analisis efisiensi pemasaran buah lokal dan buah impor di ... · manggis. studi kasus desa sipak,...

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LAMPIRAN

Lampiran 1. Jumlah dan Laju Pertumbuhan Penduduk Jakarta dan Indonesia Tahun 197 1 - 1999

Sumber: BPS 1999a. Hal: 46-47 * tidak termasuk Timtim

Lampiran 2. Ekspor-Impor Buah Indonesia Tahun 1996 -- 2000

Sumber BPS, diolah

Keterangan:

- Data buah segarbeku terdiri dari 39 HS - Data buah olahanlkering terdiri dari 64 HS

Lampiran 3. Perkembangan Pembangunan Terminal AgribisnisISub Terminal Agribisnis

I Kab. am I Studi kelayakan

Tahapan Pengembangan Studi kelayakan Operasional

Wilayah Lokasi Merek Kab. Solok

3. Silmsel

4. Riau

8. Banten L

Jaka-Baring Palembang Oku & Oki, MUBA, Linggau Pagar Alam Kodya Batam Kab. Muara Bungo Kab. Rejang Lebong Bandar Lampung lam nun^ Timur

Pluit- Jakarta Utara Pasar Induk Kramatjati

Studi kelayakan Sosialisasi gagasan Studi kelayakan Studi kelayakan Rencana Studi kelayakan Operasional Rencana studi kelayakan Rencana Studi kelavakan

CilegodSerang Tangerang

Studi kelayakan Pembangunan Tahap I1

I

1 [ Kuningan Operasional

Rencana Studi kelayakan Op erasional

/ (modemisasi sistem)

b a b a r Marunda -Jakarta Utara Sukabumi

Maj alengka Cirebon

Rencana realisasi fisik Operasional

Operasional berasional

1 1. Jateng

b , l v i r g y a k a r t a

Sidoarjo Studi kelayakan Tabanan berasional

Brebes Magelang

b a t im

Singaraja Operasional Barito Kuala Operasional & pengembangan

Operasional Studi kelayakan

Semarang Sleman

Operasional Operasional

Yogyakarta Purwodadi-Pasuruan

Operasional berasional

16. Kalbar 17. Ealtim t

Gowa Minahasa

Pontianak Balikpapan/Samarinda

Operasional Tahap usulan

Tondano Kendari

Studi kelayakan Rencana Studi kelayakan

Tahap usulan Studi kelayakan

Sumber: Deptan. 2002

Lampiran 4. Print Out Hasil Olahan Data

APEL IMPOR

Regression A~ialysis

The regression equation is PaA = 829 + 0.565 PaA(t-I) + 0.548 Pk-Pk(t-1) + 0.398 Pk(t-I)

18 cases used 1 cases contain missing values

Predictor Coef StDev T P Co~istant 829.2 763.3 1.09 0.296 PaA(t-I) 0.5650 0.1932 2.92 0.01 1 Pk-Pk(t- 0.5475 0.1 14 1 4.80 0.000 Pk(l-I) 0.3983 0.1629 2.45 0.028

A~ialysis of Variance

Source DF SS MS F P Regressio~i 3 1 10 1 1737 3670579 87.25 0.000 Error 14 588958 42068 Tolal 17 11600694

Source DF Seq SS PaA(t-I) 1 98771 16 Pk-Pk(I- 1 883095 Pk(t-I) 1 251526

Unusual Observatio~ls Obs PaA(t-I) PaA Fit StDev Fit Residual St Resid

7 9750 9750.0 10234.6 94.6 -484.6 -2.66R 14 10500 11750.0 11367.3 157.1 382.7 2.90R

R denotes an observation with a large standardized residual

Durbin-Watson statistic = 2.01

Regression Analysis

Tlie regression equation is P,d3 = 910 + 0.882 PaB(t-I) + 0.239 Pk-Pk(t-I) + 0.048 Pk(t-I)


Prediclor Coef StDcv T P Corista~it 9 10.5 827.1 1.10 0.290 Ptd3(t-I) 0.8818 0.1339 6.59 0.000 Pk-Pk(t- 0.2395 0.1373 1.74 0.103. Pk(t-1) 0.0484 0.1121 0.43 0.673

