Laporan 1 Praktikum Pemrograman Statatitistik - Tugas Satria June Adwendi

Praktikum Pertemuan 1

Instalasi Linux

source: https://docs.microsoft.com/en-us/windows/wsl/install-win10

Install the Windows Subsystem for Linux

Before installing any Linux distros for WSL, you must ensure that the “Windows Subsystem for Linux” optional feature is enabled:

Open PowerShell as Administrator and run: Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux
Restart your computer when prompted.

Install your Linux Distribution of Choice

To download and install your preferred distro(s), you have three choices:

Download and install from the Windows Store (see below)
Download and install from the Command-Line/Script (read the manual installation instructions)
Download and manually unpack and install (for Windows Server – instructions here)

Windows 10 Fall Creators Update and later: Install from the Microsoft Store

This section is for Windows build 16215 or later. Follow these steps to check your build.

Open the Microsoft Store and choose your favorite Linux distribution.The following links will open the Windows store page for each distribution: Ubuntu

OpenSUSE

SLES

Kali Linux

Debian GNU/Linux

From the distro’s page, select “Get”

###Complete initialization of your distro Now that your Linux distro is installed, you must initialize your new distro instance once, before it can be used.

Praktikum Pertemuan 2 P

Obkjek di R

Dasar-Dasar R

•Assignment

A<-5 sama dengan A=5 sama dengan 5 -> A

•Case-sensitive

A<-5 berbeda dengan a<-5

•Penamaan objek

-Diawalihuruf(A-Z ataua-z) atautitik(.)

-Tidakmenggunakanspasidankarakterspesial(!,@,#, dst)

-Tidakmenggunakanataumenghindarikata yang sudahdigunakanolehR (NULL, TRUE, FALSE, q, c, t, sin, cos, dll)

•Packages

Moduldi R

•RStudio

Integrated Development Environment (IDE) untuk R

Objek di R

•Vector c(…), seq(…), rep(…), paste(…) •Matrix matrix(…,m,n), dim(vector)<-c(m,n),rbind(…),cbind(…) •Array array(…,dim=c(…)) •Factor factor(…), ordered(…,levels=c(…)) •List list(…) •Data Frame data.frame(…)

Vector

a1<-c(2,4,7,3)
assign("a2",c("2","4","7","3"))
a1

## [1] 2 4 7 3

a2

## [1] "2" "4" "7" "3"

#baris bilangan
a3<-seq(1,10,by=0.5)
a3

##  [1]  1.0  1.5  2.0  2.5  3.0  3.5  4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  8.0
## [16]  8.5  9.0  9.5 10.0

a4<-seq(1,10,length.out=12)
a4

##  [1]  1.000000  1.818182  2.636364  3.454545  4.272727  5.090909  5.909091
##  [8]  6.727273  7.545455  8.363636  9.181818 10.000000

#bilangan berulang
a5 <-rep(1,3)
a5

## [1] 1 1 1

a6 <-rep(1:3,3)
a6

## [1] 1 2 3 1 2 3 1 2 3

a7 <-rep(1:3,1:3)
a7

## [1] 1 2 2 3 3 3

a8 <-rep(1:3,rep(2,3))
a8

## [1] 1 1 2 2 3 3

a9 <-rep(1:3,each=2)
a9

## [1] 1 1 2 2 3 3

#karakterberpola
a10<-paste("A",1:10,sep="")
a10

##  [1] "A1"  "A2"  "A3"  "A4"  "A5"  "A6"  "A7"  "A8"  "A9"  "A10"

a11 <-paste0("A",1:10)
a11

##  [1] "A1"  "A2"  "A3"  "A4"  "A5"  "A6"  "A7"  "A8"  "A9"  "A10"

a12<-paste("A",1:10,sep="-")
a12

##  [1] "A-1"  "A-2"  "A-3"  "A-4"  "A-5"  "A-6"  "A-7"  "A-8"  "A-9"  "A-10"

a13<-paste0("A",a8)
a13

## [1] "A1" "A1" "A2" "A2" "A3" "A3"

#aksesvector
a2[3]

## [1] "7"

a3[10:15]

