 310352 2900

Total 108 504675

4.2 Musim Kemarau

Regression Analysis: Y versus X

The regression equation is Y = 1.36 X

Predictor Coef SE Coef T P Noconstant

X 1.35802 0.09341 14.54 0.000 S = 38.7146

Analysis of Variance

Source DF SS MS F P Regression 1 316800 316800 211.37 0.000 Residual Error 107 160374 1499

Total 108 477174

Z = 6.1702 dengan α = 0.9997, dimana α > taraf nyata sehingga persamaan musim hujan dan

musim kemarau tidak berbeda nyata.

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Lampiran 6. Uji dua regresi Stasiun Dabo Singkep 5.1 Musim Hujan

Regression Analysis: Y versus X

The regression equation is Y = 0.799 X

Predictor Coef SE Coef T P Noconstant

X 0.79933 0.04956 16.13 0.000 S = 43.7527

Analysis of Variance

Source DF SS MS F P Regression 1 497924 497924 260.11 0.000 Residual Error 107 204830 1914

Total 108 702754

5.2 Musim Kemarau

Regression Analysis: Y versus X

The regression equation is Y = 1.15 X

Predictor Coef SE Coef T P Noconstant

X 1.14893 0.06827 16.83 0.000 S = 41.8755

Analysis of Variance

Source DF SS MS F P Regression 1 496658 496658 283.23 0.000 Residual Error 107 187631 1754

Total 108 684289

Z = 2.5661 dengan α = 0.9949, dimana α > taraf nyata sehingga persamaan musim hujan dan

musim kemarau tidak berbeda nyata.

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Lampiran 7. Contoh hasil keluaran VIF (Variation Inflation Factor) Stasiun Pekanbaru

• Domain 3x3

Regression Analysis: X1 versus X2, X3, X4, X5, X6, X7, X8, X9

The regression equation is

X1 = 1.45 + 1.24 X2 - 0.450 X3 + 0.658 X4 - 0.680 X5 + 0.231 X6 - 0.0625 X7 - 0.0212 X8 + 0.0784 X9

Predictor Coef SE Coef T P VIF Constant 1.451 1.749 0.83 0.408 X2 1.24220 0.06916 17.96 0.000 14.2 X3 -0.45022 0.06872 -6.55 0.000 12.8 X4 0.65843 0.08441 7.80 0.000 22.9 X5 -0.6796 0.1015 -6.70 0.000 31.4 X6 0.23121 0.09228 2.51 0.013 23.3 X7 -0.06250 0.07583 -0.82 0.411 20.4 X8 -0.02120 0.09154 -0.23 0.817 25.2 X9 0.07844 0.07125 1.10 0.272 13.1 S = 12.9878 R-Sq = 94.7% R-Sq(adj) = 94.5%

Analysis of Variance

Source DF SS MS F P Regression 8 517066 64633 383.16 0.000 Residual Error 171 28845 169

Total 179 545911

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Lampiran 8. Contoh hasil keluaran VIF (Variation Inflation Factor) Stasiun Japura Rengat

• Domain 3x3

Regression Analysis: X1 versus X2, X3, X4, X5, X6, X7, X8, X9

The regression equation is

X1 = 0.99 + 1.10 X2 - 0.316 X3 + 0.658 X4 - 0.457 X5 + 0.0749 X6 - 0.174 X7 + 0.261 X8 - 0.149 X9

Predictor Coef SE Coef T P VIF Constant 0.995 1.663 0.60 0.551 X2 1.10488 0.08217 13.45 0.000 15.2 X3 -0.31624 0.07824 -4.04 0.000 12.0 X4 0.65814 0.07614 8.64 0.000 17.7 X5 -0.4569 0.1057 -4.32 0.000 28.7 X6 0.07490 0.09448 0.79 0.429 19.1 X7 -0.17449 0.07813 -2.23 0.027 20.7 X8 0.26086 0.09875 2.64 0.009 29.6 X9 -0.14878 0.07621 -1.95 0.053 15.0 S = 12.7578 R-Sq = 94.1% R-Sq(adj) = 93.9%

Analysis of Variance

Source DF SS MS F P Regression 8 446312 55789 342.76 0.000 Residual Error 171 27832 163

Total 179 474144

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Lampiran 9. Contoh hasil keluaran VIF (Variation Inflation Factor) Stasiun Tanjung Pinang