Analysis of Variance

Source DF S S MS F P Regression 3 90203 14 300677 1 53.57 0.000 Error 14 785797 56128 Total 17 9806111

Source DF Seq SS PaB(t-1) 1 8846065 Pk-Pk(1- 1 163789 Pk(t-1) 1 10459

Unusual Observations Obs P&(t-1) PC* Fit StDev Fit Residual St Resid

9 10650 11500.0 10861.4 80.4 638.6 2.87R

R denotes an observation wit11 a large sta~~dardized residual


Regression Analysis

The regression equation is PaC = 665 + 0.778 PaC(1-I) + 0.177 Pk-Pk(t-1) + 0.189 Pk(t-1)

18 cases used 1 cases contain nlissing values

Predictor Coef StDcv T P Constant 665.3 834.4 0.80 0.439 PaC(1-I) 0.7783 0.1719 4.53 0.000 Pk-Pk(t- 0.1774 0.1648 1.08 0.300 Pk(t-1) 0.1891 0.1599 1.18 0.256


Source DF SS MS F P Regression 3 1189 1907 3963969 5 1.80 0.000 Error 14 1071298 76521 Total 17 12963205

Source DF Seq SS PaC(t-1) 1 11754972 Pk-Pk(t- 1 2981 1 Pk(t-1) 1 107124

Unusual Observatio~ls Obs PaC(t-1) PaC Fit StDev Fit Residual St Resid 10 10071 11153.0 10251.1 116.9 901.9 3.60R

R de~lotes an observation with a large standardized residual


Regression Analysis

The regressiorl equation is PaD = 684 + 0.752 PaD(t-1) + 0.157 Pk-Pk(t-1) + 0.214 Pk(t-1)

1 8 cases used I cases contain ~llissillg values

Predictor Coef StDev T P Co~lsta~lt 684.2 743.1 0.92 0.373 PaD(t-I) 0.7520 0.13 14 5.72 0.000 Pk-Pk(1- 0.15729 0.09779 1.6 1 0.130 Pk(t-1) 0.21356 0.09466 2.26 0.041


Source DF SS MS F P Regression 3 810401 1 2701337 89.49 0.000 Error 14 422598 30186 Total 17 8526609

Sotirce DF Seq SS PaD(t-I) 1 7917065 Pk-Pk(t- 1 33305 Pk(t-I) 1 153641

U~~r~sua l Observatio~~s Obs PaD(t-I) PaD Fit StDev Fit Residual St Resid 10 10000 10583.0 10189.4 92.6 393.6 2.68R

R dc~~oles an observation with a large standardized residual


JERUK IMPOR

Regression A~ralysis

The regression equation is PjA = 1148 + 0.558 PjA(t-I) + 0.299 Pk-Pk(t-1) + 0.422 Pk(t-1)

I8 cases used 1 cases contain missing values

Predictor Coef StDev T P Constant 1148 1246 0.92 0.373 P A - 1 0.5583 0.2554 2.19 0.046 Pk-Pk(t- 0.2990 0.2390 1.25 0.231 Pk(t-1) 0.4222 0.2166 1.95 0.072


Source DF SS MS F P Pegression 3 17837203 5945734 26.91 0.000 Error 14 3093507 220965 Total 17 20930710

Sorircc DF Seq SS P.jA(t-1) 1 1698373 1 Pk-Pk(t- 1 13733 Pk(t-I) 1 839739

Unusual Observations Obs PjA(t-I) PjA Fit StDev Fit Residual St Resid 14 9000 10563 9341 224 1222 2.96R 17 10563 11925 11128 275 797 2.09R



Regression Analysis

Tlle regression equation is PjB = 985 + 0.744 PjB(t-I) + 0.012 Pk-Pk(t-1) + 0.225 Pk(t-1)

18 cases used 1 cases contain nlissing values

Predictor Coef StDev T P Colistar~t 984.6 957.1 1.03 0.32 1 P.jB(t-I) 0.7438 0.1348 5.52 0.000 Pk-Pk(t- 0.01 18 0.1552 0.08 0.941 Pk(t-1) 0.2254 0.1107 2.04 0.061

