Backup Fix
Denan
2023-12-03
Input Library
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## ✔ modeldata 1.4.0 ✔ workflows 1.1.4
## ✔ parsnip 1.2.1 ✔ workflowsets 1.1.0
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## +.gg ggplot2Untuk membuat model menggunakan random forest, langkah pertama adalah load data dari excel
Input Data
data <- read.csv("tugas sainsdata 23.csv", sep = ";", header = TRUE)
data$jenis.kelamin <- as.factor(data$jenis.kelamin)
data$pendidikan <- as.factor(data$pendidikan)
data$promo <- as.factor(data$promo)
data$cabang <- as.factor(data$cabang)Mengubah variabel respon menjadi faktor dan menghilangkan kolom ID
Data Eksplorasi
Statistika Deskriptif
library(skimr)## Warning: package 'skimr' was built under R version 4.3.2##
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## partitionskim_without_charts(data = data)| Name | data |
| Number of rows | 358 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 10 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| cabang | 0 | 1 | FALSE | 14 | 9: 36, 3: 32, 10: 32, 7: 31 |
| jenis.kelamin | 0 | 1 | FALSE | 2 | 2: 206, 1: 152 |
| pendidikan | 0 | 1 | FALSE | 4 | 3: 156, 4: 119, 2: 71, 1: 12 |
| promo | 0 | 1 | FALSE | 2 | 0: 239, 1: 119 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| no.sampel | 0 | 1 | 179.50 | 103.49 | 1.00 | 90.25 | 179.50 | 268.75 | 358.00 |
| usia | 0 | 1 | 40.13 | 5.07 | 26.00 | 37.00 | 40.00 | 44.00 | 56.00 |
| frekuensi.fashion | 0 | 1 | 3.06 | 1.52 | 0.00 | 2.00 | 3.00 | 4.00 | 8.00 |
| nilai.fashion | 0 | 1 | 0.79 | 0.52 | 0.03 | 0.40 | 0.68 | 1.05 | 2.63 |
| frekuensi.footwear | 0 | 1 | 3.07 | 1.63 | 0.00 | 2.00 | 3.00 | 4.00 | 8.00 |
| nilai.footwear | 0 | 1 | 0.85 | 0.61 | 0.03 | 0.41 | 0.69 | 1.11 | 3.55 |
| frekuensi.lainnya | 0 | 1 | 2.78 | 1.49 | 0.00 | 2.00 | 3.00 | 4.00 | 7.00 |
| nilai.lainnya | 0 | 1 | 0.85 | 0.58 | 0.02 | 0.43 | 0.73 | 1.11 | 3.03 |
| total.nilai.tunai | 0 | 1 | 2.24 | 3.43 | 0.00 | 0.00 | 0.68 | 3.04 | 23.02 |
| lama.member | 0 | 1 | 25.85 | 14.41 | 1.00 | 14.00 | 25.00 | 38.00 | 51.00 |
Pengamatan:
Jumlah baris adalah 358 dan jumlah kolom (variabel) adalah 13, yang terdiri dari 10 variabel bertipe numerik, 3 variabel factor, dan 1 variabel (Y) bertipe factor.
Dari semua variabel, teramati bahwa tidak terdapat variabel yang mengandung missing value.
Untuk variabel respon Y tampaknya terjadi class imbalance, dengan jumlah nilai 0 sebesar 239 dan jumlah nilai 0 sebesar 119.
Berdasarkan nilai minimum, sepertinya tidak ada nilai yang aneh (misal mengandung nilai negatif padahal konteks variabelnya adalah durasi,penghasilan, frekuensi, dan persentase) pada variabel X1 hingga X11.
Beberapa prediktor mengandung nilai 0.