## [1] 5.5 6.0 6.5 7.0 7.5 8.0

a10[c(4,7,9)]

## [1] "A4" "A7" "A9"

a13[-c(1:2)]

## [1] "A2" "A2" "A3" "A3"

length(a4)

## [1] 12

Latihan 1

#tentuukan output
c("la","ye")[rep(c(1,2,2,1),times=4)]

##  [1] "la" "ye" "ye" "la" "la" "ye" "ye" "la" "la" "ye" "ye" "la" "la" "ye" "ye"
## [16] "la"

c("la","ye")[rep(rep(1:2,each=3),2)]

##  [1] "la" "la" "la" "ye" "ye" "ye" "la" "la" "la" "ye" "ye" "ye"

Latihan 2

•Buatlah syntaxagar dihasilkan outputvektor sebagai berikut

X1 Y2 X3 Y4 X5 Y6 X7 Y8 X9 Y10

1 4 7 10 13 16 19 22 25 28

lat2a<-(paste0(rep(c("X","Y"),5),1:10))
lat2a

##  [1] "X1"  "Y2"  "X3"  "Y4"  "X5"  "Y6"  "X7"  "Y8"  "X9"  "Y10"

lat2b<-seq(1,28,by=3)
lat2b

##  [1]  1  4  7 10 13 16 19 22 25 28

Matriks

a14 <-1:12
b1<-matrix(a14,3,4)
b1

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

b2 <-matrix(a14,3,4,byrow=TRUE)
b2

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8
## [3,]    9   10   11   12

b3<-matrix(1:10,4,4)

## Warning in matrix(1:10, 4, 4): data length [10] is not a sub-multiple or
## multiple of the number of rows [4]

b3

##      [,1] [,2] [,3] [,4]
## [1,]    1    5    9    3
## [2,]    2    6   10    4
## [3,]    3    7    1    5
## [4,]    4    8    2    6

b4<-matrix(1:10,4,5)
b4

##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    5    9    3    7
## [2,]    2    6   10    4    8
## [3,]    3    7    1    5    9
## [4,]    4    8    2    6   10

b5 <-a14
dim(b5)<-c(6,2) #merubahobjek vectorke matrix

b6 <-matrix(1:4,2)
b7 <-matrix(6:9,2)
b8 <-rbind(b6,b7) #gabungbaris
b9 <-cbind(b7,b6) #gabungkolom
dim(b8)

## [1] 4 2

dim(b9)

## [1] 2 4

dim(a14)

## NULL

length(b3)

## [1] 16

#aksesmatrix
b2

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8
## [3,]    9   10   11   12

b2[2,3]

## [1] 7

b2[2,2:4]

## [1] 6 7 8

b2[1:2,]

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8

b2[c(1,3),-2]

##      [,1] [,2] [,3]
## [1,]    1    3    4
## [2,]    9   11   12

b2[10]

## [1] 4

Array

c1<-array(a14,dim=c(2,2,3))
c2<-array(a14,dim=c(2,1,2,3))
c3 <-array(a14,dim=c(1,2,4,2))
c4 <-array(a14,dim=c(3,4))
#akses array
c2[,,1,] #lembar 1 dari c2

##      [,1] [,2] [,3]
## [1,]    1    5    9
## [2,]    2    6   10

c2[,,,2] #buku ke 2 dari c2

##      [,1] [,2]
## [1,]    5    7
## [2,]    6    8

c2[,,1,3] #lembarke 1 buku ke 3 dari c2

## [1]  9 10

Faktor

a15 <-c("A","B","AB","O")
d1 <-factor(a15) #skala pengukuran nominal
d2 <-factor(a15,levels=c("O","A","B","AB"))
levels(d2)

## [1] "O"  "A"  "B"  "AB"

a16 <-c("SD","SMP","SMA")
d3 <-ordered(a16) #skala pengukuran ordinal
d4 <-ordered(a16, levels=a16)
d5 <-factor(a16, levels=a16, ordered=TRUE)
levels(d4)

## [1] "SD"  "SMP" "SMA"

#aksesfactor
d1[2]

## [1] B
## Levels: A AB B O

d4[2:3]