Analysis or Variance

Source DF S S MS F P Regression 3 13581482 4527161 33.34 0.000 Error 14 1900865 135776 Total 17 15482347

Source DF Seq SS PjB(t-I) 1 13013818 Pk-Pk(1- 1 5336 Pk(t-I) 1 562328

Umsual Observations Obs PjB(t-I) PjB Fit StDev Fil Residual St Resid

6 8667 10000.0 9205.8 209.4 794.2 2.62R

R denotes an observatiot~ wit11 a large standardized residual

Durbin-Watson slalistic = 2.18

Regression Analysis

T l ~ e regression equation is PjC = 257 + 0.786 PjC(t-I) + 0.338 Pk-Pk(t-I) + 0.247 Pk(t-I)

18 cases used I cases contain lnissing values

Predictor Coef StDev T P Co~istant 257.4 868.3 0.30 0.771 P.jC(t-I) 0.7859 0.1574 4.99 0.000 Pk-Pk(t- 0.3376 0.1704 1.98 0.068 Pk(1-I) 0.2469 0.1468 1 . 6 0.115

Aualysis of Variance


Source DF Seq SS P.jC(t-1) 1 19662772 Pk-Pk(t- 1 420909 Pk(t-1) 1 444136

Uiiusrlal Observations Obs PjC(t-1) PjC Fit StDev Fit Residual St. Resid 17 10156 11813.0 10797.4 219.0 1015.6 3.08R


Dorbin-Watson statistic = 2.13

Regression A~~nlys is

Tl~e regression equation is P.jD = - 191 + 0.782 PjD(t-I) + 0.063 Pk-Pk(t-1) + 0.315 Pk(t-1)

18 cases used I cases conlain ~nissing values

Predictor Coef StDev T P Constant -191 1038 -0.18 0.856 P.jD(t-1) 0.7823 0.2192 3.57 0.003 Pk-Pk(t- 0.0632 0.3044 0.21 0.838 Pk(t-I) 0.3148 0.2535 1.24 0.235


Source DF SS MS F P Regression 3 32366389 10788796 29.79 0.000 Error 14 5070525 362180 Total 17 37436914

Source DF Seq SS P.jD(t-1) 1 3 1714378 Pk-Pk(t- 1 93745 Pk(t-1) 1 558266

Unusual Observations Obs PjD(t-1) PjD Fit StDev Fit Residual St Resid 14 8500 10667 8904 254 1763 3.23R

R denotes an observation wit11 a large standardized residual


ANGGUR

Regression Analysis

The regression equation is PgA = - 2198 + 1.01 PgA(t-I) + 0.1 10 Pk-Pk(t-I) + 0.0865 Pk(t-I)


Predictor Coef StDev T P Constant -2198 2493 -0.88 0.393 PgA(t-I) 1.01249 0.08059 12.56 0.000 Pk-Pk(t- 0.10992 0.03272 3.36 0.005 Pk(t-1) 0.08647 0.04222 2.05 0.060


Sowce DF S S MS F P Regression 3 2778625 926208 58.57 0.000 Error 14 221375 15812 Total 17 3000000

Source DF Seq SS PgA(t-1) 1 2595339 Pk-Pk(t- 1 116964 Pk(t-1) 1 66322

Unusual Observations Obs PgA(t-1) PgA Fit StDev Fit Residual St Resid

6 23000 23500.0 23496.7 121.9 3.3 0.11 X 7 23500 23500.0 23488.4 121.2 11.6 0.35 X 1 0 23500 24000.0 23560.6 32.6 439.4 3.62R

R denotes an observation with a large standardized residual X denotes an observatio~i wllose X value gives it large influence.


