LAMPIRAN
xx
Lampiran 1 Data Indeks Harga Konsumen dan Inflasi Kota Jayapura, Sorong dan Manokwari Bulan Januari 2009 – Mei 2013 Dari BPS (sumber : www.bps.go.id) BULAN
Jayapura (JPR)
IHK Sorong (SRG)
Manokwari (MAN)
INFLASI Jayapura
Sorong
Manokwari
Jan-09
113,86
130,27
127,41
-1,27
-0,14
3,84
Feb-09
113,36
130,36
127,44
-0,44
0,07
0,02
Mar-09
115,25
131,46
127,02
1,67
0,84
-0,33
Apr-09
115,21
130,44
127,11
-0,03
-0,78
0,07
Mei-09
113,7
130,51
127,39
-1,31
0,05
0,22
Jun-09
114,84
132,14
127,48
1
1,25
0,07
Jul-09
114,2
135,04
127,71
-0,56
2,19
0,18
Agust-09
115,13
134,01
130,26
0,81
-0,08
2
Sep-09
116,62
132,7
130,53
1,29
-0,98
0,21
Okt-09
115,87
133,3
129,84
-0,64
0,45
-0,53
Nov 09
117,17
133,37
129,96
1,12
0,05
0,09
Des-09
117,53
133,85
131,93
0,31
0,36
1,52
Jan-10
119,03
134,01
132,15
1,28
0,12
0,17
Feb-10
118,41
135,23
131,33
-0,52
0,91
-0,62
Mar-10
119,07
135,65
131,35
0,56
0,31
0,02
Apr-10
118,46
136,99
134,03
-0,51
0,99
2,04
Mei-10
119,3
137,95
131,87
0,71
0,7
-1,61
Jun-10
120,3
138,14
133,43
0,84
0,14
1,18
Jul-10
120,59
142,12
135,97
0,24
2,88
1,9
Agust-10
121,22
144,41
136,58
0,52
1,61
0,45
Sep-10
121,94
145,74
135,95
0,59
0,92
-0,46
Okt-10
120,09
145,67
134,79
-1,52
-0,05
-0,85
Nop-10
120,54
146,64
134,45
0,37
0,67
-0,25
Des-10
122,8
144,73
138,1
1,87
-1,3
2,71
Jan-11
125
143,18
138,19
1,79
-1,07
0,07
Feb-11
124,01
143,07
138,05
-0,79
-0,08
-0,1
Mar-11
123,97
142,6
136,64
-0,03
-0,33
-1,02
Apr-11
123,67
141,65
136,56
-0,24
-0,67
-0,06
Mei-11
124,29
141,79
137,01
0,5
0,1
0,33
Jun-11
125,03
145,12
138,51
0,6
2,35
1,09
Jul-11
125,3
145,37
142,05
0,22
0,17
2,56
Agust-11
126,73
145,49
143,86
1,14
0,08
1,27
Sep-11
125,38
145,36
141,95
-1,07
-0,09
-1,33
Okt-11
125,41
144,42
141,39
0,02
-0,65
-0,39
Nop-11
126,52
144,31
141,11
0,89
-0,08
-0,2
Des-11
126,97
146,03
143,12
0,36
1,19
1,42
Jan-12
127,05
145,47
142,67
0,06
-0,38
-0,31
xxi
Jayapura (JPR)
IHK Sorong (SRG)
Manokwari (MAN)
Jayapura
Sorong
Manokwari
Feb-12
128,23
144,92
141,6
0,93
-0,38
-0,75
Mar-12
126,38
145,05
141,35
-1,44
0,09
-0,18
Apr-12
127,27
147,4
142,78
0,7
1,62
1,01
Mei-12
126,07
148,24
143,56
-0,94
0,57
0,55
Jun-12
127,28
150,48
146,51
0,96
1,51
2,05
Jul-12
128,08
152,28
148,26
0,63
1,2
1,19
Agust-12
128,91
154,46
148,93
0,65
1,43
0,45
Sep-12
129,07
154,05
147,31
0,12
-0,27
-1,09
Okt-12
129,26
153,39
148,74
0,15
-0,43
0,97
Nop-12
129,39
152,57
147,31
0,1
-0,53
-0,96
Des-12
132,71
153,5
150,1
2,57
0,61
1,89
Jan-13
133,24
152
148,97
0,4
-0,98
-0,75
BULAN
INFLASI
Feb-13
137,44
153,65
149,81
3,15
1,09
0,56
Mar-13
133,82
156,31
151,4
-2,63
1,73
1,06
Apr-13
133,02
157,07
151,99
-0,6
0,49
0,39
Mei-13
134,31
157,53
152,73
0,97
0,29
0,49
xxii
Lampiran 2 Data Indeks Harga Konsumen dan Perubahan Indeks Harga Konsumen Kota-Kota di Papua ∆JPR (dJPR)
∆SRG (dSRG)
∆MAN (dMAN)
JPR 𝑡−1 (JPRt)
SRG𝑡−1 (SRGt)
MAN𝑡−1 (MANt)
-0,5
0,09
0,03
113,86
130,27
127,41
1,89
1,1
-0,42
113,36
130,36
127,44
-0,04
-1,02
0,09
115,25
131,46
127,02
-1,51
0,07
0,28
115,21
130,44
127,11
1,14
1,63
0,09
113,7
130,51
127,39
-0,64
2,9
0,23
114,84
132,14
127,48
0,93
-1,03
2,55
114,2
135,04
127,71
1,49
-1,31
0,27
115,13
134,01
130,26
-0,75
0,6
-0,69
116,62
132,7
130,53
1,3
0,07
0,12
115,87
133,3
129,84
0,36
0,48
1,97
117,17
133,37
129,96
1,5
0,16
0,22
117,53
133,85
131,93
-0,62
1,22
-0,82
119,03
134,01
132,15
0,66
0,42
0,02
118,41
135,23
131,33
-0,61
1,34
2,68
119,07
135,65
131,35
0,84
0,96
-2,16
118,46
136,99
134,03
1
0,19
1,56
119,3
137,95
131,87
0,29
3,98
2,54
120,3
138,14
133,43
0,63
2,29
0,61
120,59
142,12
135,97
0,72
1,33
-0,63
121,22
144,41
136,58
-1,85
-0,07
-1,16
121,94
145,74
135,95
0,45
0,97
-0,34
120,09
145,67
134,79
2,26
-1,91
3,65
120,54
146,64
134,45
2,2
-1,55
0,09
122,8
144,73
138,1
-0,99
-0,11
-0,14
125
143,18
138,19
-0,04
-0,47
-1,41
124,01
143,07
138,05
-0,3
-0,95
-0,08
123,97
142,6
136,64
0,62
0,14
0,45
123,67
141,65
136,56
0,74
3,33
1,5
124,29
141,79
137,01
0,27
0,25
3,54
125,03
145,12
138,51
1,43
0,12
1,81
125,3
145,37
142,05
-1,35
-0,13
-1,91
126,73
145,49
143,86
0,03
-0,94
-0,56
125,38
145,36
141,95
1,11
-0,11
-0,28
125,41
144,42
141,39
0,45
1,72
2,01
126,52
144,31
141,11
0,08
-0,56
-0,45
126,97
146,03
143,12
1,18
-0,55
-1,07
127,05
145,47
142,67
-1,85
0,13
-0,25
128,23
144,92
141,6
0,89
2,35
1,43
126,38
145,05
141,35
-1,2
0,84
0,78
127,27
147,4
127,27
xxiii
∆JPR (dJPR)
∆SRG (dSRG)
∆MAN (dMAN)
JPR 𝑡−1 (JPRt)
SRG𝑡−1 (SRGt)
MAN𝑡−1 (MANt)
1,21