## [1] SMP SMA
## Levels: SD < SMP < SMA

LIst

a1; b2; c1; d2

## [1] 2 4 7 3

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8
## [3,]    9   10   11   12

## , , 1
## 
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4
## 
## , , 2
## 
##      [,1] [,2]
## [1,]    5    7
## [2,]    6    8
## 
## , , 3
## 
##      [,1] [,2]
## [1,]    9   11
## [2,]   10   12

## [1] A  B  AB O 
## Levels: O A B AB

e1 <-list(a1,b2,c1,d2)
e2 <-list(vect=a1,mat=b2,array=c1,fac=d2)
#akseslist
e1[[1]]

## [1] 2 4 7 3

e2$fac

## [1] A  B  AB O 
## Levels: O A B AB

e2[2]

## $mat
##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8
## [3,]    9   10   11   12

e1[c(2,4)]

## [[1]]
##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8
## [3,]    9   10   11   12
## 
## [[2]]
## [1] A  B  AB O 
## Levels: O A B AB

dim(e2)

## NULL

length(e2)

## [1] 4

names(e2)

## [1] "vect"  "mat"   "array" "fac"

Data Frame

a17 <-11:15
d5 <-factor(LETTERS[6:10])
f1 <-data.frame(d5,a17)
#aksesdata frame
f1[1,2]

## [1] 11

f1[3,]

##   d5 a17
## 3  H  13

f1$d5

## [1] F G H I J
## Levels: F G H I J

f1[,"a17"]

## [1] 11 12 13 14 15

colnames(f1)

## [1] "d5"  "a17"

str(f1)

## 'data.frame':    5 obs. of  2 variables:
##  $ d5 : Factor w/ 5 levels "F","G","H","I",..: 1 2 3 4 5
##  $ a17: int  11 12 13 14 15

summary(f1)

##  d5         a17    
##  F:1   Min.   :11  
##  G:1   1st Qu.:12  
##  H:1   Median :13  
##  I:1   Mean   :13  
##  J:1   3rd Qu.:14  
##        Max.   :15

Pengolahan Objek dan struktur kendali

Pengolahan Objek

x1 <-c(2,6,9,5)
x1

## [1] 2 6 9 5

x2 <-1:4
x2

## [1] 1 2 3 4

x3 <-x1 + 1:2
x2

## [1] 1 2 3 4

x4 <-x1 + 1:4
x4

## [1]  3  8 12  9

x5 <-x1*x2
x5

## [1]  2 12 27 20

x6 <-x1 %*% x1 #setarax'x
x6

##      [,1]
## [1,]  146

x7 <-x1 %o% x1 #setaraxx'
x7

##      [,1] [,2] [,3] [,4]
## [1,]    4   12   18   10
## [2,]   12   36   54   30
## [3,]   18   54   81   45
## [4,]   10   30   45   25

#Operasidasarvektorkarakter:
# nchar(…),paste(…),substr(…),substring(…)
y1 <-c("InstitutPertanianBogor")
n1 <-nchar(y1)
n1

## [1] 22

y2 <-c("Adam","Pramesti","Fathi","Ririn")
n2 <-nchar(y2)
n2

## [1] 4 8 5 5

y3 <-substr(y1,15,18) #”nian”
y3

## [1] "ianB"

y4 <-substring(y1,15) #”nianBogor”
y4

## [1] "ianBogor"

y5 <-substring(y1,4,8) #”titut”
y5

## [1] "titut"

Struktur Kendali

Eksekusibersyarat

•if (kondisi) ekspresielse ekspresi

•ifelse(kondisi,ekspresibenar,ekspresisalah)

•switch(“kondisi”=ekspresi,…)

Pengulangan(loops)

•for (objekin sekuens) ekspresi

•while (kondisi) ekspresi

•repeat ekspresi (untuk menghentikan gunakan perintah break)

Tanpa pengulangan

•apply(array, margin, function, function args)

for (i in 1:5) print(i^2)