Visualisasi Data
plot_intro(data = data,
ggtheme = theme_classic(),
theme_config = list(axis.line=element_blank(),
axis.ticks=element_blank(),
axis.text.x=element_blank(),
axis.title=element_blank()
)
)
Duplikat Data
any(duplicated(data))## [1] FALSEAnalisis Univariat
Visualisasi Peubah Respon
a <- data %>%
count(promo) %>%
mutate(percent=n*100/sum(n),label=str_c(round(percent,2),"%")) %>%
ggplot(aes(x="",y=n,fill=promo))+
geom_col()+
geom_text(aes(label = label),
position = position_stack(vjust = 0.5)) +
coord_polar(theta = "y")+
theme_cowplot()
ggsave("Imbalance.png", a)## Saving 7 x 5 in imageData Imbalanced
b. KDE, Histogram, Boxplot Tiap Prediktor
# KDE
a <- plot_density(data,
ggtheme = theme_light(),
geom_density_args = list("fill" = "blue", "alpha" = 0.6),
ncol=3)
Boxplot
# Boxplot tiap Prediktor
tmp <- par(mfrow=c(3,4))
names <- colnames(data[2:12])
for(i in names){
boxplot(data[[i]], horizontal=T, col='red')
title(i)
}
par(tmp)
Terdapat banyak pencilan pada nilai fashion, nilai footwear, nilai lainnya, dan total nilai tunai
Area Plot
x1_y <- ggplot(data, aes(x = cabang, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Cabang vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x1_y
x2_y<- ggplot(data, aes(x = jenis.kelamin, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Jenis Kelamin vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x2_y
x3_y<- ggplot(data, aes(x = pendidikan, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Pendidikan vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x3_y
x4_y<- ggplot(data, aes(x = frekuensi.fashion, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Frekuensi Fashion vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x4_y
x5_y<- ggplot(data, aes(x = frekuensi.footwear, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Frekuensi Footwear vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x5_y
x6_y<- ggplot(data, aes(x = nilai.footwear, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Nilai Footwear vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x6_y
x7_y<- ggplot(data, aes(x = frekuensi.lainnya, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Frekuensi Lainnya vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x7_y
x8_y<- ggplot(data, aes(x = nilai.lainnya, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Nilai Lainnya vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x8_y
x9_y<- ggplot(data, aes(x = total.nilai.tunai, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Total Nilai Tunai vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x9_y