2,24
2,95
126,07
148,24
126,07
0,8
1,8
1,75
127,28
150,48
127,28
0,83
2,18
0,67
128,08
152,28
128,08
0,16
-0,41
-1,62
128,91
154,46
128,91
0,19
-0,66
1,43
129,07
154,05
129,07
0,13
-0,82
-1,43
129,26
153,39
129,26
3,32
0,93
2,79
129,39
152,57
129,39
0,53
-1,5
-1,13
132,71
153,5
132,71
4,2
1,65
0,84
133,24
152
133,24
-3,62
2,66
1,59
137,44
153,65
137,44
-0,8
0,76
0,59
133,82
156,31
133,82
1,29
0,46
0,74
133,02
157,07
133,02
xxiv
Lampiran 3 Program R untuk mengestimasi parameter model koreksi kesalahan (dengan intersep) dengan metode bootstrap pada data Indeks Harga Konsumen kota Jayapura dan kota Manokwari di Papua dari BPS dengan sampel n = 51 (Makalah 1) dJPR <- read.table('dat.txt')[,1] dMAN <- read.table('dat.txt')[,2] MANt <- read.table('dat.txt')[,4] JPRt <- read.table('dat.txt')[,3] ECT <- MANt-JPRt bootstrap <- function(dMAN,MANt,ECT,dJPR,B) { hasil <- matrix(0,B,4) d<-data.frame(dMAN,MANt,ECT,dJPR) reg<- lm(dJPR ~ dMAN+MANt+ECT,data=d) r<-resid(reg) a <- reg[[1]][1] b <- reg[[1]][2] f <- reg[[1]][3] g <- reg[[1]][4] for(i in 1:B) { rbintang <- sample(r,replace=T) JPRbintang <- a + b*dMAN + f*MANt + g*ECT + rbintang dbintang <- data.frame(dMAN,MANt,ECT,JPRbintang) reg2<-lm(JPRbintang~.,data=dbintang) hasil[i,1] <- reg2[[1]][1] hasil[i,2] <- reg2[[1]][2] hasil[i,3] <- reg2[[1]][3] hasil[i,4] <- reg2[[1]][4] } hasil } hasil<-bootstrap(dMAN, MANt, ECT, dJPR,1000) mean(hasil[,1]) mean(hasil[,2]) mean(hasil[,3]) mean(hasil[,4]) sd(hasil[,1]) sd(hasil[,2]) sd(hasil[,3]) sd(hasil[,4]) t1<-mean(hasil[,1])/sd(hasil[,1]) t2<-mean(hasil[,2])/sd(hasil[,2]) t3<-mean(hasil[,3])/sd(hasil[,3]) t4<-mean(hasil[,4])/sd(hasil[,4]) 2*pt(-t1,52-4) 2*pt(-t2,52-4) 2*pt(t3,52-4) 2*pt(-t4,52-4)
Program R untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode bootstrap pada data Indeks Harga Konsumen kota Jayapura dan kota Manokwari di Papua dari BPS dengan sampel n = 52 (Makalah 1) dJPR <- read.table('dat.txt')[,1] dMAN <- read.table('dat.txt')[,2] MANt <- read.table('dat.txt')[,4]
xxv
JPRt <- read.table('dat.txt')[,3] ECT <- MANt-JPRt bootstrap <- function(dMAN,MANt,ECT,dJPR,B) { hasil <- matrix(0,B,3) d<-data.frame(dMAN,MANt,ECT,dJPR) reg<- lm(dJPR ~ dMAN+MANt+ECT-1,data=d) r<-resid(reg) b <- reg[[1]][1] f <- reg[[1]][2] g <- reg[[1]][3] for(i in 1:B) { rbintang <- sample(r,replace=T) JPRbintang <- b*dMAN + f*MANt + g*ECT + rbintang dbintang <- data.frame(dMAN,MANt,ECT,JPRbintang) reg2<-lm(JPRbintang~dMAN+MANt+ECT-1,data=dbintang) hasil[i,1] <- reg2[[1]][1] hasil[i,2] <- reg2[[1]][2] hasil[i,3] <- reg2[[1]][3] } hasil } hasil<-bootstrap(dMAN, MANt, ECT, dJPR,1000) mean(hasil[,1]) mean(hasil[,2]) mean(hasil[,3]) sd(hasil[,1]) sd(hasil[,2]) sd(hasil[,3]) t1<-mean(hasil[,1])/sd(hasil[,1]) t2<-mean(hasil[,2])/sd(hasil[,2]) t3<-mean(hasil[,3])/sd(hasil[,3]) 2*pt(-t1,52-4) 2*pt(t2,52-4) 2*pt(-t3,52-4)
Program R untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode bootstrap pada data Indeks Harga Konsumen kota Jayapura dan kota Sorong di Papua dari BPS dengan sampel n = 52 (Makalah 1) dJPR <- read.table('dat1.txt')[,1] dSRG <- read.table('dat1.txt')[,2] SRGt <- read.table('dat1.txt')[,4] JPRt <- read.table('dat1.txt')[,3] ECT <- SRGt-JPRt bootstrap <- function(dSRG,SRGt,ECT,dJPR,B) { hasil <- matrix(0,B,3) d<-data.frame(dSRG,SRGt,ECT,dJPR) reg<- lm(dJPR ~ dSRG+SRGt+ECT-1,data=d) r<-resid(reg) b <- reg[[1]][1] f <- reg[[1]][2] g <- reg[[1]][3] for(i in 1:B) { rbintang <- sample(r,replace=T) JPRbintang <- b*dSRG + f*SRGt + g*ECT + rbintang dbintang <- data.frame(dSRG,SRGt,ECT,JPRbintang)
xxvi
reg2<-lm(JPRbintang~dSRG+SRGt+ECT-1,data=dbintang) hasil[i,1] <- reg2[[1]][1] hasil[i,2] <- reg2[[1]][2] hasil[i,3] <- reg2[[1]][3] } hasil } hasil<-bootstrap(dSRG, SRGt, ECT, dJPR, 1000) mean(hasil[,1]) mean(hasil[,2]) mean(hasil[,3]) sd(hasil[,1]) sd(hasil[,2]) sd(hasil[,3]) t1<-mean(hasil[,1])/sd(hasil[,1]) t2<-mean(hasil[,2])/sd(hasil[,2]) t3<-mean(hasil[,3])/sd(hasil[,3]) 2*pt(t1,52-4) 2*pt(t2,52-4) 2*pt(-t3,52-4)