## [1] 1
## [1] 4
## [1] 9
## [1] 16
## [1] 25

i<-1
while (i<=5) {
print(i^2)
i=i+1
}

## [1] 1
## [1] 4
## [1] 9
## [1] 16
## [1] 25

y=runif(20)
for (i in y) {
if(i<0.5){
print(100*i)
} else print(i/100)
}

## [1] 0.005389451
## [1] 0.007634479
## [1] 16.19016
## [1] 0.00792545
## [1] 25.63307
## [1] 0.007185275
## [1] 1.682251
## [1] 47.26836
## [1] 0.006863742
## [1] 0.005084901
## [1] 9.741865
## [1] 1.951317
## [1] 33.45788
## [1] 0.007761382
## [1] 29.17358
## [1] 22.4096
## [1] 0.007546317
## [1] 1.540109
## [1] 0.007453097
## [1] 0.009411481

z=0
while(z<=10) {
y=runif(20)
z=sum(y)
print(z)
}

## [1] 9.752191
## [1] 9.478571
## [1] 10.72605

acak <-sample(1:5,1)
switch(EXPR=acak, "1" = "a", "2" = "z",
"3" = "m", "4" = "h", "5" = "t")

## [1] "a"

Z6 <-matrix(1:25,5,5)
apply(Z6,1,sum)

## [1] 55 60 65 70 75

apply(Z6,2,sd)

## [1] 1.581139 1.581139 1.581139 1.581139 1.581139

Latihan

a<-0
for(i in 1:5) { b<-a+i
print(b)
a<-b
}

## [1] 1
## [1] 3
## [1] 6
## [1] 10
## [1] 15

i<-1
z<-1
while(z<15)
{y<-z+i
z<-y
print(z)
i<-i+1
}

## [1] 2
## [1] 4
## [1] 7
## [1] 11
## [1] 16

i<-1
m<-2
repeat
{m<-m+i
print(m)
i<-i+1
if(m>15)
break
}

## [1] 3
## [1] 5
## [1] 8
## [1] 12
## [1] 17

Praktikum Pertemuan 3

Manajemen Data Frame(Data Wrangling/Munging):

• Membuat peubah baru dalam data frame

• Subsetting data

• Sorting data

• Recoding data

• Merging data

• Reshaping data

Data

Seorang peneliti merancang sebuah perancangan percobaan RAKL dengan 4 perlakuan dan 3 kelompok (anggaplah respon percobaan berupa baris bilangan), kemudian disimpan dalam objek data1.

Perl <-paste("P",rep(1:4,each=3),sep="")
Kel<-factor(rep(1:3,4))
Resp<-seq(1,23,by=2)
data1 <-data.frame(Perl,Kel,Resp)
data1

##    Perl Kel Resp
## 1    P1   1    1
## 2    P1   2    3
## 3    P1   3    5
## 4    P2   1    7
## 5    P2   2    9
## 6    P2   3   11
## 7    P3   1   13
## 8    P3   2   15
## 9    P3   3   17
## 10   P4   1   19
## 11   P4   2   21
## 12   P4   3   23

Membuat Peubah Baru dalam Data Frame

•Dilakukan seperti membuat vektor (dengan indeks atau operasi seleksi)

namadataframe$namavariabelbaru<-ekspresi

namadataframe[,nomorkolom]<-ekspresi

Latihan 1

Pada data1, buatlah peubah’baru1’yang berisi nilai dari 12 sampai 1 secara berurutan

data1$baru1<-12:1
data1

##    Perl Kel Resp baru1
## 1    P1   1    1    12
## 2    P1   2    3    11
## 3    P1   3    5    10
## 4    P2   1    7     9
## 5    P2   2    9     8
## 6    P2   3   11     7
## 7    P3   1   13     6
## 8    P3   2   15     5
## 9    P3   3   17     4
## 10   P4   1   19     3
## 11   P4   2   21     2
## 12   P4   3   23     1

Subsetting Data

•Dilakukan untuk akses sebagian data

•Membuat ide logicuntuk diterapkan dalam vektor logic yang diinginkan

•Fungsi yang digunakan : ==, !=, >, >=, <, <=, %in%, duplicated(…),which(…),is.na(…), is.null(…), is.numeric(…), dll…

