Nothing
R/database.R
In ddp: Desirable Dietary Pattern
standar <- data.frame(
Berat = c(1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 100, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 60, 50, 70, 20, 40, 71, 800, 200, 397,
1000, 1000, 100, 100, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 100, 500,
600, 1000, 1000, 100, 100, 1000, 100, 100, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 100, 1000, 1000,
1000, 100, 100, 100, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 800, 800, 800, 250,
100, 800, 100, 100, 100, 2, 100, 20, 150, 100,
644, 100, 1, 1, 1, 1, 1, 1, 100, 1,
100, 100, 1, 1, 80, 100, 100, 100, 100, 100,
7, 150, 100, 150, 30, 100, 30, 40, 200, 250,
500, 250, 200, 350, 250, 250, 25, 250, 250, 100,
75, 150, 100, 125, 125, 30, 520, 1000, 259, 208,
208, 104, 208, 50, 200, 800, 644.8),
Energi = c(3622, 3605, 361.2, 3200, 3640, 3550, 3330, 3520, 1309, 1252,
3380, 1135, 520, 3380, 3630, 3620, 1794, 872, 904, 904,
480, 824, 740, 1032, 477, 712, 688, 477, 736, 1200,
477, 960, 712, 904, 618, 750, 679, 1010,552.2, 140.4,
135.1, 138.6, 230.5, 145.5, 216.8, 296, 233.6, 338, 305, 265.5,
265.5, 357, 2070, 840, 1540, 4165, 3020, 3020, 3260, 2040,
2060, 4330, 212, 2410, 3548.44, 4520, 1360, 1213.3, 1280, 1280,
905, 82.24, 68.9, 125.4, 25.9, 51.8, 114.9, 488, 122, 1333.9,
5090, 1672, 326, 52, 113.6, 168, 180, 66, 191.4, 306,
276, 190, 288, 68.7, 635.1, 373.1, 340, 191.7, 33, 67.5,
116, 408, 197.6, 71.5, 51.1, 1260, 35.1, 83.6, 264, 189,
876, 0, 285, 4520, 5250, 3810, 3373.3, 606, 2723.6, 800,
1430, 166, 187, 290.8, 311.3, 365.3, 484.5, 518.5, 276, 403.2,
294.8, 1350.6, 204, 644, 1131.1, 345, 441.8, 665.5, 309.6, 237.8,
128.8, 128.8, 296.8, 240, 450, 587, 6960, 6204, 7216, 1335.5,
720, 7362, 364, 377, 132, 2.64, 352, 90, 645, 298,
1056.5, 0, 0, 6.36, 4.04, 3.59, 1.32, 2.5, 36.79, 0,
78.43, 78.43, 0, 0.49, 356, 860, 360, 350, 453, 460,
1.47, 277.1, 345, 248.5, 161.5, 426.3, 137.5, 181, 109, 290,
583.6, 552, 391.6, 263.8, 143.7, 232.5, 89.5, 529, 356, 509.1,
624, 490, 340, 203.75, 203.75, 246.3, 0, 0, 76.25, 57.6,
240, 80, 61, 207, 56, 384, 0),
Protein = c(84.75, 77, 11.48, 82.8, 70, 92, 90, 73, 8.5, 11.78, 6,
15.5, 17.64, 15, 11, 5, 13, 136, 136, 136, 90.24, 176,
103, 160, 76.88, 149.6, 128, 76.88, 160, 165, 76.88, 190, 149.6,
136, 142.8, 161, 62.1, 144, 108.6, 25.2, 29.4, 25.55, 48.65, 28.5,
28.5, 17.1, 46.4, 21.1, 43.62, 56.16, 56.16, 41.1, 188, 187, 166,
130, 182, 182, 160, 97.2, 171, 550, 18, 160, 161.74, 145,
197, 149.83, 155.3, 155.3, 178.5, 6.62, 4.52, 7.76, 2.16, 4.32, 8.01,
25.6, 6.4, 32.55, 246, 76, 22.8, 3.3, 6.39, 20.4, 10.5, 6.3,
20.01, 21.6, 27.6, 9.5, 8, 3.22, 59.16, 15.32, 37, 6.08, 2.2,