x10_y<- ggplot(data, aes(x = lama.member, fill = promo, color = promo)) +
geom_density(kernel = "gaussian", color= "black", alpha = 0.8) +
labs(title = "Lama Member vs Promo") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_cowplot()
x10_y
col_panggilan <- colnames(data)
#ggpairs(data, columns=col_panggilan, aes(color = promo,alpha = 0.5))Analisis multivariat
Boxplot Multivariat
plot_boxplot(data = data[-1],by = "promo",
ggtheme = theme_classic(),
geom_boxplot_args = list(fill='red'))
boxplot(data[[3]], data$promo, horizontal=T, col='red')
Analisis Korelasi
all_numeric_predictor <- data[c(-1,-2,-3,-5)]
# Heatmap Corrrelation Pearson
plot_correlation(data = all_numeric_predictor,
type = "all",
cor_args = list(method="pearson"),
ggtheme = theme_classic(),
theme_config = list(legend.position = "none",
axis.text.x=element_text(angle = 90)))
Yang berpengaruh cabang, usia, nilai Footwear, frekuensi footwear, total
nilai tunai, lama member
Yang berpengaruh Pearson: cabang, usia, nilai footwear, frekuensi footwear, lama member Yang berpengaruh Spearman: cabang, usia, nilai Footwear, frekuensi footwear, total nilai tunai, lama member
Data Pra-Processing
Data 1: Anomali dan Feature Extraction
Anomali Data
data_new <- data %>%
filter(((frekuensi.fashion > 0 & nilai.fashion ==0) |
(frekuensi.fashion == 0 & nilai.fashion > 0)) |
((frekuensi.footwear > 0 & nilai.footwear ==0) |
(frekuensi.footwear == 0 & nilai.footwear > 0)) |
((frekuensi.lainnya > 0 & nilai.lainnya ==0) |
(frekuensi.lainnya == 0 & nilai.lainnya > 0)))
data_new## no.sampel cabang jenis.kelamin usia pendidikan frekuensi.fashion
## 1 21 1 1 41 2 2
## 2 27 10 2 40 3 0
## 3 49 14 2 28 3 0
## 4 52 14 2 46 3 0
## 5 64 9 2 35 3 3
## 6 94 14 2 45 4 1
## 7 104 2 1 44 2 3
## 8 112 1 1 39 4 0
## 9 137 3 2 40 4 0
## 10 142 3 2 38 2 1
## 11 143 10 2 32 3 0
## 12 153 13 2 37 3 0
## 13 172 9 2 41 2 2
## 14 179 11 1 41 2 0
## 15 181 13 2 44 4 1
## 16 195 3 2 33 3 2
## 17 202 10 1 36 4 3
## 18 217 5 1 44 1 0
## 19 231 5 1 32 4 1
## 20 236 12 1 38 2 0
## 21 243 6 1 35 3 0
## 22 259 7 2 39 3 1
## 23 279 3 2 39 2 1
## 24 284 3 2 43 3 3
## 25 287 7 2 34 4 1
## 26 290 6 2 48 2 0
## 27 291 8 1 44 4 4
## 28 310 3 2 45 4 0
## 29 313 8 1 56 3 4
## 30 320 1 1 47 2 0
## 31 324 13 2 46 4 4
## 32 353 12 2 42 3 2
## 33 355 7 2 37 4 2
## nilai.fashion frekuensi.footwear nilai.footwear frekuensi.lainnya
## 1 0.6352 0 0.6734 2
## 2 0.9158 3 0.6022 0
## 3 1.4866 4 0.5018 0
## 4 0.9998 5 1.0418 0
## 5 0.6970 0 0.5288 1
## 6 0.1066 0 0.0644 1
## 7 1.0230 0 2.4298 3
## 8 0.0898 2 0.3694 0
## 9 1.0758 3 0.6992 0
## 10 1.9944 5 1.6942 0
## 11 0.5164 2 2.5292 0
## 12 0.4632 4 