## ---------------------------
## KLASIFIKASI TRANSMISI MOBIL DENGAN KNN
## Dataset: mtcars (32 mobil)
## Variabel Target: am (0=automatic, 1=manual)
## ---------------------------
# 1. Memuat Paket dan Data
library(class) # Untuk algoritma KNN
## Warning: package 'class' was built under R version 4.4.3
library(ggplot2) # Untuk visualisasi
library(gridExtra) # Untuk menyusun plot
## Warning: package 'gridExtra' was built under R version 4.4.3
# Memuat dataset mtcars lengkap (32 observasi)
data(mtcars)
cat("Jumlah mobil dalam dataset:", nrow(mtcars), "\n")
## Jumlah mobil dalam dataset: 32
# 2. Menampilkan Seluruh Data
cat("\n## Seluruh Data Mobil ##\n")
##
## ## Seluruh Data Mobil ##
print(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# 3. Persiapan Data
# Mengubah target variable menjadi factor
mtcars$am <- factor(mtcars$am, levels = c(0, 1), labels = c("Automatic", "Manual"))
# Normalisasi fitur (min-max scaling)
normalize <- function(x) {
(x - min(x)) / (max(x) - min(x))
}
# Normalisasi fitur yang dipilih
features <- c("mpg", "hp", "wt")
mtcars_norm <- as.data.frame(lapply(mtcars[, features], normalize))
mtcars_norm$am <- mtcars$am
mtcars_norm$car_name <- row.names(mtcars) # Menyimpan nama mobil
# 4. Membagi Data (80% training, 20% testing)
set.seed(123)
train_idx <- sample(1:nrow(mtcars_norm), 0.8 * nrow(mtcars_norm))
train_data <- mtcars_norm[train_idx, ]
test_data <- mtcars_norm[-train_idx, ]
cat("\n## Mobil dalam Training Set ##\n")
##
## ## Mobil dalam Training Set ##
print(train_data[, c("car_name", "am")])
## car_name am
## 31 Maserati Bora Manual
## 15 Cadillac Fleetwood Automatic
## 19 Honda Civic Manual
## 14 Merc 450SLC Automatic
## 3 Datsun 710 Manual
## 10 Merc 280 Automatic
## 18 Fiat 128 Manual
## 22 Dodge Challenger Automatic
## 11 Merc 280C Automatic
## 5 Hornet Sportabout Automatic
## 20 Toyota Corolla Manual
## 29 Ford Pantera L Manual
## 23 AMC Javelin Automatic
## 30 Ferrari Dino Manual
## 9 Merc 230 Automatic
## 28 Lotus Europa Manual
## 8 Merc 240D Automatic
## 27 Porsche 914-2 Manual
## 7 Duster 360 Automatic
## 32 Volvo 142E Manual
## 26 Fiat X1-9 Manual
## 17 Chrysler Imperial Automatic
## 4 Hornet 4 Drive Automatic
## 1 Mazda RX4 Manual
## 24 Camaro Z28 Automatic
cat("\n## Mobil dalam Test Set ##\n")
##
## ## Mobil dalam Test Set ##
print(test_data[, c("car_name", "am")])
## car_name am
## 2 Mazda RX4 Wag Manual
## 6 Valiant Automatic
## 12 Merc 450SE Automatic
## 13 Merc 450SL Automatic
## 16 Lincoln Continental Automatic
## 21 Toyota Corona Automatic
## 25 Pontiac Firebird Automatic
# 5. Model KNN
k <- 3
knn_pred <- knn(
train = train_data[, features],
test = test_data[, features],
cl = train_data$am,
k = k
)
# 6. Evaluasi Model
conf_matrix <- table(Predicted = knn_pred, Actual = test_data$am)
accuracy <- sum(diag(conf_matrix)) / sum(conf_matrix)
cat("\n## Confusion Matrix ##\n")
##
## ## Confusion Matrix ##
print(conf_matrix)
## Actual
## Predicted Automatic Manual
## Automatic 5 0
## Manual 1 1
cat("\n## Akurasi Model:", round(accuracy * 100, 2), "% ##\n")
##
## ## Akurasi Model: 85.71 % ##
# 7. Visualisasi Hasil
# Plot 1: MPG vs HP
p1 <- ggplot(mtcars_norm, aes(x = mpg, y = hp, color = am)) +
geom_point(size = 3) +
geom_point(data = test_data, aes(x = mpg, y = hp, shape = knn_pred),
size = 5, stroke = 1.5) +
scale_shape_manual(values = c(4, 3)) +
labs(title = "Klasifikasi Berdasarkan MPG dan HP",
subtitle = paste("Akurasi:", round(accuracy * 100, 2), "%"),
x = "MPG (Normalized)",
y = "Horsepower (Normalized)") +
theme_minimal()
# Plot 2: MPG vs Weight
p2 <- ggplot(mtcars_norm, aes(x = mpg, y = wt, color = am)) +
geom_point(size = 3) +
geom_point(data = test_data, aes(x = mpg, y = wt, shape = knn_pred),
size = 5, stroke = 1.5) +
scale_shape_manual(values = c(4, 3)) +
labs(title = "Klasifikasi Berdasarkan MPG dan Weight",
x = "MPG (Normalized)",
y = "Weight (Normalized)") +
theme_minimal()
# Menampilkan kedua plot
grid.arrange(p1, p2, ncol = 2)
# 8. Prediksi Mobil Baru
new_car <- data.frame(
mpg = (24 - min(mtcars$mpg)) / (max(mtcars$mpg) - min(mtcars$mpg)),