Program R untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode bootstrap pada data Indeks Harga Konsumen kota Manokwari dan kota Jayapura di Papua dari BPS dengan sampel n = 52 (Makalah 1) dJPR <- read.table('dat.txt')[,1] dMAN <- read.table('dat.txt')[,2] MANt <- read.table('dat.txt')[,4] JPRt <- read.table('dat.txt')[,3] ECT <- JPRt-MANt bootstrap <- function(dJPR,JPRt,ECT,dMAN,B) { hasil <- matrix(0,B,3) d<-data.frame(dJPR,JPRt,ECT,dMAN) reg<- lm(dMAN ~ dJPR+JPRt+ECT-1,data=d) r<-resid(reg) b <- reg[[1]][1] f <- reg[[1]][2] g <- reg[[1]][3] for(i in 1:B) { rbintang <- sample(r,replace=T) MANbintang <- b*dJPR + f*JPRt + g*ECT + rbintang dbintang <- data.frame(dJPR,JPRt,ECT,MANbintang) reg2<-lm(MANbintang~dJPR+JPRt+ECT-1,data=dbintang) hasil[i,1] <- reg2[[1]][1] hasil[i,2] <- reg2[[1]][2] hasil[i,3] <- reg2[[1]][3] } hasil } hasil<-bootstrap(dJPR, JPRt, ECT, dMAN,1000) mean(hasil[,1]) mean(hasil[,2]) mean(hasil[,3])
xxvii
sd(hasil[,1]) sd(hasil[,2]) sd(hasil[,3]) t1<-mean(hasil[,1])/sd(hasil[,1]) t2<-mean(hasil[,2])/sd(hasil[,2]) t3<-mean(hasil[,3])/sd(hasil[,3]) 2*pt(-t1,52-4) 2*pt(-t2,52-4) 2*pt(t3,52-4)
Program R untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode bootstrap pada data Indeks Harga Konsumen kota Manokwari dan kota Sorong di Papua dari BPS dengan sampel n = 52 (Makalah 1) dMAN <- read.table('dat2.txt')[,1] dSRG <- read.table('dat2.txt')[,2] SRGt <- read.table('dat2.txt')[,4] MANt <- read.table('dat2.txt')[,3] ECT <- SRGt-MANt bootstrap <- function(dSRG,SRGt,ECT,dMAN,B) { hasil <- matrix(0,B,3) d<-data.frame(dSRG,SRGt,ECT,dMAN) reg<- lm(dMAN ~ dSRG+SRGt+ECT-1,data=d) r<-resid(reg) b <- reg[[1]][1] f <- reg[[1]][2] g <- reg[[1]][3] for(i in 1:B) { rbintang <- sample(r,replace=T) MANbintang <- b*dSRG + f*SRGt + g*ECT + rbintang dbintang <- data.frame(dSRG,SRGt,ECT,MANbintang) reg2<-lm(MANbintang~dSRG+SRGt+ECT-1,data=dbintang) hasil[i,1] <- reg2[[1]][1] hasil[i,2] <- reg2[[1]][2] hasil[i,3] <- reg2[[1]][3] } hasil } hasil<-bootstrap(dSRG, SRGt, ECT, dMAN,1000) mean(hasil[,1]) mean(hasil[,2]) mean(hasil[,3]) sd(hasil[,1]) sd(hasil[,2]) sd(hasil[,3]) t1<-mean(hasil[,1])/sd(hasil[,1]) t2<-mean(hasil[,2])/sd(hasil[,2]) t3<-mean(hasil[,3])/sd(hasil[,3])
xxviii
2*pt(-t1,52-4) 2*pt(-t2,52-4) 2*pt(-t3,52-4)
Program R untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode bootstrap pada data Indeks Harga Konsumen kota Sorong dan kota Jayapura di Papua dari BPS dengan sampel n = 52 (Makalah 1) dJPR <- read.table('dat1.txt')[,1] dSRG <- read.table('dat1.txt')[,2] SRGt <- read.table('dat1.txt')[,4] JPRt <- read.table('dat1.txt')[,3] ECT <- JPRt-SRGt bootstrap <- function(JPRt,ECT,dSRG,B) { hasil <- matrix(0,B,2) d<-data.frame(JPRt,ECT,dSRG) reg<- lm(dSRG ~ JPRt+ECT-1,data=d) r<-resid(reg) b <- reg[[1]][1] f<- reg [[1]][2] for(i in 1:B) { rbintang <- rnorm(52,0,w[[6]])#sample(r,replace=T) SRGbintang <- b*JPRt+f*ECT + rbintang dbintang <- data.frame(JPRt,ECT,SRGbintang) reg2<-lm(SRGbintang~JPRt+ECT-1,data=dbintang) hasil[i,1] <- reg2[[1]][1] hasil[i,2] <- reg2[[1]][2] } hasil } hasil<-bootstrap(JPRt,ECT, dSRG,1000) mean(hasil[,1]) mean(hasil[,2]) sd(hasil[,1]) sd(hasil[,2]) t1<-mean(hasil[,1])/sd(hasil[,1]) t2<-mean(hasil[,2])/sd(hasil[,2]) 2*pt(-t1,52-4) 2*pt(-t2,52-4)
Program R untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode bootstrap pada data Indeks Harga Konsumen kota Sorong dan kota Manokwari di Papua dari BPS dengan sampel n = 52 (Makalah 1) dMAN <- read.table('dat2.txt')[,1] dSRG <- read.table('dat2.txt')[,2] SRGt <- read.table('dat2.txt')[,4] MANt <- read.table('dat2.txt')[,3] ECT <- MANt-SRGt bootstrap <- function(dMAN,MANt,ECT,dSRG,B) {
xxix
hasil <- matrix(0,B,3) d<-data.frame(dMAN,MANt,ECT,dSRG) reg<- lm(dSRG ~ dMAN+MANt+ECT-1,data=d) r<-resid(reg) b <- reg[[1]][1] f<- reg [[1]][2] for(i in 1:B) { rbintang <- sample(r,replace=T) SRGbintang <- b*dMAN + f*MANt + g*ECT + rbintang dbintang <- data.frame(dMAN,MANt,ECT,SRGbintang) reg2<-lm(SRGbintang~dMAN+MANt+ECT-1,data=dbintang) hasil[i,1] <- reg2[[1]][1] hasil[i,2] <- reg2[[1]][2] } hasil } hasil<-bootstrap(dMAN, MANt, ECT, dSRG,1000) mean(hasil[,1]) mean(hasil[,2]) sd(hasil[,1]) sd(hasil[,2]) t1<-mean(hasil[,1])/sd(hasil[,1]) t2<-mean(hasil[,2])/sd(hasil[,2]) 2*pt(-t1,52-4) 2*pt(-t2,52-4)
xxx