3.25, 2.8, 16, 15.96, 9.9, 3.74, 56.7, 1.35, 3.96, 8.5, 5.7,
40, 0, 24.96, 253, 279, 404, 202.67, 19.5, 201.31, 109, 120,
10.4, 13, 16.15, 5.29, 3.64, 4.25, 5.49, 3.6, 6.4, 5.5, 4.68,
3.06, 7, 10.06, 3.75, 5.78, 6.94, 3.44, 5.8, 2.3, 2.3, 3.36,
13, 5.6, 10, 8, 19.52, 0, 12.65, 0.6, 13.5, 0, 3,
19.5, 0.39, 17.4, 1, 16.52, 8, 0, 0, 0, 0.19, 0.14,
0.12, 0.01, 0.23, 4.56, 0, 1.6, 1.6, 0, 0.02, 8, 6,
4.7, 8.3, 3.88, 11.02, 0.01, 9.96, 8.5, 7.95, 2.45, 6.19, 1.96,
4.94, 8.7, 14, 19.42, 6.4, 4.62, 5.93, 8.92, 9.62, 11.25, 6.82,
8, 6.28, 70.35, 66.2, 11.05, 7.43, 7.43, 8.9, 0, 0, 0,
0, 0, 0, 3.2, 4, 0, 4.81, 0),
Lemak = c(14.5, 15.5, 3.64, 39, 5, 39, 10, 9, 2.55, 3.26, 3,
3.78, 1.68, 7, 5, 3, 1.75, 32, 36, 36, 10.56, 8,
14, 38.4, 6.2, 8, 16, 6.2, 5.6, 53, 6.2, 17, 8,
36, 1.36, 7, 17.1, 26, 8.3, 3.6, 1.05, 1.54, 2.52, 2.62,
10.5, 20.3, 3.2, 27, 8.9, 2.07, 2.07, 10, 140, 5, 92,
400, 250, 250, 286, 180.3, 148, 90, 10.6, 250, 294.14, 423,
32, 60, 65, 65, 15, 5.77, 5.32, 10.18, 1.84, 3.67, 8.01,
28, 7, 39.7, 300, 36, 20.3, 2.5, 2.84, 4.2, 1.5, 1.5,
2.61, 2.7, 4.6, 2.9, 4.8, 1.21, 10.44, 5.88, 12, 1.58, 0.1,
5, 2.4, 3.2, 0.76, 0.75, 0.72, 0.9, 0.27, 0.18, 2.6, 2.5,
20.4, 0, 5.9, 428, 427, 167, 18, 47.3, 40.06, 47, 55,
4.9, 6, 10.45, 1.6, 1.3, 3.4, 39.65, 0.4, 1.28, 6.6, 1.64,
1.53, 2.1, 6.03, 0, 2.92, 19.47, 3.44, 0.58, 0.92, 0.92, 0.84,
5, 3.5, 17.97, 784, 372, 88, 130, 81, 810, 0, 10,
0.7, 0.01, 1.3, 15, 12, 23.8, 0, 0, 0, 0.63, 0.16,
0.07, 0.01, 0.02, 1.04, 0, 0.32, 0.32, 0, 0.02, 13.6, 33,
1, 6.1, 17.85, 20.4, 0.01, 5.1, 2.2, 1.35, 5.75, 13.02, 2.91,
10.35, 0.5, 10.9, 9.07, 6.4, 0.22, 5.2, 7.96, 8.02, 1.75, 22.1,
13.6, 23.32, 35.85, 22.8, 3.55, 6.58, 6.58, 11.29, 0, 0, 0,
0, 0, 0, 3.5, 12.5, 0, 0, 0),
Karbohidrat = c(775.5, 764.5, 84.84, 663.3, 800, 737,772, 762, 312.8, 293.68, 831,
257.31, 113.4, 813, 882, 869, 432.27, 0, 0, 0, 0, 0,
41, 0, 22.94, 0, 0, 22.94, 0, 4, 22.94, 0, 0,
0, 0.68, 1, 63.5, 39, 8.1, 0, 0, 3.85, 0.45, 0,
0, 11.3, 0, 0, 10.86, 1.6, 1.6, 25.6, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 59.3, 0, 245.25, 23,
60, 8.33, 8, 8, 3.5, 0.37, 0.28, 0, 0, 0, 0.8,
34.4, 8.6, 218.35, 362, 262, 13.1, 4, 20.59, 23.4, 39.75, 10.8,
34.8, 64.8, 47.8, 39.9, 63.2, 13.67, 113.1, 71.44, 43, 45.61, 7.4,
2.5, 20, 90.4, 37.24, 32.75, 7.92, 259.2, 0.18, 20.33, 62.1, 42.6,
169.22, 0, 48.82, 211, 174, 249, 640.33, 34.9, 411.33, 8, 114,
24.1, 22.6, 43.2, 78.3,95.03, 108.8, 46.97,72.4, 103.04, 61.6, 329.62,
50.49, 168, 264.82, 91.5, 110.9, 115.24, 75.68,59.74,31.74,31.74,77.28,
47, 114, 125.88, 0, 0, 0, 52.1, 0.4, 0, 94, 85.5,
67.8, 1.36, 69, 17, 112.5, 48.9, 272.8, 0, 0, 0.08, 0.54,
0.64, 0.31, 0.35, 7.2, 0, 19.6, 19.6, 0, 0.08, 48, 140,
82.1, 64.35, 69.15, 61.72, 0.34, 47.84, 78, 49.85, 25.05, 72.24, 25.91,
