[BPS] Badan Pusat Statistik. 1999-2011. Produksi Hasil Ekspor Perikanan.
Dinas Kelautan dan Perikanan. 1999-2010. Statistika Hasil Ekspor Perikanan 1999-2011. Departemen Kelautan dan Perikanan RI. Jakarta.
Firdaus, M. September 2011. Aplikasi Ekonometrika untuk Dara Panel dan Time Series. IPB Press: Bogor.
Gonarsyah, I. 1987. Landasan Perdagangan Internasional. Departemen Ilmu-Ilmu Sosial Ekonomi Pertanian. Institut Pertanian Bogor, Bogor.
Gujarati.1978. Basic Econometric. MCGraw-Hill Companies Inc. New York.
Index Mundi. 2011. Indexmundi Database. [Indexmundi Online].
http://indexmundi.com [28 April2012]
[DKP] Departemen Kelautan dan Perikanan. 2005. Revitalisasi Perikanan Budidaya 2006-2009. Jakarta: Badan Riset Departemen Kelautan dan Perikanan.
[DKP] Departemen Kelautan dan Perikanan. 2007. Budidaya Rumput Laut (Eucheuma cottonii). Jakarta: Badan Riset Departemen Kelautan dan Perikanan 2007.
Direktorat Jendral Perikanan Budidaya.2009. Profil Rumput Laut Indonesia.
Departemen Kelautan dan Perikanan, Jakarta.
Kementrian Kelautan dan Perikanan. Statistika Ekspor Hasil Perikanan 2005-2011. Kementrian Kelautan dan Perikanan, Jakarta.
Kustantiny A, Sarwanto C, Bernhard, Hadiastuty H, Fajar C, Wahyuni S. 2011.
Profil Peluang Usaha Rumput Laut II. Nikijuluw V, editor. Direktorat Usaha dan Investasi & P2HP – KKP.
Lipsey, Richard G. 1997. Pengantar Makroekonomi. Jilid Kedua. Binarupa Aksara. Jakarta.
Mankiw, N, Gregory. 2000. Teori Makroekonomi Edisi Keempat.
Erlangga.Jakarta.
Rajagukguk, M.M.2009. Analisis Daya Saing Rumput Laut Indonesia di Paasr Internasional [skripsi]. Fakultas Ekonomi dan Manajemen, Institut Pertanian Bogor Bogor.
Salvatore, D. 1997. Ekonomi Internasional. Cetakan Pertama. Penerbit Erlangga.
Jakarta.
Santono et al. 2009. Peningkatan Nilai Tambah Rumput LautMelalui Teknologi Penanganan dan Pengolahan. Jakarta: Direktorat Pengolahan Hasil &
P2HP – DKP.
Surono A et al. 2009. Profil Rumput Laut Indonesia. Jakarta: Direktorat Jenderal Perikanan Budidaya & Direktorat Produksi - DKP.
76
LAMPIRAN
Lampiran 1. Korelasi Antar Peubah x dan y
Correlations: Y, LnX1, LnX2, LnX3, X4, LnX5, LnX6
Y LnX1 LnX2 LnX3 X4 LnX5 LnX1 -0.669
0.012
LnX2 -0.129 0.301 0.675 0.318
LnX3 0.853 -0.232 0.117 0.000 0.446 0.703
X4 0.851 -0.462 -0.013 0.854 0.000 0.112 0.967 0.000
LnX5 0.689 -0.236 0.304 0.774 0.785 0.009 0.439 0.312 0.002 0.001
LnX6 0.879 -0.275 0.053 0.989 0.854 0.761 0.000 0.363 0.865 0.000 0.000 0.003
Cell Contents: Pearson correlation P-Value
78 Lampiran 2. Analisis Regresi
Regression Analysis: Y versus LnX1, LnX2, LnX3, X4, LnX5, LnX6
The regression equation is
Y = 26.6 - 0.790 LnX1 - 2.28 LnX2 + 0.400 LnX3 - 0.262 X4 + 0.108 LnX5 + 1.46 LnX6
Predictor Coef SE Coef T P VIF Constant 26.64 25.74 1.04 0.340 LnX1 -0.7903 0.1412 -5.60 0.001 1.6 LnX2 -2.278 2.641 -0.86 0.421 1.7 LnX3 0.4004 0.6467 0.62 0.559 63.6 X4 -0.2616 0.4795 -0.55 0.605 6.4 LnX5 0.1081 0.1744 0.62 0.558 3.8 LnX6 1.463 1.656 0.88 0.411 59.5
S = 0.331981 R-Sq = 97.4% R-Sq(adj) = 94.8%
Analysis of Variance
Source DF SS MS F P Regression 6 24.7679 4.1280 37.46 0.000 Residual Error 6 0.6613 0.1102
Total 12 25.4292
Durbin-Watson statistic = 2.55225
NILAI VIF > 10 MAKA TERJADI PELANGGARAN MULTIKOLINIERITAS
Lampiran 3. Asumsi dalam Regresi Berganda
1. Uji Kenormalan
Ho : Sisaan meyebar normal H1 : Sisaan tidak menyebar normal Kriteria keputusan
p-value < alpha(0,05) -> tolak Ho (sisaan tidak meyebar normal) p-value > alpha(0.05) -> terima H0 (sisaam menyebar normal)
1 5 0 0 0 0 0 0
2. Uji autokorelasi Ho : Sisaan saling bebas H1 : Sisaan tidak saling bebas Kriteria keputusan
p-value < alpha(0,05) -> tolak Ho (Sisaan tidak saling bebas) p-value >alpha(0.05) -> terima H0 (Sisaan saling bebas)
Runs Test: RESI1
Runs test for RESI1
Runs above and below K = -4.08618E-08 The observed number of runs = 8 The expected number of runs = 7.46154 6 observations above K, 7 below
* N is small, so the following approximation may be invalid.