Latihan 2

Dari data1 tersebut ambillah yang termasuk kelompok 1

indeks1 <-data1$Kel == 1
data2 <-data1[indeks1,]
data2

##    Perl Kel Resp baru1
## 1    P1   1    1    12
## 4    P2   1    7     9
## 7    P3   1   13     6
## 10   P4   1   19     3

Latihan 3

Dari data1tersebut ambillah yang termasuk kelompok 1 atau perlakuan P2

indeks2 <-data1$Kel == 1 | data1$Perl == "P2"
data3 <-data1[indeks2,]
data3

##    Perl Kel Resp baru1
## 1    P1   1    1    12
## 4    P2   1    7     9
## 5    P2   2    9     8
## 6    P2   3   11     7
## 7    P3   1   13     6
## 10   P4   1   19     3

Latihan 4

Dari data1tersebut ambillah amatan yang responnya berupa bilangan prima

indeks3 <-data1$Resp %in% c(2,3,5,7,11,13,17,19,23)
data4 <-data1[indeks3,]
data4

##    Perl Kel Resp baru1
## 2    P1   2    3    11
## 3    P1   3    5    10
## 4    P2   1    7     9
## 6    P2   3   11     7
## 7    P3   1   13     6
## 9    P3   3   17     4
## 10   P4   1   19     3
## 12   P4   3   23     1

Sorting Data

•Dilakukan untuk mengurutkan data berdasarkan beberapa peubahtertentu

•Dilakukan dengan membuat vektor logika untuk melakukan pengurutan data

•Fungsi yang sering digunakan order(…), sort(…), rev(…), unique(…)

Latihan 5

Urutkan data1 tersebut berdasarkan kelompok secara ascending

indeks4 <-order(data1$Kel)
data5 <-data1[indeks4,]
data5

##    Perl Kel Resp baru1
## 1    P1   1    1    12
## 4    P2   1    7     9
## 7    P3   1   13     6
## 10   P4   1   19     3
## 2    P1   2    3    11
## 5    P2   2    9     8
## 8    P3   2   15     5
## 11   P4   2   21     2
## 3    P1   3    5    10
## 6    P2   3   11     7
## 9    P3   3   17     4
## 12   P4   3   23     1

Latihan 6

Urutkan data1tersebut berdasarkan kelompok dan respon secara descending

indeks5 <-order(data1$Kel, data1$Resp, decreasing=TRUE)
data6 <-data1[indeks5,]
data6

##    Perl Kel Resp baru1
## 12   P4   3   23     1
## 9    P3   3   17     4
## 6    P2   3   11     7
## 3    P1   3    5    10
## 11   P4   2   21     2
## 8    P3   2   15     5
## 5    P2   2    9     8
## 2    P1   2    3    11
## 10   P4   1   19     3
## 7    P3   1   13     6
## 4    P2   1    7     9
## 1    P1   1    1    12

Latihan 7

Urutkan data1tersebut berdasarkan kelompok secara ascending dan respon secara descending

indeks6 <-order(data1$Resp, decreasing=TRUE)
data7 <-data1[indeks6,]
indeks7 <-order(data7$Kel)
data8 <-data7[indeks7,]
data8

##    Perl Kel Resp baru1
## 10   P4   1   19     3
## 7    P3   1   13     6
## 4    P2   1    7     9
## 1    P1   1    1    12
## 11   P4   2   21     2
## 8    P3   2   15     5
## 5    P2   2    9     8
## 2    P1   2    3    11
## 12   P4   3   23     1
## 9    P3   3   17     4
## 6    P2   3   11     7
## 3    P1   3    5    10

Sorting Data

data8$Resp

##  [1] 19 13  7  1 21 15  9  3 23 17 11  5

sort(data8$Resp)

##  [1]  1  3  5  7  9 11 13 15 17 19 21 23

rev(data8$Resp)

##  [1]  5 11 17 23  3  9 15 21  1  7 13 19

order(data8$Resp)

##  [1]  4  8 12  3  7 11  2  6 10  1  5  9

rank(data8$Resp)

##  [1] 10  7  4  1 11  8  5  2 12  9  6  3

data8$Resp>10

##  [1]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE

which(data8$Resp>10)