0.9636 0
## 13 0.6418 3 2.4346 0
## 14 0.3296 5 0.5542 0
## 15 0.5928 1 2.7520 0
## 16 0.9008 0 1.2710 2
## 17 1.3390 0 1.3960 3
## 18 0.6590 4 0.4756 0
## 19 0.1780 1 1.7278 0
## 20 0.4886 5 0.9914 0
## 21 0.3790 5 0.3320 0
## 22 0.4528 4 0.4224 0
## 23 0.3000 0 0.8818 0
## 24 0.8956 0 0.2238 3
## 25 0.5258 4 0.3660 0
## 26 0.1528 3 0.3956 0
## 27 0.0676 0 1.2780 4
## 28 0.3364 3 2.7402 0
## 29 0.2934 0 0.0874 4
## 30 0.0592 2 0.2044 0
## 31 0.5120 0 0.9438 3
## 32 0.3298 0 1.6796 1
## 33 0.6226 0 0.7206 2
## nilai.lainnya total.nilai.tunai lama.member promo
## 1 0.9780 2.68 7 0
## 2 1.8056 1.06 33 0
## 3 0.5484 0.00 14 0
## 4 0.2828 2.68 50 0
## 5 1.0264 2.57 44 0
## 6 0.3904 0.07 19 0
## 7 0.8418 2.60 17 0
## 8 1.1160 0.00 28 0
## 9 2.5776 0.17 33 0
## 10 0.6570 0.00 15 1
## 11 0.7262 4.82 41 0
## 12 1.0144 1.95 33 0
## 13 0.8766 7.18 32 0
## 14 1.6476 2.02 1 1
## 15 1.5186 0.00 34 0
## 16 1.8024 2.97 25 0
## 17 1.0828 0.00 16 1
## 18 2.6500 0.00 21 1
## 19 1.3642 1.68 37 1
## 20 1.4048 3.91 21 0
## 21 0.1914 0.50 8 1
## 22 0.6282 0.00 14 1
## 23 0.6910 0.23 3 0
## 24 0.0898 1.68 12 0
## 25 0.3554 0.40 32 1
## 26 1.0428 0.00 4 1
## 27 0.2660 0.61 47 0
## 28 1.2704 6.59 29 0
## 29 1.6668 3.03 5 0
## 30 0.8228 0.00 12 0
## 31 0.9848 4.63 23 0
## 32 0.5102 0.29 45 0
## 33 0.6518 1.83 5 1data2<- anti_join(data, data_new)## Joining with `by = join_by(no.sampel, cabang, jenis.kelamin, usia, pendidikan,
## frekuensi.fashion, nilai.fashion, frekuensi.footwear, nilai.footwear,
## frekuensi.lainnya, nilai.lainnya, total.nilai.tunai, lama.member, promo)`Feature Extraction: Semua Frekuensi Pembelian
data2$Total_Frekuensi_Pembelian <- data2$frekuensi.fashion + data2$frekuensi.lainnya+ data2$frekuensi.footwearplot_boxplot(data = data2[-1],by = "promo",
ggtheme = theme_classic(),
geom_boxplot_args = list(fill='red'))
a <- ggplot(data = data2, aes(x= frekuensi.lainnya, y = promo))+
geom_boxplot(fill = "red") + theme_classic()
ggsave("Boxplot FE 3.png", a)## Saving 7 x 5 in imageall_numeric_predictor <- data2[c(-1,-2,-3,-5,-6,-8,-10)]
# Heatmap Corrrelation Pearson
a <- plot_correlation(data = all_numeric_predictor,
type = "all",
cor_args = list(method="pearson"),
ggtheme = theme_classic(),
theme_config = list(legend.position = "none",
axis.text.x=element_text(angle = 90)))
ggsave("Korelasi.png", a)## Saving 7 x 5 in imageData 2: Anomali dan Feature Extraction
Anomali Data
data3 <- datadata_new <- data %>%
filter(((frekuensi.fashion > 0 & nilai.fashion ==0) |
(frekuensi.fashion == 0 & nilai.fashion > 0)) |
((frekuensi.footwear > 0 & nilai.footwear ==0) |
(frekuensi.footwear == 0 & nilai.footwear > 0)) |