P-value = 0.754
80 3. Uji Heteroskedastisas
Ho : ragam homogen H1 : ragam tidak homogeny Kriteria keputusan
p-value < alpha(0,05) -> tolak H0 p-value >alpha(0.05) -> terima H0
Regression Analysis: e2 versus x1, x2, x3, x4, x5, x6
The regression equation is
e2 = - 5.56E+14 - 2.04E+08 x1 + 5.53E+10 x2 - 36023264 x3 - 4.15E+13 x4 - 6.85E+08 x5 + 4.83E+10 x6
Predictor Coef SE Coef T P VIF Constant -5.56443E+14 2.41235E+14 -2.31 0.061
x1 -204305146 285838096 -0.71 0.502 2.004 x2 55296526366 24689084130 2.24 0.066 1.201 x3 -36023264 49001875 -0.74 0.490 52.420 x4 -4.15381E+13 4.88973E+13 -0.85 0.428 6.441 x5 -685238495 835186318 -0.82 0.443 15.811 x6 48335096953 34861639285 1.39 0.215 49.793
S = 3.379492E+13 R-Sq = 55.8% R-Sq(adj) = 11.6%
PRESS = 4.053833E+28 R-Sq(pred) = 0.00%
Analysis of Variance
Source DF SS MS F P Regression 6 8.65964E+27 1.44327E+27 1.26 0.392 Residual Error 6 6.85258E+27 1.14210E+27
Total 12 1.55122E+28
Source DF Seq SS x1 1 2.49367E+25 x2 1 4.48990E+27 x3 1 8.60640E+25 x4 1 4.67290E+26 x5 1 1.39595E+27 x6 1 2.19549E+27
Durbin-Watson statistic = 2.49705
Ragam Homogen
Lampiran 4. Regresi Komponen Utama
KOMPONEN UTAMA
Principal Component Analysis: Z1, Z2, Z3, Z4, Z5, Z6
Eigenanalysis of the Correlation Matrix
Eigenvalue 3.6588 1.3424 0.6303 0.2399 0.1204 0.0082 Proportion 0.610 0.224 0.105 0.040 0.020 0.001 Cumulative 0.610 0.834 0.939 0.979 0.999 1.000
Variable PC1 PC2 PC3 PC4 PC5 PC6 Z1 0.215 0.608 0.714 -0.207 0.177 -0.030 Z2 -0.058 0.753 -0.568 0.300 0.121 -0.045 Z3 -0.498 0.080 0.259 0.368 -0.166 0.718 Z4 -0.493 -0.119 -0.008 -0.159 0.846 -0.049 Z5 -0.459 0.207 -0.162 -0.758 -0.382 0.017 Z6 -0.499 0.022 0.272 0.363 -0.257 -0.692
NILAI EIGEN YANG LEBH DARI SATU ADALAH PC1 DAN PC2 Skor Komponen Utama
W1 W2 W3 W4 W5 W6
2.49878 1.0348 ‐0.35054 ‐0.35642 0.094523 0.001076 2.22215 2.37148 ‐0.79874 0.29758 0.24405 0.063243 2.18036 ‐0.27458 1.01905 ‐0.73574 ‐0.33009 0.070112 2.76363 ‐1.75867 0.83236 1.12439 0.146022 0.006602 1.29273 ‐0.40246 ‐0.67225 ‐0.09764 ‐0.77299 ‐0.13857