• Domain 3x3

Regression Analysis: X1 versus X2, X3, X4, X5, X6, X7, X8, X9

The regression equation is

X1 = 1.05 + 1.24 X2 - 0.317 X3 + 0.353 X4 - 0.501 X5 + 0.088 X6 + 0.218 X7 - 0.022 X8 - 0.0578 X9

Predictor Coef SE Coef T P VIF Constant 1.0506 0.6054 1.74 0.084 X2 1.23808 0.06971 17.76 0.000 26.0 X3 -0.31743 0.07132 -4.45 0.000 31.9 X4 0.35316 0.07571 4.66 0.000 29.1 X5 -0.5011 0.1220 -4.11 0.000 84.5 X6 0.0883 0.1030 0.86 0.392 72.5 X7 0.21799 0.07940 2.75 0.007 31.1 X8 -0.0220 0.1154 -0.19 0.849 78.8 X9 -0.05778 0.08039 -0.72 0.473 45.9 S = 6.23610 R-Sq = 96.5% R-Sq(adj) = 96.4%

Analysis of Variance

Source DF SS MS F P Regression 8 185294 23162 595.59 0.000 Residual Error 171 6650 39

Total 179 191944

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Lampiran 10. Contoh hasil keluaran VIF (Variation Inflation Factor) Stasiun Dabo Singkep

• Domain 3x3

Regression Analysis: X1 versus X2, X3, X4, X5, X6, X7, X8, X9

The regression equation is

X1 = 0.455 + 1.03 X2 - 0.279 X3 + 0.594 X4 - 0.345 X5 - 0.0373 X6 - 0.0876 X7 - 0.128 X8 + 0.213 X9

Predictor Coef SE Coef T P VIF Constant 0.4545 0.8934 0.51 0.612 X2 1.02938 0.07334 14.04 0.000 29.4 X3 -0.27896 0.06156 -4.53 0.000 30.5 X4 0.59405 0.05529 10.74 0.000 16.7 X5 -0.34537 0.08517 -4.06 0.000 51.0 X6 -0.03726 0.05928 -0.63 0.530 28.9 X7 -0.08762 0.06275 -1.40 0.164 25.0 X8 -0.12783 0.09386 -1.36 0.175 62.2 X9 0.21333 0.05913 3.61 0.000 26.0 S = 8.22001 R-Sq = 95.9% R-Sq(adj) = 95.7%

Analysis of Variance

Source DF SS MS F P Regression 8 270757 33845 500.89 0.000 Residual Error 171 11554 68

Total 179 282311

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Lampiran 11. Partial Least Square (PLS) Stasiun Pekanbaru a. Domain 3x3

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, X9

Number of components specified: 5

Analysis of Variance for Y

Source DF SS MS F P Regression 5 399225 79845.0 31.37 0.000 Residual Error 174 442882 2545.3

Total 179 842107

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.860830 502282 0.403541 2 0.896507 453751 0.461172 3 0.954814 450520 0.465008 4 0.973081 445720 0.470709 5 0.982214 442882 0.474079

b. Domain 5x5

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 5

Analysis of Variance for Y

Source DF SS MS F P Regression 5 3682367 736473 221.08 0.000 Residual Error 174 579645 3331

Total 179 4262012

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.840239 942939 0.778757 2 0.945897 799293 0.812461 3 0.957915 636876 0.850569 4 0.967756 600215 0.859171 5 0.982104 579645 0.863997

c. Domain 7x7

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 3

Analysis of Variance for Y

Source DF SS MS F P Regression 3 41562899 13854300 4531.95 0.000 Residual Error 176 538037 3057

Total 179 42100936

Model Selection and Validation for Y

Components X Variance Error SS R-Sq

1 0.905286 1280752 0.969579

2 0.970463 571409 0.986428

3 0.975203 538037 0.987220

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d. Domain 9x9

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 11

Analysis of Variance for Y

Source DF SS MS F P Regression 11 574214 52201.2 32.74 0.000 Residual Error 168 267893 1594.6

Total 179 842107

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.621955 547959 0.349300 2 0.702334 487081 0.421592 3 0.755631 438254 0.479575 4 0.792673 400086 0.524899 5 0.821672 370721 0.559769 6 0.830362 329533 0.608680 7 0.845836 308871 0.633216 8 0.863729 296217 0.648242 9 0.880338 286275 0.660048 10 0.890059 274865 0.673598 11 0.900857 267893 0.681878

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Lampiran 12. Partial Least Square (PLS) Stasiun Japura Rengat a. Domain 3x3