((frekuensi.lainnya > 0 & nilai.lainnya ==0) |
(frekuensi.lainnya == 0 & nilai.lainnya > 0)))
data_new## no.sampel cabang jenis.kelamin usia pendidikan frekuensi.fashion
## 1 21 1 1 41 2 2
## 2 27 10 2 40 3 0
## 3 49 14 2 28 3 0
## 4 52 14 2 46 3 0
## 5 64 9 2 35 3 3
## 6 94 14 2 45 4 1
## 7 104 2 1 44 2 3
## 8 112 1 1 39 4 0
## 9 137 3 2 40 4 0
## 10 142 3 2 38 2 1
## 11 143 10 2 32 3 0
## 12 153 13 2 37 3 0
## 13 172 9 2 41 2 2
## 14 179 11 1 41 2 0
## 15 181 13 2 44 4 1
## 16 195 3 2 33 3 2
## 17 202 10 1 36 4 3
## 18 217 5 1 44 1 0
## 19 231 5 1 32 4 1
## 20 236 12 1 38 2 0
## 21 243 6 1 35 3 0
## 22 259 7 2 39 3 1
## 23 279 3 2 39 2 1
## 24 284 3 2 43 3 3
## 25 287 7 2 34 4 1
## 26 290 6 2 48 2 0
## 27 291 8 1 44 4 4
## 28 310 3 2 45 4 0
## 29 313 8 1 56 3 4
## 30 320 1 1 47 2 0
## 31 324 13 2 46 4 4
## 32 353 12 2 42 3 2
## 33 355 7 2 37 4 2
## nilai.fashion frekuensi.footwear nilai.footwear frekuensi.lainnya
## 1 0.6352 0 0.6734 2
## 2 0.9158 3 0.6022 0
## 3 1.4866 4 0.5018 0
## 4 0.9998 5 1.0418 0
## 5 0.6970 0 0.5288 1
## 6 0.1066 0 0.0644 1
## 7 1.0230 0 2.4298 3
## 8 0.0898 2 0.3694 0
## 9 1.0758 3 0.6992 0
## 10 1.9944 5 1.6942 0
## 11 0.5164 2 2.5292 0
## 12 0.4632 4 0.9636 0
## 13 0.6418 3 2.4346 0
## 14 0.3296 5 0.5542 0
## 15 0.5928 1 2.7520 0
## 16 0.9008 0 1.2710 2
## 17 1.3390 0 1.3960 3
## 18 0.6590 4 0.4756 0
## 19 0.1780 1 1.7278 0
## 20 0.4886 5 0.9914 0
## 21 0.3790 5 0.3320 0
## 22 0.4528 4 0.4224 0
## 23 0.3000 0 0.8818 0
## 24 0.8956 0 0.2238 3
## 25 0.5258 4 0.3660 0
## 26 0.1528 3 0.3956 0
## 27 0.0676 0 1.2780 4
## 28 0.3364 3 2.7402 0
## 29 0.2934 0 0.0874 4
## 30 0.0592 2 0.2044 0
## 31 0.5120 0 0.9438 3
## 32 0.3298 0 1.6796 1
## 33 0.6226 0 0.7206 2
## nilai.lainnya total.nilai.tunai lama.member promo
## 1 0.9780 2.68 7 0
## 2 1.8056 1.06 33 0
## 3 0.5484 0.00 14 0
## 4 0.2828 2.68 50 0
## 5 1.0264 2.57 44 0
## 6 0.3904 0.07 19 0
## 7 0.8418 2.60 17 0
## 8 1.1160 0.00 28 0
## 9 2.5776 0.17 33 0
## 10 0.6570 0.00 15 1
## 11 0.7262 4.82 41 0
## 12 1.0144 1.95 33 0
## 13 0.8766 7.18 32 0
## 14 1.6476 2.02 1 1
## 15 1.5186 0.00 34 0
## 16 1.8024 2.97 25 0
## 17 1.0828 0.00 16 1
## 18 2.6500 0.00 21 1
## 19 1.3642 1.68 37 1
## 20 1.4048 3.91 21 0
## 21 0.1914 0.50 8 1
## 22 0.6282 0.00 14 1
## 23 0.6910 0.23 3 0
## 24 0.0898 1.68 12 0
## 25 0.3554 0.40 32 1
## 26 1.0428 0.00 4 1
## 27 0.2660 0.61 47 0
## 28 1.2704 6.59 29 0
## 29 1.6668 3.03 5 0
## 30 0.8228 0.00 12 0
## 31 0.9848 4.63 23 0
## 32 0.5102 0.29 45 0
## 33 0.6518 1.83 5 1data3 <- anti_join(data3, data_new)## Joining with `by = join_by(no.sampel, cabang, jenis.kelamin, usia, pendidikan,
## frekuensi.fashion, nilai.fashion, frekuensi.footwear, nilai.footwear,
## frekuensi.lainnya, nilai.lainnya, total.nilai.tunai, lama.member, promo)`Feature Extraction: Semua Frekuensi Pembelian
data3$Total_Frekuensi_Pembelian <- data3$frekuensi.fashion + data3$frekuensi.lainnya + data3$frekuensi.footweartest
