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

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1 LAMPIRAN 19

2 20 Lampiran 1. Peta administrasi Riau dan plotting stasiun pengamatan wilayah Riau Sumber : Badan Koordinasi Survei dan Pemetaan Nasional (Bakosurtanal)

3 21 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.

4 22 Lampiran 3. Uji dua regresi Stasiun Pekanbaru 2.1 Musim Hujan Regression Analysis: Y versus X The regression equation is Y = X Predictor Coef SE Coef T P Noconstant X S = Analysis of Variance Regression Residual Error Total Musim Kemarau Regression Analysis: Y versus X The regression equation is Y = 1.08 X Predictor Coef SE Coef T P Noconstant X S = Analysis of Variance Regression Residual Error Total Z = dengan α = , dimana α > taraf nyata sehingga persamaan musim hujan dan musim kemarau tidak berbeda nyata.

5 23 Lampiran 4. Uji dua regresi Stasiun Japura Rengat 3.1 Musim Hujan Regression Analysis: Y versus X The regression equation is Y = X Predictor Coef SE Coef T P Noconstant X S = Analysis of Variance Regression Residual Error Total Musim Kemarau Regression Analysis: Y versus X The regression equation is Y = X Predictor Coef SE Coef T P Noconstant X S = Analysis of Variance Regression Residual Error Total Z = dengan α = , dimana α > taraf nyata sehingga persamaan musim hujan dan musim kemarau tidak berbeda nyata.

6 24 Lampiran 5. Uji dua regresi Stasiun Tanjung Pinang 4.1 Musim Hujan Regression Analysis: Y versus X The regression equation is Y = X Predictor Coef SE Coef T P Noconstant X S = Analysis of Variance Regression Residual Error Total Musim Kemarau Regression Analysis: Y versus X The regression equation is Y = 1.36 X Predictor Coef SE Coef T P Noconstant X S = Analysis of Variance Regression Residual Error Total Z = dengan α = , dimana α > taraf nyata sehingga persamaan musim hujan dan musim kemarau tidak berbeda nyata.

7 25 Lampiran 6. Uji dua regresi Stasiun Dabo Singkep 5.1 Musim Hujan Regression Analysis: Y versus X The regression equation is Y = X Predictor Coef SE Coef T P Noconstant X S = Analysis of Variance Regression Residual Error Total Musim Kemarau Regression Analysis: Y versus X The regression equation is Y = 1.15 X Predictor Coef SE Coef T P Noconstant X S = Analysis of Variance Regression Residual Error Total Z = dengan α = , dimana α > taraf nyata sehingga persamaan musim hujan dan musim kemarau tidak berbeda nyata.

8 26 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 = X X X X X X X X9 Predictor Coef SE Coef T P VIF Constant X X X X X X X X S = R-Sq = 94.7% R-Sq(adj) = 94.5% Analysis of Variance Regression Residual Error Total

9 27 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 = X X X X X X X X9 Predictor Coef SE Coef T P VIF Constant X X X X X X X X S = R-Sq = 94.1% R-Sq(adj) = 93.9% Analysis of Variance Regression Residual Error Total

10 28 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 = X X X X X X X X9 Predictor Coef SE Coef T P VIF Constant X X X X X X X X S = R-Sq = 96.5% R-Sq(adj) = 96.4% Analysis of Variance Regression Residual Error Total

11 29 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 = X X X X X X X X9 Predictor Coef SE Coef T P VIF Constant X X X X X X X X S = R-Sq = 95.9% R-Sq(adj) = 95.7% Analysis of Variance Regression Residual Error Total

12 30 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 Regression Residual Error Total b. Domain 5x5 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 5 Regression Residual Error Total c. Domain 7x7 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 3 Regression Residual Error Total

13 31 d. Domain 9x9 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 11 Regression Residual Error Total

14 32 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 Regression Residual Error Total b. Domain 5x5 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 4 Regression Residual Error Total c. Domain 7x7 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 3 Regression Residual Error Total

15 33 d. Domain 9x9 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 12 Regression Residual Error Total

16 34 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 Regression Residual Error Total b. Domain 5x5 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 5 Regression Residual Error Total c. Domain 7x7 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 5 Regression Residual Error Total

17 35 d. Domain 9x9 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 11 Regression Residual Error Total

18 36 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 Regression Residual Error Total b. Domain 5x5 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 5 Regression Residual Error Total c. Domain 7x7 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 3 Regression Residual Error Total

19 37 d. Domain 9x9 PLS Regression: Y versus X1, X2, X3, X4, X5, X6, X7, X8,... Number of components specified: 11 Regression Residual Error Total

20 38 Lampiran 15. Keragaman curah hujan yang dapat diterangkan oleh setiap komponen berdasarkan metode Partial Least Square (PLS) Stasiun Pekanbaru Japura Rengat Tanjung Pinang Dabo Singkep Domain X variance PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 3x x x x x x x x x x x x x x x x

21 39 Lampiran 16. Koefisien determinasi berdasarkan metode Partial Least Square (PLS) Stasiun Pekanbaru Japura Rengat Tanjung Pinang Dabo Singkep Domain R 2 (%) PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 3x x x x x x x x x x x x x x x x

22 40 Lampiran 17. Regresi sederhana Stasiun Pekanbaru 40

23 41 Lampiran 18. Regresi sederhana Stasiun Japura Rengat 41

24 42 Lampiran 19. Regresi sederhana Stasiun Tanjung Pinang 42

25 43 Lampiran 20. Regresi sederhana Stasiun Dabo Singkep 43