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, X9

Number of components specified: 5

Analysis of Variance for Y

Source DF SS MS F P Regression 5 66772 13354.3 15.28 0.000 Residual Error 174 152053 873.9

Total 179 218824

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.839062 164460 0.248437 2 0.886706 160725 0.265506 3 0.902245 155724 0.288359 4 0.954468 154428 0.294281 5 0.978373 152053 0.305138

b. Domain 5x5

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 4

Analysis of Variance for Y

Source DF SS MS F P Regression 4 9043314 2260828 1208.97 0.000 Residual Error 175 327257 1870

Total 179 9370570

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.828310 1685862 0.820090 2 0.944038 650152 0.930618 3 0.964630 352851 0.962345 4 0.980464 327257 0.965076 5 0.984394 293037 0.968728

c. Domain 7x7

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 3

Analysis of Variance for Y

Source DF SS MS F P Regression 3 27410368 9136789 4606.41 0.000 Residual Error 176 349095 1983

Total 179 27759464

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.830031 1975675 0.928829 2 0.945332 462319 0.983346 3 0.951652 349095 0.987424

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d. Domain 9x9

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 12

Analysis of Variance for Y

Source DF SS MS F P Regression 12 116608 9717.36 15.88 0.000 Residual Error 167 102216 612.07

Total 179 218824

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.609809 162236 0.258602 2 0.690525 154449 0.294184 3 0.728496 143092 0.346086 4 0.753411 136839 0.374664 5 0.767391 128869 0.411085 6 0.787903 124107 0.432845 7 0.805584 118179 0.459937 8 0.855198 116123 0.469330 9 0.871852 111811 0.489036 10 0.884067 107676 0.507934 11 0.895979 104446 0.522692 12 0.908082 102216 0.532886

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Lampiran 13. Partial Least Square (PLS) Stasiun Tanjung Pinang a. Domain 3x3

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, X9

Number of components specified: 4

Analysis of Variance for Y

Source DF SS MS F P Regression 4 83834 20958.4 13.54 0.000 Residual Error 175 270834 1547.6

Total 179 354668

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.930431 286216 0.193004 2 0.939306 272444 0.231834 3 0.958772 271190 0.235371 4 0.984754 270834 0.236372

b. Domain 5x5

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 5

Analysis of Variance for Y

Source DF SS MS F P Regression 5 812309 162462 100.86 0.000 Residual Error 174 280265 1611

Total 179 1092574

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.815661 552145 0.494638 2 0.946064 349036 0.680538 3 0.954771 309863 0.716392 4 0.967711 294457 0.730492 5 0.977193 280265 0.743482

c. Domain 7x7

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 5

Analysis of Variance for Y

Source DF SS MS F P Regression 5 2330496 466099 261.94 0.000 Residual Error 174 309621 1779

Total 179 2640117

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.779742 828029 0.686367 2 0.939489 404162 0.846915 3 0.949731 344351 0.869570 4 0.964833 321995 0.878038 5 0.971654 309621 0.882725

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d. Domain 9x9

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 11

Analysis of Variance for Y

Source DF SS MS F P Regression 11 176839 16076.3 15.19 0.000 Residual Error 168 177814 1058.4

Total 179 354653

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.753082 285120 0.196058 2 0.793677 262863 0.258816 3 0.837217 247479 0.302195 4 0.860591 233300 0.342172 5 0.903983 226447 0.361496 6 0.919291 210808 0.405594 7 0.931429 203533 0.426105 8 0.935733 192836 0.456268 9 0.941689 186380 0.474473 10 0.947443 181414 0.488473 11 0.952402 177814 0.498626

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Lampiran 14. Partial Least Square (PLS) Stasiun Dabo Singkep a. Domain 3x3

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, X9

Number of components specified: 5

Analysis of Variance for Y

Source DF SS MS F P Regression 5 324341 64868.1 49.99 0.000 Residual Error 174 225785 1297.6

Total 179 550126

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.904752 300257 0.454203 2 0.940044 243121 0.558063 3 0.970850 234345 0.574016 4 0.981111 228004 0.585541 5 0.988920 225785 0.589575

b. Domain 5x5

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 5

Analysis of Variance for Y

Source DF SS MS F P Regression 5 3973367 794673 638.54 0.000 Residual Error 174 216545 1245

Total 179 4189911

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.794795 635450 0.848338 2 0.950092 292760 0.930128 3 0.969522 262479 0.937355 4 0.976460 230797 0.944916 5 0.981562 216545 0.948318

c. Domain 7x7

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 3

Analysis of Variance for Y

Source DF SS MS F P Regression 3 5921141 1973714 1330.72 0.000 Residual Error 176 261041 1483

Total 179 6182182

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.857442 701592 0.886514 2 0.942271 309220 0.949982 3 0.958812 261041 0.957775

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d. Domain 9x9

PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8, ...