diskretasi_usia <- function(){
for (i in 1:length(data3$usia)){
x <- data3$usia[i]
if (x>=25 && x < 36){
x<- "1"
}
else if (x>= 36 && x < 46){
x <- "2"
}
else if (x >= 46){
x <- "3"
}
data3[i,4] <- x
}
data3$usia <- as.factor(data3$usia)
return(data3)
}
diskretasi_member <- function(b){
for (i in 1:length(b[,13])){
x <- b[i,13]
if (x>=1 && x < 13){
x<- "1"
}
else if (x>= 13 && x < 25){
x <- "2"
}
else if (x>= 25 && x < 37){
x <- "3"
}
else if (x>= 37 && x < 49){
x <- "4"
}
else if (x>= 49){
x <- "5"
}
b[i,13] <- x
}
b[,13] <- as.factor(b[,13])
return(b)
}data3_diskret <- diskretasi_usia()
data3_diskret2 <- diskretasi_member(data3_diskret)Feature Extraction: Pembelian Non Tunai
Data 3: Anomali dan Feature Extraction
Anomali Data
data4 <- datadata4 <- anti_join(data4,data_new)## Joining with `by = join_by(no.sampel, cabang, jenis.kelamin, usia, pendidikan,
## frekuensi.fashion, nilai.fashion, frekuensi.footwear, nilai.footwear,
## frekuensi.lainnya, nilai.lainnya, total.nilai.tunai, lama.member, promo)`data4$Total_Frekuensi_Pembelian <- data4$frekuensi.fashion + data4$frekuensi.lainnya + data4$frekuensi.footweardata4$Total_Rata_Pembelian <- data4$nilai.fashion + data4$nilai.lainnya + data4$nilai.footweardata_nontunai <- data4 %>%
filter(((frekuensi.fashion > 0 | nilai.fashion >0)|
(frekuensi.footwear > 0 | nilai.footwear > 0) |
((frekuensi.lainnya > 0 | nilai.lainnya > 0))) & total.nilai.tunai == 0)
data_nontunai## no.sampel cabang jenis.kelamin usia pendidikan frekuensi.fashion
## 1 1 11 2 38 2 5
## 2 4 2 1 43 3 6
## 3 8 9 1 42 3 5
## 4 12 1 2 44 4 3
## 5 16 2 2 46 2 4
## 6 23 6 2 40 3 5
## 7 24 6 2 41 3 3
## 8 30 9 1 39 1 2
## 9 31 9 2 40 3 3
## 10 33 2 1 46 4 6
## 11 34 7 1 37 3 4
## 12 36 12 2 43 3 3
## 13 39 14 1 30 3 3
## 14 41 6 1 42 3 1
## 15 44 14 2 44 3 4
## 16 47 8 1 40 4 3
## 17 48 7 1 35 3 5
## 18 51 5 2 43 3 3
## 19 54 7 1 37 2 1
## 20 57 2 2 42 2 3
## 21 58 9 1 40 3 2
## 22 59 5 2 36 4 4
## 23 61 2 1 40 2 4
## 24 63 12 1 36 3 2
## 25 65 7 1 34 4 2
## 26 66 6 1 42 4 3
## 27 67 14 2 39 3 4
## 28 70 14 1 43 3 3
## 29 71 14 1 43 4 3
## 30 72 1 2 38 3 4
## 31 73 12 2 44 3 3
## 32 74 14 2 39 3 5
## 33 75 10 2 42 3 5
## 34 77 7 2 33 4 4
## 35 79 10 1 45 4 1
## 36 80 12 1 32 3 1
## 37 82 14 1 47 4 2
## 38 88 14 1 42 4 3
## 39 89 7 2 