title: “TMKOMSTAT_Vania Dewi Rusyanti” author: “Vania Dewi Rusyanti” date: “2026-11-12” output: html_document
#PACKAGES
library(car)## Loading required package: carDatalibrary(agricolae)
#Memanggil Data
data=read.table(file.choose(), header=T)
data## Merek_Tablet Jenis_Pelarut Data
## 1 1 1 46.18
## 2 1 2 57.69
## 3 1 3 48.61
## 4 2 1 54.54
## 5 2 2 71.57
## 6 2 3 51.01
## 7 3 1 56.63
## 8 3 2 81.51
## 9 3 3 80.02#Pendefinisian Variabel
data$Merek_Tablet=factor(data$Merek_Tablet,levels=1:3,
labels=c("CDR","Redoxon","Enervon-C"))
data$Jenis_Pelarut=factor(data$Jenis_Pelarut,levels=1:3,
labels=c("Air Biasa","Air Soda","Air Kelapa"))
data## Merek_Tablet Jenis_Pelarut Data
## 1 CDR Air Biasa 46.18
## 2 CDR Air Soda 57.69
## 3 CDR Air Kelapa 48.61
## 4 Redoxon Air Biasa 54.54
## 5 Redoxon Air Soda 71.57
## 6 Redoxon Air Kelapa 51.01
## 7 Enervon-C Air Biasa 56.63
## 8 Enervon-C Air Soda 81.51
## 9 Enervon-C Air Kelapa 80.02# Normalitas per Merek Tablet
for(i in levels(data$Merek_Tablet)){
cat("\nUji Normalitas untuk Merek Tablet:", i, "\n")
print(shapiro.test(data$Data[data$Merek_Tablet==i]))
}##
## Uji Normalitas untuk Merek Tablet: CDR
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$Merek_Tablet == i]
## W = 0.89987, p-value = 0.3851
##
##
## Uji Normalitas untuk Merek Tablet: Redoxon
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$Merek_Tablet == i]
## W = 0.87434, p-value = 0.3079
##
##
## Uji Normalitas untuk Merek Tablet: Enervon-C
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$Merek_Tablet == i]
## W = 0.79474, p-value = 0.102# Normalitas per Jenis Pelarut
for(j in levels(data$Jenis_Pelarut)){
cat("\nUji Normalitas untuk Jenis Pelarut:", j, "\n")
print(shapiro.test(data$Data[data$Jenis_Pelarut==j]))
}##
## Uji Normalitas untuk Jenis Pelarut: Air Biasa
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$Jenis_Pelarut == j]
## W = 0.89286, p-value = 0.3631
##
##
## Uji Normalitas untuk Jenis Pelarut: Air Soda
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$Jenis_Pelarut == j]
## W = 0.99096, p-value = 0.8182
##
##
## Uji Normalitas untuk Jenis Pelarut: Air Kelapa
##
## Shapiro-Wilk normality test
##
## data: data$Data[data$Jenis_Pelarut == j]
## W = 0.80695, p-value = 0.1312#Homogenitas variansi#
leveneTest(Data~Merek_Tablet, center=mean, data=data)## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 2 1.9806 0.2185
## 6leveneTest(Data~Jenis_Pelarut, center=mean, data=data)## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 2 2.4902 0.1632
## 6#Uji ANOVA Manual#
anava2arah <- function(y, A, B){
a <- nlevels(A); b <- nlevels(B)
N <- length(y); n <- N/(a*b)
FK <- sum(y)^2/N
JKT <- sum(y^2) - FK
JK_A <- sum(tapply(y, A, sum)^2)/(b*n) - FK
JK_B <- sum(tapply(y, B, sum)^2)/(a*n) - FK
JKG <- JKT - JK_A - JK_B
db <- c(a-1, b-1, N-a-b+1, N-1)
JK <- c(JK_A, JK_B, JKG, JKT)
KT <- JK/db
F <- c(KT[1:2]/KT[3], NA, NA)
p <- c(pf(F[1:2], db[1:2], db[3],
lower.tail=FALSE), NA, NA)
data.frame(Sumber=c("Merek Tablet","Jenis Pelarut",
"Galat","Total"), db, JK, KT, F, p)
}
anava2arah(data$Data, data$Merek_Tablet, data$Jenis_Pelarut)## Sumber db JK KT F p
## 1 Merek Tablet 2 733.9193 366.95964 6.528748 0.05499073
## 2 Jenis Pelarut 2 479.9575 239.97874 4.269572 0.10176166
## 3 Galat 4 224.8270 56.20674 NA NA
## 4 Total 8 1438.7038 179.83797 NA NA#Uji ANOVA Menggunakan Fungsi R#
uji_anava=aov(Data~Merek_Tablet+Jenis_Pelarut,data=data)
anova(uji_anava)## Analysis of Variance Table
##
## Response: Data
## Df Sum Sq Mean Sq F value Pr(>F)
## Merek_Tablet 2 733.92 366.96 6.5287 0.05499 .
## Jenis_Pelarut 2 479.96 239.98 4.2696 0.10176
## Residuals 4 224.83 56.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1