‐0.08077 ‐1.24624 ‐0.46059 ‐0.51322 0.565348 ‐0.16359
‐0.75921 ‐1.31495 ‐0.47732 ‐0.31756 0.213972 0.128692
‐1.26918 ‐0.91843 ‐0.61124 ‐0.1602 0.001132 0.132094
‐1.22559 0.33682 0.36799 ‐0.13606 0.20761 ‐0.02478
‐1.68433 0.3387 ‐0.48828 0.3447 0.020849 ‐0.0646
‐1.70733 0.87838 0.29418 0.37331 0.097374 ‐0.04083
‐1.75375 0.97891 1.77725 ‐0.19917 ‐0.00862 ‐0.02246
‐2.47751 ‐0.02375 ‐0.43188 0.37603 ‐0.47918 0.053008
82 Lanjutan Lampiran 4.
Regression Analysis: Y versus W1, W2
The regression equation is Y = 16.1 - 0.691 W1 - 0.406 W2
Predictor Coef SE Coef T P VIF Constant 16.1196 0.1189 135.61 0.000 W1 -0.69051 0.06468 -10.68 0.000 1.0 W2 -0.4062 0.1068 -3.80 0.003 1.0
S = 0.428584 R-Sq = 92.8% R-Sq(adj) = 91.3%
Analysis of Variance
Source DF SS MS F P Regression 2 23.592 11.796 64.22 0.000 Residual Error 10 1.837 0.184
Total 12 25.429
Source DF Seq SS W1 1 20.935 W2 1 2.657
Durbin-Watson statistic = 2.38858
Lampiran 5. Transformasi
Lny = 16.1 ‐0.3954 Z1 ‐0.2656 Z2 + 0.3116 Z3 + 0.3890 Z4 + 0.2331 Z5 + 0.3359 Z6
Lny = 16.1 ‐0.3954 ⎟⎟
Y = 16.1 ‐0.4550 LnX1 ‐5.7103 LnX2 + 0.2636 LnX3 + 0.7682 X4 + 0.2183 LnX5 + 0.7525 LnX6
84 Lampiran 6. Standarisasi Data
Data Awal
Lny LNx1 LNx2 LNx3 Lnx4 Lnx5 Lnx6
13.59971 11.48855 9.158099 11.96319 0 10.12999 7.680031 14.00745 11.77018 9.230143 12.42094 0 10.04642 7.774506 14.28739 11.5851 9.065661 12.41685 0 10.23545 7.869545 15.24726 10.53768 9.05275 12.53829 0 7.957177 7.966241 15.9152 10.13158 9.11603 12.59993 0 10.60068 8.076347 16.4161 9.840388 9.093807 12.9253 1 10.8398 8.192599 17.02975 9.567175 9.093807 13.7219 1 11.14568 8.319351 17.39442 9.535101 9.11052 14.13357 1 11.4678 8.465425 16.22959 10.84895 9.126959 14.36275 1 11.45183 8.621634 17.37748 10.1231 9.159047 14.57868 1 11.51241 8.730152 16.60843 10.89217 9.159047 14.9019 1 11.45107 8.823791 16.99301 11.94293 9.11438 15.18047 1 11.74544 8.928535 18.43292 9.729967 9.145162 15.24168 1 11.98884 8.935904 Data Standarisasi variable X
Lny Z1 Z2 Z3 Z4 Z5 Z6
13.59971 1.00595 0.73853 ‐1.39945 ‐1.21529 ‐0.64137 ‐1.47291 14.00745 1.32814 2.22734 ‐1.01028 ‐1.21529 ‐0.71635 ‐1.27131 14.28739 1.12102 ‐1.17565 ‐1.01028 ‐1.21529 ‐0.53826 ‐1.04731 15.24726 ‐0.08719 ‐1.60103 ‐0.90876 ‐1.21529 ‐2.67536 ‐0.82331 15.9152 ‐0.55897 ‐0.11222 ‐0.858 ‐1.21529 ‐0.20082 ‐0.57692 16.4161 ‐0.89267 ‐0.75028 ‐0.57881 0.75955 0.02413 ‐0.33052 17.02975 ‐1.20335 ‐0.75028 0.08954 0.75955 0.3147 ‐0.03932 17.39442 ‐1.23787 ‐0.3249 0.43641 0.75955 0.61465 0.29667 16.22959 0.26952 0.10047 0.63099 0.75955 0.5959 0.63267 17.37748 ‐0.57048 0.73853 0.81712 0.75955 0.65214 0.87907 16.60843 0.31807 0.71833 1.08945 0.75955 0.59693 1.08915 16.99301 1.52709 ‐0.23175 1.32515 0.75955 0.87278 1.32377 18.43292 ‐1.01924 0.4229 1.37693 0.75955 1.10093 1.34028