Lampiran 4 Program WinBUGS untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode Bayesian pada data Indeks Harga Konsumen kota Jayapura dan kota Manokwari di Papua dari BPS dengan sampel n = 52 (Makalah 2) model { for(i in 1:n) { diff_jayapura[i]~dnorm(mu[i],tau) mu[i]<-g1*diff_manokwari[i]+g2*manokwari_1[i]+g3*ect[i] } tau~dgamma(0.01,0.01) g1~dnorm(0.0,1.0E-4) g2~dnorm(0.0,1.0E-4) g3~dnorm(0.0,1.0E-4) s2<-1.0/tau s<-sqrt(s2) } list(g1=0,g2=0,g3=0,tau=1) list(n=52, diff_jayapura=c(-0.5, 1.89, -0.04, -1.51, 1.14, -0.64, 0.93, 1.49, -0.75, 1.3, 0.36, 1.5, -0.62, 0.66, -0.61, 0.84, 1, 0.29, 0.63, 0.72, -1.85, 0.45, 2.26, 2.2, -0.99, -0.04, -0.3, 0.62, 0.74, 0.27, 1.43, -1.35, 0.03, 1.11, 0.45, 0.08, 1.18, -1.85, 0.89, -1.2, 1.21, 0.8, 0.83, 0.16, 0.19, 0.13, 3.32, 0.53, 4.2, -3.62, -0.8, 1.29), diff_manokwari=c(0.03, -0.42, 0.09, 0.28, 0.09, 0.23, 2.55, 0.27, -0.69, 0.12, 1.97, 0.22, -0.82, 0.02, 2.68, -2.16, 1.56, 2.54, 0.61, -0.63, -1.16, -0.34, 3.65, 0.09, -0.14, -1.41, -0.08, 0.45, 1.5, 3.54, 1.81, -1.91, -0.56, -0.28, 2.01, -0.45, -1.07, -0.25, 1.43, 0.78, 2.95, 1.75, 0.67, -1.62, 1.43, -1.43, 2.79, -1.13, 0.84, 1.59, 0.59, 0.74), manokwari_1=c(127.41, 127.44, 127.02, 127.11, 127.39, 127.48, 127.71, 130.26, 130.53, 129.84, 129.96, 131.93, 132.15, 131.33, 131.35, 134.03, 131.87, 133.43, 135.97, 136.58, 135.95, 134.79, 134.45, 138.1, 138.19, 138.05, 136.64, 136.56, 137.01, 138.51, 142.05, 143.86, 141.95, 141.39, 141.11, 143.12, 142.67, 141.6, 141.35, 142.78, 143.56, 146.51, 148.26, 148.93, 147.31, 148.74, 147.31, 150.1, 148.97, 149.81, 151.4, 151.99), ect=c(13.55, 14.08, 11.77, 11.90, 13.69, 12.64, 13.51, 15.13, 13.91, 13.97, 12.79, 14.40, 13.12, 12.92, 12.28, 15.57, 12.57, 13.13, 15.38, 15.36, 14.01, 14.70, 13.91, 15.30, 13.19, 14.04, 12.67, 12.89, 12.72, 13.48, 16.75, 17.13, 16.57, 15.98, 14.59, 16.15, 15.62, 13.37, 14.97, 15.51, 17.49, 19.23, 20.18, 20.02, 18.24, 19.48, 17.92, 17.39, 15.73, 12.37, 17.58, 18.97))
Program WinBUGS untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode Bayesian pada data Indeks Harga Konsumen kota Jayapura dan kota Sorong di Papua dari BPS dengan sampel n = 52 (Makalah 2) model { for(i in 1:n) { diff_jayapura[i]~dnorm(mu[i],tau) mu[i]<-g2*sorong_1[i]+g3*ect[i] } tau~dgamma(0.01,0.01) g2~dnorm(0.0,1.0E-4) g3~dnorm(0.0,1.0E-4) s2<-1.0/tau s<-sqrt(s2) } list(g2=0,g3=0,tau=1) list(n=52, diff_jayapura=c(-0.5, 1.89, -0.04, -1.51, 1.14, -0.64, 0.93, 1.49, -0.75, 1.3, 0.36, 1.5, -0.62, 0.66, -0.61, 0.84, 1, 0.29, 0.63, 0.72, -1.85, 0.45, 2.26, 2.2, -0.99, -0.04, -0.3, 0.62, 0.74, 0.27, 1.43, -1.35, 0.03, 1.11, 0.45, 0.08, 1.18, -1.85, 0.89, -1.2, 1.21, 0.8, 0.83, 0.16, 0.19, 0.13, 3.32, 0.53, 4.2, -3.62, -0.8, 1.29),
xxxi
sorong_1=c(130.27, 130.36, 131.46, 130.44, 130.51, 132.14, 135.04, 134.01, 132.70, 133.30, 133.37, 133.85, 134.01, 135.23, 135.65, 136.99, 137.95, 138.14, 142.12, 144.41, 145.74, 145.67, 146.64, 144.73, 143.18, 143.07, 142.60, 141.65, 141.79, 145.12, 145.37, 145.49, 145.36, 144.42, 144.31, 146.03, 145.47, 144.92, 145.05, 147.40, 148.24, 150.48, 152.28, 154.46, 154.05, 153.39, 152.57, 153.50, 152.00, 153.65, 156.31, 157.07), ect=c(16.41, 17.00, 16.21, 15.23, 16.81, 17.30, 20.84, 18.88, 16.08, 17.43, 16.20, 16.32, 14.98, 16.82, 16.58, 18.53, 18.65, 17.84, 21.53, 23.19, 23.80, 25.58, 26.10, 21.93, 18.18, 19.06, 18.63, 17.98, 17.50, 20.09, 20.07, 18.76, 19.98, 19.01, 17.79, 19.06, 18.42, 16.69, 18.67, 20.13, 22.17, 23.20, 24.20, 25.55, 24.98, 24.13, 23.18, 20.79, 18.76, 16.21, 22.49, 24.05))
Program WinBUGS untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode Bayesian pada data Indeks Harga Konsumen kota Manokwari dan kota Sorong di Papua dari BPS dengan sampel n = 52 (Makalah 2) model { for(i in 1:n) { diff_manokwari[i]~dnorm(mu[i],tau) mu[i]<-g1*diff_sorong[i]+g2*sorong_1[i]+g3*ect[i] } tau~dgamma(0.01,0.01) g1~dnorm(0.0,1.0E-4) g2~dnorm(0.0,1.0E-4) g3~dnorm(0.0,1.0E-4) s2<-1.0/tau s<-sqrt(s2) } list(g1=0,g2=0,g3=0,tau=1) list(n=52, diff_manokwari=c(0.03, -0.42, 0.09, 0.28, 0.09, 0.23, 2.55, 0.27, -0.69, 0.12, 1.97, 0.22, -0.82, 0.02, 2.68, -2.16, 1.56, 2.54, 0.61, -0.63, -1.16, -0.34, 3.65, 0.09, -0.14, -1.41, -0.08, 0.45, 1.5, 3.54, 1.81, -1.91, -0.56, -0.28, 2.01, -0.45, -1.07, -0.25, 1.43, 0.78, 2.95, 1.75, 0.67, -1.62, 1.43, -1.43, 2.79, -1.13, 0.84, 1.59, 0.59, 0.74), diff_sorong=c(0.09, 1.10, -1.02, 0.07, 1.63, 2.90, -1.03, -1.31, 0.60, 0.07, 0.48, 0.16, 1.22, 0.42, 1.34, 0.96, 0.19, 3.98, 2.29, 1.33, -0.07, 0.97, -1.91, -1.55, -0.11, -0.47, -0.95, 0.14, 3.33, 0.25, 0.12, -0.13, -0.94, -0.11, 1.72, -0.56, -0.55, 0.13, 2.35, 0.84, 2.24, 1.80, 2.18, -0.41, -0.66, -0.82, 0.93, -1.50, 1.65, 2.66, 0.76, 0.46), sorong_1=c(130.27, 130.36, 131.46, 130.44, 130.51, 132.14, 135.04, 134.01, 132.70, 133.30, 133.37, 133.85, 134.01, 135.23, 135.65, 136.99, 137.95, 138.14, 142.12, 144.41, 145.74, 145.67, 146.64, 144.73, 143.18, 143.07, 142.60, 141.65, 141.79, 145.12, 145.37, 145.49, 145.36, 144.42, 144.31, 146.03, 145.47, 144.92, 145.05, 147.40, 148.24, 150.48, 152.28, 154.46, 154.05, 153.39, 152.57, 153.50, 152.00, 153.65, 156.31, 157.07), ect=c(2.86, 2.92, 4.44, 3.33, 3.12, 4.66, 7.33, 3.75, 2.17, 3.46, 3.41, 1.92, 1.86, 3.90, 4.30, 2.96, 6.08, 4 .71, 6.15, 7.83, 9.79, 10.88, 12.19, 6.63, 4.99, 5.02, 5.96, 5.09, 4.78, 6.61, 3.32, 1.63, 3.41, 3.03, 3.20, 2.91, 2.80, 3.32, 3.70, 4.62, 4.68, 3.97, 4.02, 5.53, 6.74, 4.65, 5.26, 3.40, 3.03, 3.84, 4.91, 5.08))