Number of components specified: 11

Analysis of Variance for Y

Source DF SS MS F P Regression 11 419302 38118.3 48.95 0.000 Residual Error 168 130824 778.7

Total 179 550126

Model Selection and Validation for Y Components X Variance Error SS R-Sq 1 0.681429 317800 0.422315 2 0.732657 237731 0.567861 3 0.800585 225696 0.589738 4 0.845445 212952 0.612903 5 0.865842 198461 0.639244 6 0.877460 182578 0.668115 7 0.893917 170925 0.689299 8 0.904720 154741 0.718717 9 0.912864 144388 0.737536 10 0.923236 138315 0.748576 11 0.928684 130824 0.762192

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Lampiran 15. Keragaman curah hujan yang dapat diterangkan oleh setiap komponen berdasarkan metode Partial Least Square (PLS)

Stasiun Domain X variance

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12

Pekanbaru

3x3 0.8608 0.8965 0.9548 0.9731 0.9822 5x5 0.8402 0.9459 0.9579 0.9678 0.9821 7x7 0.9053 0.9705 0.9752

9x9 0.6220 0.7023 0.7556 0.7927 0.8217 0.8304 0.8458 0.8637 0.8803 0.8901 0.9009

Japura Rengat

3x3 0.8391 0.8867 0.9022 0.9545 0.9784 5x5 0.8283 0.9440 0.9646 0.9805

7x7 0.8300 0.9453 0.9517

9x9 0.6098 0.6905 0.7285 0.7534 0.7674 0.7879 0.8056 0.8552 0.8719 0.8841 0.8960 0.9081

Tanjung Pinang

3x3 0.9304 0.9393 0.9588 0.9848

5x5 0.8157 0.9461 0.9548 0.9677 0.9772 7x7 0.7797 0.9395 0.9497 0.9648 0.9717

9x9 0.7531 0.7937 0.8372 0.8606 0.9040 0.9193 0.9314 0.9357 0.9417 0.9474 0.9524

Dabo Singkep

3x3 0.9048 0.9400 0.9709 0.9811 0.9889 5x5 0.7948 0.9501 0.9695 0.9765 0.9816 7x7 0.8574 0.9423 0.9588

9x9 0.6814 0.7327 0.8006 0.8454 0.8658 0.8775 0.8939 0.9047 0.9129 0.9232 0.9287

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Lampiran 16. Koefisien determinasi berdasarkan metode Partial Least Square (PLS)

Stasiun Domain R

²

(%)

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12

Pekanbaru

3x3 40.4 46.1 46.5 47.1 47.4

5x5 77.9 81.2 85.1 85.9 86.4

7x7 97.0 98.6 98.7

9x9 34.9 42.2 48.0 52.5 56.0 60.9 63.3 64.8 66.0 67.4 68.2

Japura Rengat

3x3 24.8 26.6 28.8 29.4 30.5

5x5 82.0 93.1 96.2 96.5

7x7 92.9 98.3 98.7

9x9 25.9 29.4 34.6 37.5 41.1 43.3 46.0 46.9 48.9 50.8 52.3 53.3

Tanjung Pinang

3x3 19.3 23.2 23.5 23.6

5x5 49.5 68.1 71.6 73.0 74.3

7x7 68.6 84.7 87.0 87.8 88.3

9x9 19.6 25.9 30.2 34.2 36.1 40.6 42.6 45.6 47.4 48.8 49.9

Dabo Singkep

3x3 45.4 55.8 57.4 58.6 59.0

5x5 84.8 93.0 93.7 94.5 94.8

7x7 88.7 95.0 95.8

9x9 42.2 56.8 59.0 61.3 63.9 66.8 68.9 71.9 73.8 74.9 76.2

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Lampiran 17. Regresi sederhana Stasiun Pekanbaru

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Lampiran 18. Regresi sederhana Stasiun Japura Rengat

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Lampiran 19. Regresi sederhana Stasiun Tanjung Pinang

42

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Lampiran 20. Regresi sederhana Stasiun Dabo Singkep

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