Regression Analysis

The regression equation is PgC = - 1717 + 0.915 PgC(t-I) - 0.0353 Pk-Pk(t-I) + 0.167 Pk(t-I)

18 cases used I cases contain missing values

Predictor Coef StDev T P Corlstant -1717 4157 -0.41 0.686 PgC(t-I) 0.9151 0.1261 7.25 0.000 Pk-Pk(t- -0.0353 1 0.05 134 -0.69 0.503 Pk(t-1) 0.16656 0.07293 2.28 0.039



Source DF Seq SS PgC(t-I) 1 2251838 Pk-Pk(t- 1 47991 1 Pk(t-1) 1 208515

Unusual Observations Obs PgC(t-I) PgC Fit StDcv Fit Residual St Resid

0 23000 23000.0 22970.8 193.3 29.2 0.57 X 7 23000 24000.0 23919.0 9 2 1 8 1.0 1.46 X 13 24000 23500.0 23970.3 62.3 -470.3 -2.48R 15 23500 24000.0 23512.8 56.9 487.2 2.54R

R denotes an observation with a large standardized residual X denotes an observation wllose X value gives it large influence.


Regression Analysis

Tile regression equation is PgD = 5820 + 0.665 PgD(t-1) - 0.0103 Pk-Pk(t-I) + 0.0963 Pk(t-1)

18 cases used 1 cases contain ~tlissing values

Predictor Coef StDev T P Constant 5820 4069 1.43 0.175 PgD(t-1) 0.6648 0.1569 4.24 0.001 Pk-Pk(t- -0.0103 1 0.06818 -0.15 0.882 Pk(t-1) 0.09630 0.08938 1.08 0.299


Source DF SS MS F P Regression 3 1936166 645389 7.24 0.004 Error 14 1247862 89133 Total 17 3 184028

Source DF Seq SS PgD(t-I) 1 1723676 Pk-Pk(t- 1 109006 Pk(t-I) 1 103484

Unusual Observations Obs PgD(t-I) PgD Fit StDev Fit Residual St Resid

5 23000 24000.0 23363.1 142.5 636.9 2.43R 6 24000 24000.0 23919.7 289.5 80.3 1.10X 7 24000 24250.0 24388.6 287.4 -138.6 -1.72 X 10 24250 23500.0 24091.1 113.8 -59 .1 -2.14R

R denotes an observation with a large standardized residual X denotes an observation wllose X value gives it large influence.

Durbin-Watson statistic = 1 .88

APEL LOKAL

Regressioil Analysis

The regression equation is *

PalC = 575 + 0.807 PalC(1-I) + 0.1 IS Pk-Pk(t-I) + 0.123 Pk(t-I)

18 cases used I cases contain lnissing values

Predictor Coef StDev T P Constant 575.3 548.1 1.05 0.312 PalC(1-I 0.80705 0.09883 8.17 0.000 Pk-Pk(t- 0.1 1508 0.05886 1.96 0.071 Pk(t-I) 0.12261 0.04346 2.82 0.014


Source DF S S MS F P Regression 3 3248781 1082927 40.21 0.000 Error 14 377013 26929 Tola1 17 3625794

Source DF Seq SS PalC(1-1 1 2979317 Pk-Pk(L- 1 55076 Pk(t-I) 1 214389

Uuusual Observations Obs PalC(1-1 PalC Fit StDev Fit Residual St Resid 10 6417 6833.0 6537.3 72.8 295.7 2.01R 13 6750 6583.0 6562.2 146.6 20.8 0.28 X 19 6000 6333.0 5998.0 59.3 335.0 2.19R

R denotes an observatioli wit11 a large standardized residi~al X denotes an observatioll whose X value gives it large influence.

Durbin-Watson statislic = 1.97

Regression Analysis

The regression equation is PalD = 2157 + 0.432 PalD(t-1) + 0.144 Pk-Pk(t-I) + 0.260 Pk(t-1)

18 cases used 1 cases contain tnissit~g values

Predictor Coef StDev T P Constant 2157.3 732.3 2.95 0.01 1 PalD(t-1 0.4315 0.1677 2.57 0.022 Pk-Pk(t- 0.14351 0.05464 2.63 0.020 P i ( - ) 0.25977 0.07377 3.52 0.003



Source DF Seq SS PalD(1-I 1 2925508 Pk-Pk(t- 1 42884 Pk(t-I) 1 289450

Unnsual Observations 9bs PalD(t-1 PalD Fit StDev Fit Residual St Resid

6 6083 6417.0 6096.3 45.2 320.7 2.20R I3 6667 6500.0 0442.4 136.7 57.6 0.85 X 14 6500 6000.0 6062.5 124.7 -62.5 -0.71 X

R denotes an observatio~i with a large standardized residual X denotes an observatio~l wllose X value gives it large influence.