18.02, 18.3, 34, 103.62, 60.4, 89.32, 47, 9.11, 30.04,6.65,70.8,
48, 60.4, 0, 0.6, 1.1, 28.06, 28.06, 24.2, 0, 0, 0,
16, 64, 0, 4.3, 20.6, 14.1, 35.19, 0),
Kategori = c(1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,
3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,
3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,8,8,8,8,8,8,8,8,8,
8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,6,6,6,5,6,6,6,6,6,6,
8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,4,4,5,4,4,7,7,9,
9,9,9,9,9,7,7,9,5,9,9,9,9,9,9,9,9,9,9,1,1,1,1,2,5,9,1,1,1,1,1,
1,1,6,8,1,1,1,1,3,8,3,1,1,1,3,3,3,1,1,1,9,9,9,9,9,9,9,3,9,9,9)
)
bobot <- data.frame(
padi = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5),
umbi = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5),
hewani = c(2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
2,2,2,2),
minyak = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5),
biji = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5),
kacang = c(2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
2,2,2,2),
gula = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5),
sayurbuah = c(5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,
5,5,5,5,5),
lain = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0)
)
maksimal <- data.frame(
padi = c(25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,
25,25,25,25,25,25,25,25,25,25,22,22,22,22),
umbi = c(2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,
2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,6,6,
6,6),
hewani = c(24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,
24,24,24,24,24,24,24,24,24,24,24,24,24,24),
minyak = c(5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,
5,5,5),
biji = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1),
kacang = c(10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,
10,10,10,10,10,10,10,10,10,10,10,10,10,10),
gula = c(2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,
2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2,2,
2,2),
sayurbuah = c(30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,
30,30,30,30,30,30,30,30,30,30,30,30,30,30,30),
lain = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0)
)
rownames(bobot) <- rownames(maksimal) <- c(
"Indonesia","Aceh","Sumut","Sumbar","Riau","KepRiau","Jambi","Sumsel",
"Babel","Bengkulu","Lampung","Jakarta","Jabar","Banten","Jateng","DIY",
"Jatim","Bali","NTB","NTT","Kalbar","Kalteng","Kalsel","Kaltim","Kalut",
"Sulut","Sulteng","Sultra","Sulsel","Gorontalo","Sulbar","Maluku",
"Malut","Papua","Papbar"
)
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ddp documentation built on May 8, 2021, 5:06 p.m.
