---

title: “pertemuan 9” output: html_document date: “2026-04-21” —

# Data
deterjen <- factor(rep(1:4, each = 3))
noda <- factor(rep(1:3, times = 4))
nilai <- c(45,43,51,
           47,46,52,
           48,50,55,
           42,37,49)

data <- data.frame(deterjen, noda, nilai)
data

##    deterjen noda nilai
## 1         1    1    45
## 2         1    2    43
## 3         1    3    51
## 4         2    1    47
## 5         2    2    46
## 6         2    3    52
## 7         3    1    48
## 8         3    2    50
## 9         3    3    55
## 10        4    1    42
## 11        4    2    37
## 12        4    3    49

model <- aov(nilai ~ deterjen + noda, data = data)
summary(model)

##             Df Sum Sq Mean Sq F value  Pr(>F)   
## deterjen     3 110.92   36.97   11.78 0.00631 **
## noda         2 135.17   67.58   21.53 0.00183 **
## Residuals    6  18.83    3.14                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

TukeyHSD(model, "deterjen")

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = nilai ~ deterjen + noda, data = data)
## 
## $deterjen
##          diff         lwr        upr     p adj
## 2-1  2.000000  -3.0076411  7.0076411 0.5514395
## 3-1  4.666667  -0.3409745  9.6743078 0.0658092
## 4-1 -3.666667  -8.6743078  1.3409745 0.1506830
## 3-2  2.666667  -2.3409745  7.6743078 0.3408012
## 4-2 -5.666667 -10.6743078 -0.6590255 0.0299015
## 4-3 -8.333333 -13.3409745 -3.3256922 0.0048171

library(ggplot2)

ggplot(data, aes(x = deterjen, y = nilai)) +
  geom_boxplot(fill = "skyblue") +
  labs(title = "Perbandingan Efektivitas Deterjen",
       x = "Jenis Deterjen",
       y = "Nilai Penghilangan Noda") +
  theme_minimal()

interaction.plot(data$noda, data$deterjen, data$nilai,
                 col = 1:4, lwd = 2,
                 xlab = "Jenis Noda",
                 ylab = "Rata-rata Nilai",
                 trace.label = "Deterjen")

interaction.plot(data$noda, data$deterjen, data$nilai,
                 col = 1:4, lwd = 2,
                 xlab = "Jenis Noda",
                 ylab = "Rata-rata Nilai",
                 trace.label = "Deterjen")

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

rata2 <- data %>%
  group_by(deterjen) %>%
  summarise(mean_nilai = mean(nilai))

ggplot(rata2, aes(x = deterjen, y = mean_nilai)) +
  geom_col(fill = "steelblue") +
  geom_text(aes(label = round(mean_nilai,1)), vjust = -0.5) +
  labs(title = "Rata-rata Efektivitas Deterjen",
       x = "Deterjen",
       y = "Rata-rata Nilai") +
  theme_minimal()

ggplot(data, aes(x = deterjen, y = nilai, fill = noda)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Efektivitas Deterjen pada Setiap Jenis Noda",
       x = "Deterjen",
       y = "Nilai") +
  theme_minimal()