Dnrbiu-Watson statistic = 1.74

Saving workslieet in file: C:\A lena!\data\apellok-al.MTW * NOTE * Existing file replaced.

JERUK LOKAL

Regressio~i Analysis

The regression equation is PjlA = 812 + 0.839 PjlA(t-I) + 0.141 Pk-Pk(t-I) - 0.0229 Pk(t-I)

18 cases used 1 cases contain n~issing values

Predictor Coef StDev T P Constant 812.5 723.9 1.12 0.281 P-jlA(t-1 0.8389 0.1771 4.74 0.000 Pk-Pk(t- 0.1407 0.1058 1.33 0.205 Pk(t-I) -0.02290 0.09300 -0.25 0.809


Source DF S S MS F P Regression 3 351612 117204 10.74 0.001 krror 14 152729 10909 Total 17 504340

Source DF Seq SS P.jlA(I-1 1 3 17924 Pk-Pk(t- 1 33027 Pk(t-I) 1 66 1

Ul~l~sual Observations Obs PjlA(t-1 P-jlA Fit StDev Fit Residual St Resid 13 4875 4500.0 4825.7 39.2 -325.7 -3.36R



Tlle regression equation is P-jlB = 1303 + 0.703 PjlB(t-I) + 0.138 Pk-Pk(t-I) + 0.0135 Pk(t-I)

1 8 cases used I cases conlaill lnissi~lg values

Predictor Coer SlDev T P Constanl 1302.8 809.9 1.61 0.130 PjlB(t-1 0.7034 0.1933 3.64 0.003 Pk-Pk(t- 0.1377 0.1074 1.28 0.221 Pk(t-I) 0.01349 0.09040 0.15 0.883


Source DF S S MS F P Regression 3 21 1787 70596 5.95 0.008 Error 14 166090 11864 Total 17 377877

Source DF Seq SS P.jlB(t-1 1 188404 Pk-Pk(t- 1 23 119 Pk(t-1) 1 264

U~lusual Observations Obs PjlB(1- I P-jlB Fit StDev Fit Residual St Resid 10 4833 4500.0 4713.0 64.3 -213.0 -2.42R 12 4500 4333.0 4547.9 39.7 -214.9 -2.12R



Regressio~r Analysis

Tllc regression equation is I'jlC = 487 + 0.874 PjlC(t-I) - 0.0465 Pk-Pk(1-I) + 0.0247 Pk(1-I)

18 cases used 1 cases contain rtlissing values

Predictor Coef StDev T P Constant 487.4 500.0 0.97 0.346 P-jlC(t-1 0.8745 0.1163 7.52 0.000 Pk-Pk(t- -0.04654 0.05812 -0.80 0.437 Pk(t-1) 0.02475 0.04642 0.53 0.602


Source DF S S MS F P Regression 3 23 1120 77040 21.52 0.000 Error 14 50130 3581 Total 17 281250

Source DF Seq SS P.ilC(t- 1 1 225000 Pk-Pk(t- 1 5 103 Pk(t-I) 1 1018

Unusual Observations Obs PjlC(t-l PjlC Fit StDev Fit Residual St Resid 11 4250 4500.0 4299.5 26.6 200.5 3.74R



Regression A~~alys is

Tlie regression equation is P-jlD = 3246 + 0.101 PjlD(1-I) + 0.141 Pk-Pk(t-I) + 0.155 Pk(t-I)


Predictor Coef StDev T P Constant 3246 1359 2.39 0.032 P.jlD(t-1 0.1006 0.3963 0.25 0.803 Pk-Pk(t- 0.14056 0.07573 1.86 0.085 Pk(t-1) 0.1553 0.1015 1.53 0.148



Source DF Seq SS FjlD(t-1 1 48563 Pk-Pk(t- 1 3 114 Pk(t-1) 1 655'9

Unusual Observations Obs P.jlD(t-1 PjlD Fit StDev Fit Residual St Resid

5 4000 4167.0 4037.1 25.9 129.9 2.81R

R denotes an observatiou with a large standardized residual


Saving workslieet in file: C:\A lena!\data\jeruklok-a.MTW * NOTE * Existing file replaced.



