#LOAD PACKAGE
library(stats) #berisi fungsi analisis statistika umum
library(readxl) #untuk membaca data dari file (.xls dan .xlsx)
library(readr) #untuk baca data dalam bentuk (.csv)
library(car) #Analisis lanjutan
## Loading required package: carData
library(agricolae)
## Warning: package 'agricolae' was built under R version 4.3.3
library(tidyr)
#INPUT DATA
datakuis<- read_excel("D:\\AGH 25\\RAK-DATA 1-Kuis.xlsx", sheet=2) #BENTUK UMUM
head(datakuis) #Menampilkan 6 data teratas
## # A tibble: 4 × 7
## Kelompok K0 K1 K2 K3 K4 K5
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 27.7 36.6 37.4 42.2 39.8 42.9
## 2 2 33 33.8 41.2 46 39.5 45.9
## 3 3 26.3 27 45.4 45.4 40.9 43.9
## 4 4 37.7 39 44.6 46.2 44 45.6
str(datakuis)
## tibble [4 × 7] (S3: tbl_df/tbl/data.frame)
## $ Kelompok: num [1:4] 1 2 3 4
## $ K0 : num [1:4] 27.7 33 26.3 37.7
## $ K1 : num [1:4] 36.6 33.8 27 39
## $ K2 : num [1:4] 37.4 41.2 45.4 44.6
## $ K3 : num [1:4] 42.2 46 45.4 46.2
## $ K4 : num [1:4] 39.8 39.5 40.9 44
## $ K5 : num [1:4] 42.9 45.9 43.9 45.6
datakuis$Kelompok <- as.factor(datakuis$Kelompok)
str(datakuis)
## tibble [4 × 7] (S3: tbl_df/tbl/data.frame)
## $ Kelompok: Factor w/ 4 levels "1","2","3","4": 1 2 3 4
## $ K0 : num [1:4] 27.7 33 26.3 37.7
## $ K1 : num [1:4] 36.6 33.8 27 39
## $ K2 : num [1:4] 37.4 41.2 45.4 44.6
## $ K3 : num [1:4] 42.2 46 45.4 46.2
## $ K4 : num [1:4] 39.8 39.5 40.9 44
## $ K5 : num [1:4] 42.9 45.9 43.9 45.6
RAK12 <- pivot_longer(datakuis,
cols=c("K0","K1","K2","K3","K4","K5"),
names_to ="Perlakuan",
values_to = "Hasil")
head(RAK12)
## # A tibble: 6 × 3
## Kelompok Perlakuan Hasil
## <fct> <chr> <dbl>
## 1 1 K0 27.7
## 2 1 K1 36.6
## 3 1 K2 37.4
## 4 1 K3 42.2
## 5 1 K4 39.8
## 6 1 K5 42.9
#Analisis Anova RAKL
hasil_anova <- aov(Hasil ~ Perlakuan + Kelompok, data = RAK12)
summary(hasil_anova)
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan 5 652.7 130.55 14.491 2.75e-05 ***
## Kelompok 3 96.6 32.20 3.574 0.0395 *
## Residuals 15 135.1 9.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Uji lanjut jika signifikan
hasil_ujilanjut <- HSD.test(hasil_anova, "Perlakuan", group = TRUE)
print(hasil_ujilanjut)
## $statistics
## MSerror Df Mean CV MSD
## 9.009 15 39.66667 7.566806 6.895547
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Perlakuan 6 4.594735 0.05
##
## $means
## Hasil std r se Min Max Q25 Q50 Q75
## K0 31.175 5.220073 4 1.50075 26.3 37.7 27.350 30.35 34.175
## K1 34.100 5.188449 4 1.50075 27.0 39.0 32.100 35.20 37.200
## K2 42.150 3.652853 4 1.50075 37.4 45.4 40.250 42.90 44.800
## K3 44.950 1.864582 4 1.50075 42.2 46.2 44.600 45.70 46.050
## K4 41.050 2.056696 4 1.50075 39.5 44.0 39.725 40.35 41.675
## K5 44.575 1.422146 4 1.50075 42.9 45.9 43.650 44.75 45.675
##
## $comparison
## NULL
##
## $groups
## Hasil groups
## K3 44.950 a
## K5 44.575 a
## K2 42.150 a
## K4 41.050 a
## K1 34.100 b
## K0 31.175 b
##
## attr(,"class")
## [1] "group"