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standar <- data.frame(
Berat = c(1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 100, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 60, 50, 70, 20, 40, 71, 800, 200, 397,
1000, 1000, 100, 100, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 100, 500,
600, 1000, 1000, 100, 100, 1000, 100, 100, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 100, 1000, 1000,
1000, 100, 100, 100, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 800, 800, 800, 250,
100, 800, 100, 100, 100, 2, 100, 20, 150, 100,
644, 100, 1, 1, 1, 1, 1, 1, 100, 1,
100, 100, 1, 1, 80, 100, 100, 100, 100, 100,
7, 150, 100, 150, 30, 100, 30, 40, 200, 250,
500, 250, 200, 350, 250, 250, 25, 250, 250, 100,
75, 150, 100, 125, 125, 30, 520, 1000, 259, 208,
208, 104, 208, 50, 200, 800, 644.8),
Energi = c(3622, 3605, 361.2, 3200, 3640, 3550, 3330, 3520, 1309, 1252,
3380, 1135, 520, 3380, 3630, 3620, 1794, 872, 904, 904,
480, 824, 740, 1032, 477, 712, 688, 477, 736, 1200,
477, 960, 712, 904, 618, 750, 679, 1010,552.2, 140.4,
135.1, 138.6, 230.5, 145.5, 216.8, 296, 233.6, 338, 305, 265.5,
265.5, 357, 2070, 840, 1540, 4165, 3020, 3020, 3260, 2040,
2060, 4330, 212, 2410, 3548.44, 4520, 1360, 1213.3, 1280, 1280,
905, 82.24, 68.9, 125.4, 25.9, 51.8, 114.9, 488, 122, 1333.9,
5090, 1672, 326, 52, 113.6, 168, 180, 66, 191.4, 306,
276, 190, 288, 68.7, 635.1, 373.1, 340, 191.7, 33, 67.5,
116, 408, 197.6, 71.5, 51.1, 1260, 35.1, 83.6, 264, 189,
876, 0, 285, 4520, 5250, 3810, 3373.3, 606, 2723.6, 800,
1430, 166, 187, 290.8, 311.3, 365.3, 484.5, 518.5, 276, 403.2,
294.8, 1350.6, 204, 644, 1131.1, 345, 441.8, 665.5, 309.6, 237.8,
128.8, 128.8, 296.8, 240, 450, 587, 6960, 6204, 7216, 1335.5,
720, 7362, 364, 377, 132, 2.64, 352, 90, 645, 298,
1056.5, 0, 0, 6.36, 4.04, 3.59, 1.32, 2.5, 36.79, 0,
78.43, 78.43, 0, 0.49, 356, 860, 360, 350, 453, 460,
1.47, 277.1, 345, 248.5, 161.5, 426.3, 137.5, 181, 109, 290,
583.6, 552, 391.6, 263.8, 143.7, 232.5, 89.5, 529, 356, 509.1,
624, 490, 340, 203.75, 203.75, 246.3, 0, 0, 76.25, 57.6,
240, 80, 61, 207, 56, 384, 0),
Protein = c(84.75, 77, 11.48, 82.8, 70, 92, 90, 73, 8.5, 11.78, 6,
15.5, 17.64, 15, 11, 5, 13, 136, 136, 136, 90.24, 176,
103, 160, 76.88, 149.6, 128, 76.88, 160, 165, 76.88, 190, 149.6,
136, 142.8, 161, 62.1, 144, 108.6, 25.2, 29.4, 25.55, 48.65, 28.5,
28.5, 17.1, 46.4, 21.1, 43.62, 56.16, 56.16, 41.1, 188, 187, 166,
130, 182, 182, 160, 97.2, 171, 550, 18, 160, 161.74, 145,
197, 149.83, 155.3, 155.3, 178.5, 6.62, 4.52, 7.76, 2.16, 4.32, 8.01,
25.6, 6.4, 32.55, 246, 76, 22.8, 3.3, 6.39, 20.4, 10.5, 6.3,
20.01, 21.6, 27.6, 9.5, 8, 3.22, 59.16, 15.32, 37, 6.08, 2.2,