Lampiran 7. Tabel Uji Signifikan X1 0.045651 ‐0.4550 ‐9.9671 Significant X2 0.055344 ‐5.7103 ‐103.178 Significant X3 0.022912 0.2636 11.50339 Significant X4 0.023601 0.7682 32.5484 Significant X5 0.025448 0.2183 8.578327 Significant X6 0.02225 0.7525 33.82081 Significant
|T-HITUNG| > 1.96 MAKA TOLAK H0 ARTINYA SIGNIFICANT
LNX1 LNX2 LNX3 LNX4 LNX5 LNX6 LNY
harga ekspor
86 Lampiran 8. Trend Analysis
Produksi
Linear Quadratic Exponential Growth S-Curve
MAPE 1.01E+02 1.95E+01 1.88E+01 2.73E+01
MAD 4.61E+05 1.11E+05 1.94E+05 3.97E+05
MSD 2.58E+11 1.88E+10 7.07E+10 3.25E+11
Volume Ekspor RL ke Dunia
Linear Quadratic Exponential Growth S-Curve
MAPE 18 12 12 21
MAD 9341 7508 7849 12850
MSD 146187295 98175667 121189267 264445023
Volume Ekspr RL ke China
Linear Quadratic Exponential Growth S-Curve
MAPE 1.63E+02 1.04E+02 6.01E+01 5.30E+01
MAD 1.18E+07 1.17E+07 1.10E+07 1.19E+07
MSD 3.28E+14 2.74E+14 2.25E+14 4.76E+14
Harga Ekspor
Linear Quadratic Exponential Growth S-Curve
MAPE 112 79 77
ERROR
MAD 36479 27936 34854
MSD 2017829716 1323829178 2185285005
Nilai Tukar (Exchange Rate)
Linear Quadratic Exponential Growth S-Curve
MAPE 3 3 3
ERROR
MAD 324 277 322
MSD 173054 133213 173196
GDP
Linear Quadratic Exponential Growth S-Curve
MAPE 8 3.3 3.2 11
MAD 277 158.6 168.8 499
MSD 110083 36909.5 58641.2 322216
Lampiran 9. Analisis Trend Produksi Yt = 418687 - 207616*t + 39424.0*t**2
Trend Analysis Plot for produksi Ina
Trend Analysis for produksi Ina
Data produksi Ina
88 Lampiran 10. Analisis Trend Volume Ekspor ke China
Tahun
Trend volume ekspor RL Indonesia ke China Quadratic Trend Model
Yt = 7492915 - 3309723*t + 588396*t**2
Trend Analysis for volume cina
Data volume cina Length 13
NMissing 0
Fitted Trend Equation
Yt = 7492915 - 3309723*t + 588396*t**2
Accuracy Measures
Trend Analysis Plot for volume cina
Lampiran 11. Analisis Trend Volume Ekspor ke Dunia
Trend Volume Ekspor RL Indonesia ke dunia Quadratic Trend Model
Yt = 16071.3 + 2961.99*t + 558.358*t**2
Trend Analysis for volume ina
Data volume ina
Trend Analysis Plot for volume ina
90 Lampiran 12. Analisis Trend Harga Ekspor
Tahun
Trend Analysis Plot for harga ekspor Quadratic Trend Model
Yt = 153451 - 32842.2*t + 2122.85*t**2
Trend Analysis for harga ekspor
Data harga ekspor
Trend Analysis Plot for harga ekspor
Lampiran 13. Analisis Trend Nilai Tukar
Trend Analysis Plot for nilai tukar Quadratic Trend Model
Yt = 9742.76 - 223.506*t + 16.0844*t**2
Trend Analysis for nilai tukar
Data nilai tukar
Trend Analysis Plot for nilai tukar
92 Lampiran 14. Analisis Trend GDP
Tahun
Trend Analysis Plot for GDP Quadratic Trend Model Yt = 1837.45 + 194.065*t + 21.7980*t**2
Results for: data skripsi.MTW
Trend Analysis for GDP
Data GDP
Trend Analysis Plot for GDP
Trend Analysis for volume cina