Program WinBUGS untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode Bayesian pada data Indeks Harga Konsumen kota kota Manokwari dan kota Jayapura di Papua dari BPS dengan sampel n = 52 (Makalah 2) model { for(i in 1:n) { diff_manokwari[i]~dnorm(mu[i],tau) mu[i]<-g1*diff_jayapura[i]+g2*jayapura_1[i]+g3*ect[i] } tau~dgamma(0.01,0.01) g1~dnorm(0.0,1.0E-4) g2~dnorm(0.0,1.0E-4) g3~dnorm(0.0,1.0E-4) s2<-1.0/tau s<-sqrt(s2) } list(g1=0,g2=0,g3=0,tau=1)
xxxii
list(n=52, diff_manokwari=c(0.03, -0.42, 0.09, 0.28, 0.09, 0.23, 2.55, 0.27, -0.69, 0.12, 1.97, 0.22, -0.82, 0.02, 2.68, -2.16, 1.56, 2.54, 0.61, -0.63, -1.16, -0.34, 3.65, 0.09, -0.14, -1.41, -0.08, 0.45, 1.5, 3.54, 1.81, -1.91, -0.56, -0.28, 2.01, -0.45, -1.07, -0.25, 1.43, 0.78, 2.95, 1.75, 0.67, -1.62, 1.43, -1.43, 2.79, -1.13, 0.84, 1.59, 0.59, 0.74), diff_jayapura=c(-0.5, 1.89, -0.04, -1.51, 1.14, -0.64, 0.93, 1.49, -0.75, 1.3, 0.36, 1.5, -0.62, 0.66, -0.61, 0.84, 1, 0.29, 0.63, 0.72, -1.85, 0.45, 2.26, 2.2, -0.99, -0.04, -0.3, 0.62, 0.74, 0.27, 1.43, -1.35, 0.03, 1.11, 0.45, 0.08, 1.18, -1.85, 0.89, -1.2, 1.21, 0.8, 0.83, 0.16, 0.19, 0.13, 3.32, 0.53, 4.2, -3.62, -0.8, 1.29), jayapura_1 =c(113.86, 113.36, 115.25, 115.21, 113.70, 114.84, 114.20, 115.13, 116.62, 115.87, 117.17, 117.53, 119.03, 118.41, 119.07, 118.46, 119.30, 120.30, 120.59, 121.22, 121.94, 120.09, 120.54, 122.80, 125.00, 124.01, 123.97, 123.67, 124.29, 125.03, 125.30, 126.73, 125.38, 125.41, 126.52, 126.97, 127.05, 128.23, 126.38, 127.27, 126.07, 127.28, 128.08, 128.91, 129.07, 129.26, 129.39, 132.71, 133.24, 137.44, 133.82, 133.02), ect =c(-13.55, -14.08, -11.77, -11.90, -13.69, -12.64, -13.51, -15.13, -13.91, -13.97, -12.79, -14.40, -13.12, -12.92, 12.28, -15.57, -12.57, -13.13, -15.38, -15.36, -14.01, -14.70, -13.91, -15.30, -13.19, -14.04, -12.67, -12.89, -12.72, 13.48, -16.75, -17.13, -16.57, -15.98, -14.59, -16.15, -15.62, -13.37, -14.97, -15.51, -17.49, -19.23, -20.18, -20.02, 18.24, -19.48, -17.92, -17.39, -15.73, -12.37, -17.58, -18.97))
Program WinBUGS untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode Bayesian pada data Indeks Harga Konsumen kota Sorong dan kota Jayapura di Papua dari BPS dengan sampel n = 52 (Makalah 2) model { for(i in 1:n) { diff_sorong[i]~dnorm(mu[i],tau) mu[i]<-g2*jayapura_1[i]+g3*ect[i] } tau~dgamma(0.01,0.01) g2~dnorm(0.0,1.0E-4) g3~dnorm(0.0,1.0E-4) s2<-1.0/tau s<-sqrt(s2) } list(g2=0,g3=0,tau=1) list(n=52, diff_sorong=c(0.09, 1.10, -1.02, 0.07, 1.63, 2.90, -1.03, -1.31, 0.60, 0.07, 0.48, 0.16, 1.22, 0.42, 1.34, 0.96, 0.19, 3.98, 2.29, 1.33, -0.07, 0.97, -1.91, -1.55, -0.11, -0.47, -0.95, 0.14, 3.33, 0.25, 0.12, -0.13, -0.94, -0.11, 1.72, -0.56, -0.55, 0.13, 2.35, 0.84, 2.24, 1.80, 2.18, -0.41, -0.66, -0.82, 0.93, -1.50, 1.65, 2.66, 0.76, 0.46), jayapura_1 =c(113.86, 113.36, 115.25, 115.21, 113.70, 114.84, 114.20, 115.13, 116.62, 115.87, 117.17, 117.53, 119.03, 118.41, 119.07, 118.46, 119.30, 120.30, 120.59, 121.22, 121.94, 120.09, 120.54, 122.80, 125.00, 124.01, 123.97, 123.67, 124.29, 125.03, 125.30, 126.73, 125.38, 125.41, 126.52, 126.97, 127.05, 128.23, 126.38, 127.27, 126.07, 127.28, 128.08, 128.91, 129.07, 129.26, 129.39, 132.71, 133.24, 137.44, 133.82, 133.02), ect =c(-16.41, -17.00, -16.21, -15.23, -16.81, -17.30, -20.84, -18.88, -16.08, -17.43, -16.20, -16.32, -14.98, -16.82, 16.58, -18.53, -18.65, -17.84, -21.53, -23.19, -23.80, -25.58, -26.10, -21.93, -18.18, -19.06, -18.63, -17.98, -17.50, 20.09, -20.07, -18.76, -19.98, -19.01, -17.79, -19.06, -18.42, -16.69, -18.67, -20.13, -22.17, -23.20, -24.20, -25.55, 24.98, -24.13, -23.18, -20.79, -18.76, -16.21, -22.49, -24.05))