3.25, 2.8, 16, 15.96, 9.9, 3.74, 56.7, 1.35, 3.96, 8.5, 5.7,
40, 0, 24.96, 253, 279, 404, 202.67, 19.5, 201.31, 109, 120,
10.4, 13, 16.15, 5.29, 3.64, 4.25, 5.49, 3.6, 6.4, 5.5, 4.68,
3.06, 7, 10.06, 3.75, 5.78, 6.94, 3.44, 5.8, 2.3, 2.3, 3.36,
13, 5.6, 10, 8, 19.52, 0, 12.65, 0.6, 13.5, 0, 3,
19.5, 0.39, 17.4, 1, 16.52, 8, 0, 0, 0, 0.19, 0.14,
0.12, 0.01, 0.23, 4.56, 0, 1.6, 1.6, 0, 0.02, 8, 6,
4.7, 8.3, 3.88, 11.02, 0.01, 9.96, 8.5, 7.95, 2.45, 6.19, 1.96,
4.94, 8.7, 14, 19.42, 6.4, 4.62, 5.93, 8.92, 9.62, 11.25, 6.82,
8, 6.28, 70.35, 66.2, 11.05, 7.43, 7.43, 8.9, 0, 0, 0,
0, 0, 0, 3.2, 4, 0, 4.81, 0),
Lemak = c(14.5, 15.5, 3.64, 39, 5, 39, 10, 9, 2.55, 3.26, 3,
3.78, 1.68, 7, 5, 3, 1.75, 32, 36, 36, 10.56, 8,
14, 38.4, 6.2, 8, 16, 6.2, 5.6, 53, 6.2, 17, 8,
36, 1.36, 7, 17.1, 26, 8.3, 3.6, 1.05, 1.54, 2.52, 2.62,
10.5, 20.3, 3.2, 27, 8.9, 2.07, 2.07, 10, 140, 5, 92,
400, 250, 250, 286, 180.3, 148, 90, 10.6, 250, 294.14, 423,
32, 60, 65, 65, 15, 5.77, 5.32, 10.18, 1.84, 3.67, 8.01,
28, 7, 39.7, 300, 36, 20.3, 2.5, 2.84, 4.2, 1.5, 1.5,
2.61, 2.7, 4.6, 2.9, 4.8, 1.21, 10.44, 5.88, 12, 1.58, 0.1,
5, 2.4, 3.2, 0.76, 0.75, 0.72, 0.9, 0.27, 0.18, 2.6, 2.5,
20.4, 0, 5.9, 428, 427, 167, 18, 47.3, 40.06, 47, 55,
4.9, 6, 10.45, 1.6, 1.3, 3.4, 39.65, 0.4, 1.28, 6.6, 1.64,
1.53, 2.1, 6.03, 0, 2.92, 19.47, 3.44, 0.58, 0.92, 0.92, 0.84,
5, 3.5, 17.97, 784, 372, 88, 130, 81, 810, 0, 10,
0.7, 0.01, 1.3, 15, 12, 23.8, 0, 0, 0, 0.63, 0.16,
0.07, 0.01, 0.02, 1.04, 0, 0.32, 0.32, 0, 0.02, 13.6, 33,
1, 6.1, 17.85, 20.4, 0.01, 5.1, 2.2, 1.35, 5.75, 13.02, 2.91,
10.35, 0.5, 10.9, 9.07, 6.4, 0.22, 5.2, 7.96, 8.02, 1.75, 22.1,
13.6, 23.32, 35.85, 22.8, 3.55, 6.58, 6.58, 11.29, 0, 0, 0,
0, 0, 0, 3.5, 12.5, 0, 0, 0),
Karbohidrat = c(775.5, 764.5, 84.84, 663.3, 800, 737,772, 762, 312.8, 293.68, 831,
257.31, 113.4, 813, 882, 869, 432.27, 0, 0, 0, 0, 0,
41, 0, 22.94, 0, 0, 22.94, 0, 4, 22.94, 0, 0,
0, 0.68, 1, 63.5, 39, 8.1, 0, 0, 3.85, 0.45, 0,
0, 11.3, 0, 0, 10.86, 1.6, 1.6, 25.6, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 59.3, 0, 245.25, 23,
60, 8.33, 8, 8, 3.5, 0.37, 0.28, 0, 0, 0, 0.8,
34.4, 8.6, 218.35, 362, 262, 13.1, 4, 20.59, 23.4, 39.75, 10.8,
34.8, 64.8, 47.8, 39.9, 63.2, 13.67, 113.1, 71.44, 43, 45.61, 7.4,
2.5, 20, 90.4, 37.24, 32.75, 7.92, 259.2, 0.18, 20.33, 62.1, 42.6,
169.22, 0, 48.82, 211, 174, 249, 640.33, 34.9, 411.33, 8, 114,
24.1, 22.6, 43.2, 78.3,95.03, 108.8, 46.97,72.4, 103.04, 61.6, 329.62,
50.49, 168, 264.82, 91.5, 110.9, 115.24, 75.68,59.74,31.74,31.74,77.28,
47, 114, 125.88, 0, 0, 0, 52.1, 0.4, 0, 94, 85.5,
67.8, 1.36, 69, 17, 112.5, 48.9, 272.8, 0, 0, 0.08, 0.54,
0.64, 0.31, 0.35, 7.2, 0, 19.6, 19.6, 0, 0.08, 48, 140,