40 4 3
## 40 90 5 1 45 1 3
## 41 92 7 2 44 3 5
## 42 93 1 2 35 3 4
## 43 96 3 2 49 4 3
## 44 97 13 2 35 3 1
## 45 98 9 2 43 4 2
## 46 99 3 2 41 4 6
## 47 100 12 2 40 3 4
## 48 101 9 2 46 3 2
## 49 103 4 1 37 2 4
## 50 105 4 2 41 2 3
## 51 106 10 1 34 1 4
## 52 111 11 2 33 1 1
## 53 113 11 2 36 2 6
## 54 114 8 1 40 2 3
## 55 117 3 1 43 2 3
## 56 118 14 1 40 4 4
## 57 121 7 1 45 3 3
## 58 123 5 2 41 3 1
## 59 126 12 1 31 4 3
## 60 128 4 1 43 4 5
## 61 131 5 1 42 3 5
## 62 138 8 2 34 4 3
## 63 139 1 1 37 3 3
## 64 141 10 2 32 2 2
## 65 149 2 1 41 4 4
## 66 150 11 2 30 4 1
## 67 151 3 2 44 3 6
## 68 154 2 2 47 4 4
## 69 158 9 1 46 4 5
## 70 162 7 1 41 2 4
## 71 164 9 1 37 4 3
## 72 165 10 1 36 4 4
## 73 169 3 1 38 3 4
## 74 170 12 2 35 3 4
## 75 174 8 1 33 4 4
## 76 178 5 1 31 2 3
## 77 183 14 1 37 3 3
## 78 184 9 2 45 2 2
## 79 185 7 1 35 3 2
## 80 188 11 2 44 3 3
## 81 189 5 2 37 4 1
## 82 193 4 1 36 2 4
## 83 200 8 2 43 2 4
## 84 201 10 2 38 3 5
## 85 203 5 1 36 3 3
## 86 209 9 2 37 4 5
## 87 211 6 2 35 4 3
## 88 215 3 2 49 4 2
## 89 216 10 1 49 4 3
## 90 218 9 1 45 4 1
## 91 221 14 1 40 3 5
## 92 223 14 2 46 3 7
## 93 224 13 2 46 4 3
## 94 226 12 2 38 1 1
## 95 227 8 2 42 4 1
## 96 230 3 1 42 3 2
## 97 235 3 2 50 2 2
## 98 238 5 1 50 4 5
## 99 242 12 1 44 4 2
## 100 248 8 2 35 4 4
## 101 249 2 2 40 2 3
## 102 251 4 2 45 2 2
## 103 252 2 2 31 3 5
## 104 254 5 1 34 4 1
## 105 256 13 1 30 4 3
## 106 257 11 2 44 3 3
## 107 258 10 2 35 2 2
## 108 263 3 2 40 3 3
## 109 264 9 2 36 4 5
## 110 267 9 2 38 3 2
## 111 270 1 2 42 4 2
## 112 271 13 2 39 3 4
## 113 274 1 1 50 2 5
## 114 275 12 2 37 4 1
## 115 280 9 1 39 4 3
## 116 285 9 2 34 2 2
## 117 286 9 1 48 1 3
## 118 294 1 1 35 2 4
## 119 295 8 1 40 4 2
## 120 296 12 1 29 3 4
## 121 301 7 1 46 2 2
## 122 302 8 2 46 2 3
## 123 304 14 2 42 3 3
## 124 311 13 2 37 3 2
## 125 317 7 1 37 4 5
## 126 319 10 2 35 4 4
## 127 323 7 2 39 4 4
## 128 327 7 1 29 3 1
## 129 332 12 2 37 2 3
## 130 334 11 2 37 3 1
## 131 335 12 1 38 3 5
## 132 339 5 2 42 3 4
## 133 344 7 2 38 4 4
## 134 350 8 1 40 2 4
## 135 352 1 2 42 2 2
## 136 354 12 1 45 4 4
## 137 356 13 2 42 3 4
## nilai.fashion frekuensi.footwear nilai.footwear frekuensi.lainnya
## 1 1.1588 1 0.4560 4
## 2 0.3612 3 0.5626 6
## 3 0.3972 3 0.2280 4
## 4 0.3032 3 0.4152 3
## 5 0.6580 2 0.6720 4
## 6 0.9162 3 2.8932 5
## 7 1.6652 2 1.1082 3
## 8 0.2320 1 0.4656 1
## 9 1.1172 3 0.6006 3
## 10 0.3978 5 0.5438 5
## 11 0.8578 7 1.1914 4
## 12 0.7474 6 0.8420 3
## 13 0.7080 2 1.1704 3
## 14 0.2584 4 0.5526 1
## 15 0.4498 3 0.9920 3
## 16 0.1692 3 1.2122 3
## 17 0.4014 6 0.3272 4
## 18 0.5848 2 