Program WinBUGS untuk mengestimasi parameter model koreksi kesalahan tanpa intersep dengan metode Bayesian pada data Indeks Harga Konsumen kota Sorong dan kota Manokwari di Papua dari BPS dengan sampel n = 52 (Makalah 2) model { for(i in 1:n) { diff_sorong[i]~dnorm(mu[i],tau) mu[i]<-g1*diff_manokwari[i]+g2*manokwari_1[i]+g3*ect[i] } tau~dgamma(0.01,0.01) g1~dnorm(0.0,1.0E-4) g2~dnorm(0.0,1.0E-4) g3~dnorm(0.0,1.0E-4)
xxxiii
s2<-1.0/tau s<-sqrt(s2) } list(g1=0,g2=0,g3=0,tau=1) list(n=52, diff_sorong=c(0.09, 1.10, -1.02, 0.07, 1.63, 2.90, -1.03, -1.31, 0.60, 0.07, 0.48, 0.16, 1.22, 0.42, 1.34, 0.96, 0.19, 3.98, 2.29, 1.33, -0.07, 0.97, -1.91, -1.55, -0.11, -0.47, -0.95, 0.14, 3.33, 0.25, 0.12, -0.13, -0.94, -0.11, 1.72, -0.56, -0.55, 0.13, 2.35, 0.84, 2.24, 1.80, 2.18, -0.41, -0.66, -0.82, 0.93, -1.50, 1.65, 2.66, 0.76, 0.46), diff_manokwari=c(0.03, -0.42, 0.09, 0.28, 0.09, 0.23, 2.55, 0.27, -0.69, 0.12, 1.97, 0.22, -0.82, 0.02, 2.68, -2.16, 1.56, 2.54, 0.61, -0.63, -1.16, -0.34, 3.65, 0.09, -0.14, -1.41, -0.08, 0.45, 1.5, 3.54, 1.81, -1.91, -0.56, -0.28, 2.01, -0.45, -1.07, -0.25, 1.43, 0.78, 2.95, 1.75, 0.67, -1.62, 1.43, -1.43, 2.79, -1.13, 0.84, 1.59, 0.59, 0.74), manokwari_1=c(127.41, 127.44, 127.02, 127.11, 127.39, 127.48, 127.71, 130.26, 130.53, 129.84, 129.96, 131.93, 132.15, 131.33, 131.35, 134.03, 131.87, 133.43, 135.97, 136.58, 135.95, 134.79, 134.45, 138.1, 138.19, 138.05, 136.64, 136.56, 137.01, 138.51, 142.05, 143.86, 141.95, 141.39, 141.11, 143.12, 142.67, 141.6, 141.35, 142.78, 143.56, 146.51, 148.26, 148.93, 147.31, 148.74, 147.31, 150.1, 148.97, 149.81, 151.4, 151.99), ect =c(-2.86, -2.92, -4.44, -3.33, -3.12, -4.66, -7.33, -3.75, -2.17, -3.46, -3.41, -1.92, -1.86, -3.90, -4.30, -2.96, -6.08, -4.71, -6.15, -7.83, -9.79, -10.88, -12.19, -6.63, -4.99, -5.02, -5.96, -5.09, -4.78, -6.61, -3.32, -1.63, -3.41, -3.03, -3.20, -2.91, -2.80, -3.32, -3.70, -4.62, -4.68, -3.97, -4.02, -5.53, -6.74, -4.65, -5.26, -3.40, -3.03, -3.84, -4.91, -5.08))
xxxiv
Lampiran 5 Program R untuk plot data Gambar 2 pada Makalah 1 JPRt <- read.table('dat3.txt')[,1] MANt <- read.table('dat3.txt')[,2] SRGt <- read.table('dat3.txt')[,3] JPRt1 <- 0.8972*MANt MANt1 <- 1.1328*JPRt SRGt1 <- 1.0485*MANt JPRt2 <- 0.8793*SRGt MANt2 <- 0.9765*SRGt SRGt2 <- 1.1985*JPRt par(mfrow=c(1,1)) plot(JPRt,type="l",col="blue",ylim=c(115,165),main="JPRt=0.8972*MANt"); lines(JPRt1,col="red",ylim=c(115,165)) par(mfrow=c(1,1)) plot(JPRt,type="l",col="blue",ylim=c(115,165),main="JPRt=0.8793*SRGt"); lines(JPRt2,col="red",ylim=c(115,165)) par(mfrow=c(1,1)) plot(MANt,type="l",col="blue",ylim=c(115,165),main="MANt=1.1328*JPRt"); lines(MANt1,col="red",ylim=c(115,165)) par(mfrow=c(1,1)) plot(MANt,type="l",col="blue",ylim=c(115,165),main="MANt=0.9765*SRGt"); lines(MANt2,col="red",ylim=c(115,165 )) par(mfrow=c(1,1)) plot(SRGt,type="l",col="blue",ylim=c(115,165),main="SRGt=1.0485*MANt"); lines(SRGt1,col="red",ylim=c(115,165)) par(mfrow=c(1,1)) plot(SRGt,type="l",col="blue",ylim=c(115,165),main="SRGt=1.1985*JPRt"); lines(SRGt2,col="red",ylim=c(115,165))
Program R untuk plot data Gambar 1 pada Makalah 2 JPRt <- read.table('dat3.txt')[,1] MANt <- read.table('dat3.txt')[,2] SRGt <- read.table('dat3.txt')[,3] plot(MANt,JPRt,ylim=c(110,140)) plot(JPRt,MANt,ylim=c(125,155))
Program R untuk plot data Gambar 2 pada Makalah 2 JPRt <- read.table('dat3.txt')[,1] MANt <- read.table('dat3.txt')[,2] SRGt <- read.table('dat3.txt')[,3] plot(SRGt,MANt,ylim=c(125,155)) plot(MANt,SRGt,ylim=c(125,160))
Program R untuk plot data Gambar 3 pada Makalah 2 JPRt <- read.table('dat3.txt')[,1] MANt <- read.table('dat3.txt')[,2] SRGt <- read.table('dat3.txt')[,3] plot(JPRt,SRGt,ylim=c(130,160)) plot(SRGt,JPRt,ylim=c(110,140))
xxxv
Program R untuk menggambar rantai Markov untuk taksiran parameter g1, g2, dan g3 pada Gambar 4 (Makalah 2) g1<-read.table("beta1.txt")[501:5000,] plot(g1,type="l",main="g1") g2<-read.table("beta2.txt")[501:5000,] plot(g2,type="l",main="g2") g3<-read.table("beta3.txt")[501:5000,] plot(g3,type="l",main="g3")
Program R untuk menggambar fungsi densitas untuk taksiran parameter g1, g2, dan g3 pada Gambar 5 (Makalah 2) g1<-read.table("beta1.txt")[501:5000,] g2<-read.table("beta2.txt")[501:5000,] g3<-read.table("beta3.txt")[501:5000,] plot(density(g1),main="g1") plot(density(g2),main="g2") plot(density(g3),main="g3")