## [1]  1  2  5  6  9 10 11

data8$Resp[data8$Resp>10]

## [1] 19 13 21 15 23 17 11

data8$Resp[which(data8$Resp>10)]

## [1] 19 13 21 15 23 17 11

Recording Data

•Digunakan untuk membuat nilai baru dari nilai peubahyang sudah ada

•Dapatdilakukansecara logical, fungsi ifelse(…), dan fungsi recode(…)

Latihan 8

•Lakukanlah recoding pada data8 untuk variabel respon dengan kondisi jika respon < 15 maka Code = 1, selainnya Code = 0

#dengan logical
data8$Code1 <-0*(data8$Resp>=15) + 1*(data8$Resp<15)
data8$Code1

##  [1] 0 1 1 1 0 0 1 1 0 0 1 1

#denganfungsiifelse
data8$Code2 <-ifelse(data8$Resp<15,1,0)
data8$Code2

##  [1] 0 1 1 1 0 0 1 1 0 0 1 1

#dengan fungsi recode
library(car)

## Loading required package: carData

data8$Code3 <-recode(data8$Resp,'1:14=1; else=0')
data8$Code3

##  [1] 0 1 1 1 0 0 1 1 0 0 1 1

Merging Data

•Bisa dilakukan dengan rbind(…)atau cbind(…)

•Lebih mudah dengan fungsi merge(…)

Latihan 9

Gabungkanlah data1 dengan tabel1 berdasarkan peubah pertamanya

tabel1 <- data.frame(Tr=c("P4","P2","P5"), k1=c(50,100,200))
tabel1

##   Tr  k1
## 1 P4  50
## 2 P2 100
## 3 P5 200

data9<-merge(data1, tabel1, by.x=1, by.y=1, all=FALSE)
data10<-merge(data1, tabel1, by.x="Perl",by.y="Tr",all=TRUE)
data10

##    Perl  Kel Resp baru1  k1
## 1    P1    1    1    12  NA
## 2    P1    2    3    11  NA
## 3    P1    3    5    10  NA
## 4    P2    3   11     7 100
## 5    P2    1    7     9 100
## 6    P2    2    9     8 100
## 7    P3    2   15     5  NA
## 8    P3    3   17     4  NA
## 9    P3    1   13     6  NA
## 10   P4    1   19     3  50
## 11   P4    2   21     2  50
## 12   P4    3   23     1  50
## 13   P5 <NA>   NA    NA 200

Reshaping Data

•Membentuk data baru dengan cara :

•Long towideformat

•Widetolongformat

•Menggunakan fungsi reshape(…)

Latihan 10

Ubahlah data1 menjadi data dengan setiap barisnya merupakan masing-masing perlakuan

data1

##    Perl Kel Resp baru1
## 1    P1   1    1    12
## 2    P1   2    3    11
## 3    P1   3    5    10
## 4    P2   1    7     9
## 5    P2   2    9     8
## 6    P2   3   11     7
## 7    P3   1   13     6
## 8    P3   2   15     5
## 9    P3   3   17     4
## 10   P4   1   19     3
## 11   P4   2   21     2
## 12   P4   3   23     1

#long to wide
data11 <-reshape(data1[,-4], idvar="Perl", timevar="Kel", direction="wide")
data11

##    Perl Resp.1 Resp.2 Resp.3
## 1    P1      1      3      5
## 4    P2      7      9     11
## 7    P3     13     15     17
## 10   P4     19     21     23

#wide to long
data12 <-reshape(data11, idvar="Perl", timevar="Kel", direction="long")
data12

##      Perl Kel Resp.1
## P1.1   P1   1      1
## P2.1   P2   1      7
## P3.1   P3   1     13
## P4.1   P4   1     19
## P1.2   P1   2      3
## P2.2   P2   2      9
## P3.2   P3   2     15
## P4.2   P4   2     21
## P1.3   P1   3      5
## P2.3   P2   3     11
## P3.3   P3   3     17
## P4.3   P4   3     23

Demikian, Terima Kasih

Satria June Adwendi, IPB University, sjadwendi@apps.ipb.ac.id ↩︎