SVM

library(mlbench)

## Warning: package 'mlbench' was built under R version 4.4.3

library(e1071)
library(class)
library(rpart)

library(caret)

## Warning: package 'caret' was built under R version 4.4.3

## Loading required package: ggplot2

## Loading required package: lattice

library(gridExtra) # Untuk menggabungkan plot

## Warning: package 'gridExtra' was built under R version 4.4.3

# Ambil data
data(PimaIndiansDiabetes)
df <- PimaIndiansDiabetes

# Pisah training & testing
set.seed(42)
index <- createDataPartition(df$diabetes, p = 0.7, list = FALSE)
train <- df[index, ]
test <- df[-index, ]

# --- Model 1: SVM ---
svm_model <- svm(diabetes ~ ., data = train, kernel = "radial")
svm_pred <- predict(svm_model, newdata = test)
svm_acc <- mean(svm_pred == test$diabetes)

# --- Model 2: KNN (k = 5) ---
knn_pred <- knn(train = train[, -9], test = test[, -9],
                cl = train$diabetes, k = 5)
knn_acc <- mean(knn_pred == test$diabetes)

# --- Model 3: Decision Tree ---
dt_model <- rpart(diabetes ~ ., data = train, method = "class")
dt_pred <- predict(dt_model, test, type = "class")
dt_acc <- mean(dt_pred == test$diabetes)

# --- Evaluasi ---
cat("Akurasi SVM         :", round(svm_acc * 100, 2), "%\n")

## Akurasi SVM         : 76.09 %

cat("Akurasi KNN (k = 5) :", round(knn_acc * 100, 2), "%\n")

## Akurasi KNN (k = 5) : 70 %

cat("Akurasi Decision Tree:", round(dt_acc * 100, 2), "%\n")

## Akurasi Decision Tree: 73.91 %

library(ggplot2)
library(gridExtra)

# Membuat dataframe untuk visualisasi
results <- data.frame(
  Actual = test$diabetes,
  SVM = svm_pred,
  KNN = knn_pred,
  DecisionTree = dt_pred
)

# Mengubah kolom menjadi faktor untuk keperluan visualisasi
results$Actual <- factor(results$Actual, levels = c("neg", "pos"))
results$SVM <- factor(results$SVM, levels = c("neg", "pos"))
results$KNN <- factor(results$KNN, levels = c("neg", "pos"))
results$DecisionTree <- factor(results$DecisionTree, levels = c("neg", "pos"))

# Visualisasi untuk SVM dengan angka di atas batang
svm_plot <- ggplot(results, aes(x = Actual, fill = SVM)) +
  geom_bar(position = "dodge") +
  geom_text(stat = "count", aes(label = ..count..), position = position_dodge(0.8), vjust = -0.5) +
  labs(title = "Prediksi SVM", x = "Aktual", y = "Frekuensi") +
  scale_fill_manual(values = c("red", "blue"))

# Visualisasi untuk KNN dengan angka di atas batang
knn_plot <- ggplot(results, aes(x = Actual, fill = KNN)) +
  geom_bar(position = "dodge") +
  geom_text(stat = "count", aes(label = ..count..), position = position_dodge(0.8), vjust = -0.5) +
  labs(title = "Prediksi KNN (k = 5)", x = "Aktual", y = "Frekuensi") +
  scale_fill_manual(values = c("red", "blue"))

# Visualisasi untuk Decision Tree dengan angka di atas batang
dt_plot <- ggplot(results, aes(x = Actual, fill = DecisionTree)) +
  geom_bar(position = "dodge") +
  geom_text(stat = "count", aes(label = ..count..), position = position_dodge(0.8), vjust = -0.5) +
  labs(title = "Prediksi Decision Tree", x = "Aktual", y = "Frekuensi") +
  scale_fill_manual(values = c("red", "blue"))

# Gabungkan plot menggunakan gridExtra
grid.arrange(svm_plot, knn_plot, dt_plot, ncol = 3)

## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Interpretasi SVM

• Benar negatif (TN): 131
• Salah positif (FP): 19
• Salah negatif (FN): 36
• Benar positif (TP): 44

SVM sangat kuat dalam mengklasifikasikan kasus negatif (131 dari 150 benar). Namun, masih banyak salah deteksi pada kasus positif (36 salah dari 80 kasus).

KNN - Benar negatif (TN): 112 - Salah positif (FP): 38 - Salah negatif (FN): 31 - Benar positif (TP): 49

Decision Tree - Benar negatif (TN): 119 - Salah positif (FP): 31 - Salah negatif (FN): 29 - Benar positif (TP): 51