hp = (120 - min(mtcars$hp)) / (max(mtcars$hp) - min(mtcars$hp)),
wt = (2.8 - min(mtcars$wt)) / (max(mtcars$wt) - min(mtcars$wt))
)
pred_am <- knn(
train = train_data[, features],
test = new_car,
cl = train_data$am,
k = k
)
cat("\n## Prediksi untuk Mobil Baru ##\n")
##
## ## Prediksi untuk Mobil Baru ##
cat("Spesifikasi Normalisasi:\n")
## Spesifikasi Normalisasi:
print(new_car)
## mpg hp wt
## 1 0.5787234 0.2402827 0.3290718
cat("\nPrediksi Transmisi:", as.character(pred_am), "\n")
##
## Prediksi Transmisi: Manual
# 9. Tabel Ringkasan Mobil
cat("\n## Ringkasan Data Mobil ##\n")
##
## ## Ringkasan Data Mobil ##
mtcars_summary <- cbind(mtcars[, c("mpg", "hp", "wt", "am")],
Predicted = ifelse(row.names(mtcars) %in% test_data$car_name,
as.character(knn_pred), "Training"))
print(mtcars_summary)
## mpg hp wt am Predicted
## Mazda RX4 21.0 110 2.620 Manual Training
## Mazda RX4 Wag 21.0 110 2.875 Manual Automatic
## Datsun 710 22.8 93 2.320 Manual Training
## Hornet 4 Drive 21.4 110 3.215 Automatic Training
## Hornet Sportabout 18.7 175 3.440 Automatic Training
## Valiant 18.1 105 3.460 Automatic Manual
## Duster 360 14.3 245 3.570 Automatic Training
## Merc 240D 24.4 62 3.190 Automatic Training
## Merc 230 22.8 95 3.150 Automatic Training
## Merc 280 19.2 123 3.440 Automatic Training
## Merc 280C 17.8 123 3.440 Automatic Training
## Merc 450SE 16.4 180 4.070 Automatic Automatic
## Merc 450SL 17.3 180 3.730 Automatic Manual
## Merc 450SLC 15.2 180 3.780 Automatic Training
## Cadillac Fleetwood 10.4 205 5.250 Automatic Training
## Lincoln Continental 10.4 215 5.424 Automatic Automatic
## Chrysler Imperial 14.7 230 5.345 Automatic Training
## Fiat 128 32.4 66 2.200 Manual Training
## Honda Civic 30.4 52 1.615 Manual Training
## Toyota Corolla 33.9 65 1.835 Manual Training
## Toyota Corona 21.5 97 2.465 Automatic Automatic
## Dodge Challenger 15.5 150 3.520 Automatic Training
## AMC Javelin 15.2 150 3.435 Automatic Training
## Camaro Z28 13.3 245 3.840 Automatic Training
## Pontiac Firebird 19.2 175 3.845 Automatic Automatic
## Fiat X1-9 27.3 66 1.935 Manual Training
## Porsche 914-2 26.0 91 2.140 Manual Training
## Lotus Europa 30.4 113 1.513 Manual Training
## Ford Pantera L 15.8 264 3.170 Manual Training
## Ferrari Dino 19.7 175 2.770 Manual Training
## Maserati Bora 15.0 335 3.570 Manual Training
## Volvo 142E 21.4 109 2.780 Manual Training
# 10. Visualisasi Prediksi Mobil Baru (Tambahan)
# Plot 1: MPG vs HP dengan Mobil Baru
p1_new <- ggplot(mtcars_norm, aes(x = mpg, y = hp, color = am)) +
geom_point(size = 3, alpha = 0.7) +
geom_point(data = test_data, aes(x = mpg, y = hp, shape = knn_pred),
size = 5, stroke = 1.5) +
geom_point(data = data.frame(new_car), aes(x = mpg, y = hp),
color = "red", size = 6, shape = 18) + # Diamond shape for new car
geom_text(data = data.frame(new_car), aes(x = mpg, y = hp, label = "Mobil Baru"),
color = "black", vjust = -1.5, fontface = "bold") +
scale_shape_manual(values = c(4, 3)) + # Shapes for test data
labs(title = "Klasifikasi MPG vs HP dengan Mobil Baru",
subtitle = paste("Prediksi:", as.character(pred_am)),
x = "MPG (Normalized)",
y = "Horsepower (Normalized)") +
theme_minimal() +
theme(legend.position = "bottom")
# Plot 2: MPG vs Weight dengan Mobil Baru
p2_new <- ggplot(mtcars_norm, aes(x = mpg, y = wt, color = am)) +
geom_point(size = 3, alpha = 0.7) +
geom_point(data = test_data, aes(x = mpg, y = wt, shape = knn_pred),
size = 5, stroke = 1.5) +
geom_point(data = data.frame(new_car), aes(x = mpg, y = wt),
color = "red", size = 6, shape = 18) + # Diamond shape for new car
geom_text(data = data.frame(new_car), aes(x = mpg, y = wt, label = "Mobil Baru"),
color = "black", vjust = -1.5, fontface = "bold") +
scale_shape_manual(values = c(4, 3)) + # Shapes for test data
labs(title = "Klasifikasi MPG vs Weight dengan Mobil Baru",
subtitle = paste("Prediksi:", as.character(pred_am)),
x = "MPG (Normalized)",
y = "Weight (Normalized)") +
theme_minimal() +
theme(legend.position = "bottom")
# Menampilkan plot khusus untuk mobil baru
grid.arrange(p1_new, p2_new, ncol = 2)