82.1, 64.35, 69.15, 61.72, 0.34, 47.84, 78, 49.85, 25.05, 72.24, 25.91,
18.02, 18.3, 34, 103.62, 60.4, 89.32, 47, 9.11, 30.04,6.65,70.8,
48, 60.4, 0, 0.6, 1.1, 28.06, 28.06, 24.2, 0, 0, 0,
16, 64, 0, 4.3, 20.6, 14.1, 35.19, 0),
Kategori = c(1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,
3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,
3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,8,8,8,8,8,8,8,8,8,
8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,6,6,6,6,5,6,6,6,6,6,6,
8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,4,4,4,5,4,4,7,7,9,
9,9,9,9,9,7,7,9,5,9,9,9,9,9,9,9,9,9,9,1,1,1,1,2,5,9,1,1,1,1,1,
1,1,6,8,1,1,1,1,3,8,3,1,1,1,3,3,3,1,1,1,9,9,9,9,9,9,9,3,9,9,9)
)
bobot <- data.frame(
padi = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5),
umbi = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5),
hewani = c(2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
2,2,2,2),
minyak = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5),
biji = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5),
kacang = c(2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
2,2,2,2),
gula = c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,
0.5,0.5,0.5),
sayurbuah = c(5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,
5,5,5,5,5),
lain = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0)
)
maksimal <- data.frame(
padi = c(25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,25,
25,25,25,25,25,25,25,25,25,25,22,22,22,22),
umbi = c(2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,
2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,6,6,
6,6),
hewani = c(24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,
24,24,24,24,24,24,24,24,24,24,24,24,24,24),
minyak = c(5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,
5,5,5),
biji = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1),
kacang = c(10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,
10,10,10,10,10,10,10,10,10,10,10,10,10,10),
gula = c(2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,
2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2,2,
2,2),
sayurbuah = c(30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,
30,30,30,30,30,30,30,30,30,30,30,30,30,30,30),
lain = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0)
)
rownames(bobot) <- rownames(maksimal) <- c(
"Indonesia","Aceh","Sumut","Sumbar","Riau","KepRiau","Jambi","Sumsel",
"Babel","Bengkulu","Lampung","Jakarta","Jabar","Banten","Jateng","DIY",
"Jatim","Bali","NTB","NTT","Kalbar","Kalteng","Kalsel","Kaltim","Kalut",
"Sulut","Sulteng","Sultra","Sulsel","Gorontalo","Sulbar","Maluku",
"Malut","Papua","Papbar"
)
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