Source DF Seq SS PsB(t-1) 1 959534 Pk-Pk(t- 1 24451 Pk(t-I) 1 28018

Unusual Observations Obs PsB(t-1) PsB Fit StDev Fit Residual St Resid

6 5500 5800.0 5602.0 44.1 198.0 2.67R 15 5900 6200.0 6067.2 58.4 132.8 2.09R


Diirbin-Watson statistic = 1.94


Predictor Coef StDev T P Constant 1893.4 683.2 2.77 0.015 PC([-1) 0.6377 0.129 1 4.94 0.000 Pk-Pk(t- 0.0 1964 0.02500 0.79 0.445 Pk(t-I) 0.08179 0.03266 2.50 0.025



Source DF Seq SS PsC(t-I) 1 193 130 Pk-Pk(l- I 520 Pk(t-I) 1 7766

SALAK

Regression Analysis

The regression equation is PsA = 702 + 0.853 PsA(t-1) + 0.0917 Pk-Pk(t-I) f 0.0492 Pk(t-I)

18 cases used I cases contain tnissing values

Predictor Coef StDcv T P Consla~il 702 1157 0.61 0.554 PsA(t-I) 0.8530 0.2075 4.11 0.001 Pk-Pk(t- 0.09 175 0.08162 1.12 0.280 Pk(t-I) 0.04922 0.07141 0.69 0.502



Source DF Seq SS PsA(t-I) 1 390625 Pk-Pk(t- 1 13 136 I

Pk(t-I) 1 7260

Unnsual Observations Obs PsA(t-I) PsA Fit StDev Fit Residual St Resid 17 6000 6500.0 6072.0 46.9 428.0 3.74R



Regression Analysis

The regression equalion is PsB = 1432 + 0.621 PsB(t-I) + 0.166 Pk-Pk(t-1) + 0.190 Pk(t-I)

18 cases used 1 cases contain lnissing values

Predictor Coef StDev T P Constant 1432.1 690.1 2.08 0.057 PsB(t-I) 0.6208 0.1772 3.50 0.004 Pk-Pk(t- 0.16631 0.06384 2.61 0.021 Pk(t-1) 0.18955 0.09765 1.94 0.073,

U~iusttal Observations Obs PsC(1-I) PsC Fit StDev Fil Residual St Resid

7 6000 6125.00 6056.83 19.04 68.17 2.30R ,

9 6125 6250.00 6179.49 18.43 70.51 2.35R


Durbin-Watson statislic = 2.16

Regression Analysis

Tlie regression equalion is PsD = 2080 + 0.605 PsD(t-1) + 0.0215 Pk-Pk(t-I) + 0.0575 Pk(t-1)

18 cases used I cases contain tnissing values

Predictor Coef StDev T P Constant 2080 1 145 1.82 0.091 PsD(1-I) 0.6054 0.2079 2.91 0.01 1 Pk-Pk(t- 0.02152 0.03583 0.60 0.558 Pk(t-I) 0.05749 0.03430 1.68 0.116

A~ialysis of Variance

Source DF S S MS F P .Regression 3 72756 24252 8.12 0.002 Error 14 41788 2985 Total 17 114544

Sot~rce DF Seq SS PsD(t-1) 1 64357 Pk-Pk(t- I 15 Pk(t-1) 1 8384

Unusual Observations Obs PsD(1-1) PsD Fit StDev Fit Residual St Resid 10 5917 5750.0 5909.9 16.0 -159.9 -3.06R



Saving worksheet in file: C:\A letia!\data\sal,?k-a1.MTW * NOTE * Existing file replaced.

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