Program MATLAB untuk plot data Gambar 6 pada Makalah 2 JPR = [113.86; 113.36; 115.25; 115.21; 113.7; 114.84; 114.2; 115.13; 116.62; 115.87; 117.17; 117.53; 119.03; 118.41; 119.07; 118.46; 119.3; 120.3; 120.59; 121.22; 121.94; 120.09; 120.54; 122.8; 125; 124.01; 123.97; 123.67; 124.29; 125.03; 125.3; 126.73; 125.38; 125.41; 126.52; 126.97; 127.05; 128.23; 126.38; 127.27; 126.07; 127.28; 128.08; 128.91; 129.07; 129.26; 129.39; 132.71; 133.24; 137.44; 133.82; 133.02; 134.31]; MAN1 = [1 127.41; 1 127.44; 1 127.02; 1 127.11; 1 127.39; 1 127.48; 1 127.71; 1 130.26; 1 130.53; 1 129.84; 1 129.96; 1 131.93; 1 132.15; 1 131.33; 1 131.35; 1 134.03; 1 131.87; 1 133.43; 1 135.97; 1 136.58; 1 135.95; 1 134.79; 1 134.45; 1 138.1; 1 138.19; 1 138.05; 1 136.64; 1 136.56; 1 137.01; 1 138.51; 1 142.05; 1 143.86; 1 141.95; 1 141.39; 1 141.11; 1 143.12; 1 142.67; 1 141.6; 1 141.35; 1 142.78; 1 143.56; 1 146.51; 1 148.26; 1 148.93; 1 147.31; 1 148.74; 1 147.31; 1 150.1; 1 148.97; 1 149.81; 1 151.4; 1 151.99; 1 152.73]; MAN = [127.41; 127.44; 127.02; 127.11; 127.39; 127.48; 127.71; 130.26; 130.53; 129.84; 129.96; 131.93; 132.15; 131.33; 131.35; 134.03; 131.87; 133.43; 135.97; 136.58; 135.95; 134.79; 134.45; 138.1; 138.19; 138.05; 136.64; 136.56; 137.01; 138.51; 142.05; 143.86; 141.95; 141.39; 141.11; 143.12; 142.67; 141.6; 141.35; 142.78; 143.56; 146.51; 148.26; 148.93; 147.31; 148.74; 147.31; 150.1; 148.97; 149.81; 151.4; 151.99; 152.73]; plot(MAN,JPR,'o') JPR_pend=0.8970*MAN; hold on plot(MAN,JPR_pend,'r-') hold off SRG = [130.27; 130.36 ; 131.46; 130.44; 130.51; 132.14; 135.04; 134.01; 132.7; 133.3; 133.37; 133.85; 134.01; 135.23; 135.65; 136.99; 137.95; 138.14; 142.12; 144.41; 145.74; 145.67; 146.64; 144.73; 143.18; 143.07; 142.6; 141.65; 141.79; 145.12; 145.37; 145.49; 145.36; 144.42; 144.31; 146.03; 145.47; 144.92; 145.05; 147.4; 148.24; 150.48; 152.28; 154.46; 154.05; 153.39; 152.57; 153.5; 152; 153.65; 156.31; 157.07; 157.53]; SRG1 = [1 130.27; 1 130.36 ; 1 131.46; 1 130.44; 1 130.51; 1 132.14; 1 135.04; 1 134.01; 1 132.7; 1 133.3; 1 133.37; 1 133.85; 1 134.01; 1 135.23; 1 135.65; 1 136.99; 1 137.95; 1 138.14; 1 142.12; 1 144.41; 1 145.74; 1 145.67; 1 146.64; 1 144.73; 1 143.18; 1 143.07; 1 142.6; 1 141.65; 1 141.79; 1 145.12; 1 145.37; 1 145.49; 1 145.36; 1 144.42; 1 144.31; 1 146.03; 1 145.47; 1 144.92; 1 145.05; 1 147.4; 1 148.24; 1 150.48; 1 152.28; 1 154.46; 1 154.05; 1 153.39; 1 152.57; 1 153.5; 1 152; 1 153.65; 1 156.31; 1 157.07; 1 157.53]; JPR = [113.86; 113.36; 115.25; 115.21; 113.7; 114.84; 114.2; 115.13; 116.62; 115.87; 117.17; 117.53; 119.03; 118.41; 119.07; 118.46; 119.3; 120.3; 120.59; 121.22; 121.94; 120.09; 120.54; 122.8; 125; 124.01; 123.97; 123.67; 124.29; 125.03; 125.3; 126.73; 125.38; 125.41; 126.52; 126.97; 127.05; 128.23; 126.38; 127.27; 126.07; 127.28; 128.08; 128.91; 129.07; 129.26; 129.39; 132.71; 133.24; 137.44; 133.82; 133.02; 134.31]; plot(SRG,JPR,'o') JPR_pend=0.8787*SRG; hold on plot(SRG,JPR_pend,'r-') hold off
xxxvi
Program MATLAB untuk plot data Gambar 7 pada Makalah 2 JPR = [113.86; 113.36; 115.25; 115.21; 113.7; 114.84; 114.2; 115.13; 116.62; 115.87; 117.17; 117.53; 119.03; 118.41; 119.07; 118.46; 119.3; 120.3; 120.59; 121.22; 121.94; 120.09; 120.54; 122.8; 125; 124.01; 123.97; 123.67; 124.29; 125.03; 125.3; 126.73; 125.38; 125.41; 126.52; 126.97; 127.05; 128.23; 126.38; 127.27; 126.07; 127.28; 128.08; 128.91; 129.07; 129.26; 129.39; 132.71; 133.24; 137.44; 133.82; 133.02; 134.31]; JPR1 = [1 113.86; 1 113.36; 1 115.25; 1 115.21; 1 113.7; 1 114.84; 1 114.2; 1 115.13; 1 116.62; 1 115.87; 1 117.17; 1 117.53; 1 119.03; 1 118.41; 1 119.07; 1 118.46; 1 119.3; 1 120.3; 1 120.59; 1 121.22; 1 121.94; 1 120.09; 1 120.54; 1 122.8; 1 125; 1 124.01; 1 123.97; 1 123.67; 1 124.29; 1 125.03; 1 125.3; 1 126.73; 1 125.38; 1 125.41; 1 126.52; 1 126.97; 1 127.05; 1 128.23; 1 126.38; 1 127.27; 1 126.07; 1 127.28; 1 128.08; 1 128.91; 1 129.07; 1 129.26; 1 129.39; 1 132.71; 1 133.24; 1 137.44; 1 133.82; 1 133.02; 1 134.31]; MAN = [127.41; 127.44; 127.02; 127.11; 127.39; 127.48; 127.71; 130.26; 130.53; 129.84; 129.96; 131.93; 132.15; 131.33; 131.35; 134.03; 131.87; 133.43; 135.97; 136.58; 135.95; 134.79; 134.45; 138.1; 138.19; 138.05; 136.64; 136.56; 137.01; 138.51; 142.05; 143.86; 141.95; 141.39; 141.11; 143.12; 142.67; 141.6; 141.35; 142.78; 143.56; 146.51; 148.26; 148.93; 147.31; 148.74; 147.31; 150.1; 148.97; 149.81; 151.4; 151.99; 152.73]; plot(JPR,MAN,'o') MAN_pend=1.1350*JPR; hold on plot(JPR,MAN_pend,'r-') hold off SRG = [130.27; 130.36 ; 131.46; 130.44; 130.51; 132.14; 135.04; 134.01; 132.7; 133.3; 133.37; 133.85; 134.01; 135.23; 135.65; 136.99; 137.95; 138.14; 142.12; 144.41; 145.74; 145.67; 146.64; 144.73; 143.18; 143.07; 142.6; 141.65; 141.79; 145.12; 145.37; 145.49; 145.36; 144.42; 144.31; 146.03; 145.47; 144.92; 145.05; 147.4; 148.24; 150.48; 152.28; 154.46; 154.05; 153.39; 152.57; 153.5; 152; 153.65; 156.31; 157.07; 157.53]; SRG1 = [1 130.27; 1 130.36 ; 1 131.46; 1 130.44; 1 130.51; 1 132.14; 1 135.04; 1 134.01; 1 132.7; 1 133.3; 1 133.37; 1 133.85; 1 134.01; 1 135.23; 1 135.65; 1 136.99; 1 137.95; 1 138.14; 1 142.12; 1 144.41; 1 145.74; 1 145.67; 1 146.64; 1 144.73; 1 143.18; 1 143.07; 1 142.6; 1 141.65; 1 141.79; 1 145.12; 1 145.37; 1 145.49; 1 145.36; 1 144.42; 1 144.31; 1 146.03; 1 145.47; 1 144.92; 1 145.05; 1 147.4; 1 148.24; 1 150.48; 1 152.28; 1 154.46; 1 154.05; 1 153.39; 1 152.57; 1 153.5; 1 152; 1 153.65; 1 156.31; 1 157.07; 1 157.53]; MAN = [127.41; 127.44; 127.02; 127.11; 127.39; 127.48; 127.71; 130.26; 130.53; 129.84; 129.96; 131.93; 132.15; 131.33; 131.35; 134.03; 131.87; 133.43; 135.97; 136.58; 135.95; 134.79; 134.45; 138.1; 138.19; 138.05; 136.64; 136.56; 137.01; 138.51; 142.05; 143.86; 141.95; 141.39; 141.11; 143.12; 142.67; 141.6; 141.35; 142.78; 143.56; 146.51; 148.26; 148.93; 147.31; 148.74; 147.31; 150.1; 148.97; 149.81; 151.4; 151.99; 152.73]; plot(SRG,MAN,'o') MAN_pend=0.9766*SRG; hold on plot(SRG,MAN_pend,'r-') hold off
Program MATLAB untuk plot data Gambar 8 pada Makalah 2 SRG = [130.27; 130.36 ; 131.46; 130.44; 130.51; 132.14; 135.04; 134.01; 132.7; 133.3; 133.37; 133.85; 134.01; 135.23; 135.65; 136.99; 137.95; 138.14; 142.12; 144.41; 145.74; 145.67; 146.64; 144.73; 143.18; 143.07; 142.6; 141.65; 141.79; 145.12; 145.37; 145.49; 145.36; 144.42; 144.31; 146.03; 145.47; 144.92; 145.05; 147.4; 148.24; 150.48; 152.28; 154.46; 154.05; 153.39; 152.57; 153.5; 152; 153.65; 156.31; 157.07; 157.53]; MAN1 = [1 127.41; 1 127.44; 1 127.02; 1 127.11; 1 127.39; 1 127.48; 1 127.71; 1 130.26; 1 130.53; 1 129.84; 1 129.96; 1 131.93; 1 132.15; 1 131.33; 1 131.35; 1 134.03; 1 131.87; 1 133.43; 1 135.97; 1 136.58; 1 135.95; 1 134.79; 1 134.45; 1 138.1; 1 138.19; 1 138.05; 1 136.64; 1 136.56; 1 137.01; 1 138.51; 1 142.05; 1 143.86; 1 141.95; 1 141.39; 1 141.11; 1 143.12; 1 142.67; 1 141.6; 1 141.35; 1 142.78; 1 143.56; 1 146.51; 1 148.26; 1 148.93; 1 147.31; 1 148.74; 1 147.31; 1 150.1; 1 148.97; 1 149.81; 1 151.4; 1 151.99; 1 152.73]; MAN = [127.41; 127.44; 127.02; 127.11; 127.39; 127.48; 127.71; 130.26; 130.53; 129.84; 129.96; 131.93; 132.15; 131.33; 131.35; 134.03; 131.87; 133.43; 135.97; 136.58; 135.95; 134.79; 134.45; 138.1; 138.19; 138.05; 136.64; 136.56; 137.01; 138.51; 142.05; 143.86; 141.95; 141.39; 141.11; 143.12; 142.67; 141.6; 141.35; 142.78; 143.56; 146.51; 148.26; 148.93; 147.31; 148.74; 147.31; 150.1; 148.97; 149.81; 151.4; 151.99; 152.73]; plot(MAN,SRG,'o') SRG_pend=1.0484*MAN; hold on
xxxvii
plot(MAN,SRG_pend,'r-') hold off
SRG = [130.27; 130.36 ; 131.46; 130.44; 130.51; 132.14; 135.04; 134.01; 132.7; 133.3; 133.37; 133.85; 134.01; 135.23; 135.65; 136.99; 137.95; 138.14; 142.12; 144.41; 145.74; 145.67; 146.64; 144.73; 143.18; 143.07; 142.6; 141.65; 141.79; 145.12; 145.37; 145.49; 145.36; 144.42; 144.31; 146.03; 145.47; 144.92; 145.05; 147.4; 148.24; 150.48; 152.28; 154.46; 154.05; 153.39; 152.57; 153.5; 152; 153.65; 156.31; 157.07; 157.53]; JPR = [113.86; 113.36; 115.25; 115.21; 113.7; 114.84; 114.2; 115.13; 116.62; 115.87; 117.17; 117.53; 119.03; 118.41; 119.07; 118.46; 119.3; 120.3; 120.59; 121.22; 121.94; 120.09; 120.54; 122.8; 125; 124.01; 123.97; 123.67; 124.29; 125.03; 125.3; 126.73; 125.38; 125.41; 126.52; 126.97; 127.05; 128.23; 126.38; 127.27; 126.07; 127.28; 128.08; 128.91; 129.07; 129.26; 129.39; 132.71; 133.24; 137.44; 133.82; 133.02; 134.31]; JPR1 = [1 113.86; 1 113.36; 1 115.25; 1 115.21; 1 113.7; 1 114.84; 1 114.2; 1 115.13; 1 116.62; 1 115.87; 1 117.17; 1 117.53; 1 119.03; 1 118.41; 1 119.07; 1 118.46; 1 119.3; 1 120.3; 1 120.59; 1 121.22; 1 121.94; 1 120.09; 1 120.54; 1 122.8; 1 125; 1 124.01; 1 123.97; 1 123.67; 1 124.29; 1 125.03; 1 125.3; 1 126.73; 1 125.38; 1 125.41; 1 126.52; 1 126.97; 1 127.05; 1 128.23; 1 126.38; 1 127.27; 1 126.07; 1 127.28; 1 128.08; 1 128.91; 1 129.07; 1 129.26; 1 129.39; 1 132.71; 1 133.24; 1 137.44; 1 133.82; 1 133.02; 1 134.31]; plot(JPR,SRG,'o') SRG_pend=1.2024*JPR; hold on plot(JPR,SRG_pend,'r-') hold off
xxxviii