0.7110 3
## 19 0.1878 4 0.2702 1
## 20 0.7874 6 0.2848 3
## 21 1.2244 1 0.3646 2
## 22 0.1094 3 0.0508 4
## 23 2.0776 2 1.7100 4
## 24 1.1672 4 1.3962 1
## 25 0.7948 2 1.4732 2
## 26 0.6052 1 0.8180 2
## 27 0.4274 3 0.7756 3
## 28 0.4596 3 0.0864 2
## 29 0.2938 3 0.8572 3
## 30 0.2436 4 0.7096 2
## 31 1.8560 1 0.4080 3
## 32 0.8322 5 3.5494 5
## 33 1.0726 4 0.8582 5
## 34 0.1618 2 1.2094 4
## 35 0.2162 1 0.4944 1
## 36 0.7378 4 0.6298 1
## 37 0.0824 2 0.5744 2
## 38 1.6262 3 0.7242 3
## 39 1.1430 2 1.5044 3
## 40 2.2716 2 1.4642 2
## 41 0.8796 2 0.7912 5
## 42 2.2006 2 0.5302 4
## 43 1.4324 2 1.7754 2
## 44 0.4670 5 0.0250 1
## 45 0.8562 8 0.3494 2
## 46 2.3006 3 0.5554 6
## 47 0.3060 5 0.6344 4
## 48 0.7802 4 0.6124 1
## 49 0.6044 2 0.8074 4
## 50 0.6628 1 1.0388 3
## 51 0.5060 2 0.4764 4
## 52 1.4026 2 1.8544 1
## 53 0.3620 5 0.3708 5
## 54 1.0576 1 0.5406 3
## 55 0.8782 3 0.5402 3
## 56 1.4614 5 2.2678 4
## 57 1.0432 4 0.8360 3
## 58 0.5476 3 1.1274 1
## 59 0.9770 3 0.7792 2
## 60 1.0212 4 0.3192 5
## 61 0.5828 2 0.4592 5
## 62 0.9144 5 1.3064 3
## 63 0.6008 1 1.1428 2
## 64 0.6992 5 0.3702 2
## 65 0.6912 6 0.3332 4
## 66 0.5144 3 1.1400 1
## 67 0.7406 4 0.9764 6
## 68 1.2490 3 0.4326 4
## 69 0.3942 1 0.7114 4
## 70 0.9178 3 0.1720 4
## 71 0.5144 4 0.4716 3
## 72 0.0710 7 0.3342 4
## 73 1.1700 7 2.2276 4
## 74 1.0124 2 1.0558 4
## 75 0.3012 4 0.4540 4
## 76 1.3982 1 0.8708 3
## 77 0.4218 3 1.2106 3
## 78 0.8540 4 1.5140 2
## 79 0.0436 4 0.7572 2
## 80 0.7434 2 0.4764 2
## 81 0.0728 1 0.6946 1
## 82 0.7052 4 0.6468 3
## 83 0.3538 2 0.3870 3
## 84 0.9758 3 0.4550 5
## 85 1.3438 2 1.0136 1
## 86 1.1160 2 0.4304 5
## 87 0.8078 2 0.3832 3
## 88 1.4352 4 0.6002 2
## 89 1.0656 3 1.5228 2
## 90 0.3946 4 0.1444 1
## 91 0.7428 4 0.2388 4
## 92 0.9844 3 0.8712 7
## 93 1.7836 2 0.3928 3
## 94 0.5232 3 0.8160 1
## 95 0.2030 2 0.5932 1
## 96 0.1906 4 0.8268 2
## 97 0.1494 5 0.2928 2
## 98 0.6392 1 0.2736 5
## 99 0.7520 7 1.3686 2
## 100 0.5990 2 0.6540 4
## 101 0.7084 2 0.4616 3
## 102 0.3924 2 0.2432 2
## 103 0.3202 4 1.7544 5
## 104 1.5422 7 0.6236 1
## 105 1.4190 3 0.7962 3
## 106 0.7750 3 1.1862 3
## 107 0.2876 2 0.3852 1
## 108 2.5078 5 0.9228 3
## 109 1.0838 4 1.6454 5
## 110 0.0578 4 0.2680 2
## 111 0.8828 2 0.5552 2
## 112 0.9876 2 0.1514 4
## 113 0.5220 5 1.6352 4
## 114 1.4922 1 0.4800 1
## 115 0.6192 4 0.2198 3
## 116 1.4020 7 1.3114 2
## 117 0.3608 6 0.4002 3
## 118 0.4302 3 1.5724 3
## 119 0.9484 4 1.3998 2
## 120 0.1806 3 0.3066 4
## 121 0.7358 2 0.6044 2
## 122 1.9708 4 1.9294 3
## 123 0.9964 3 1.1062 3
## 124 0.7728 3 0.9014 2
## 125 1.3142 3 0.8154 5
## 126 1.5568 3 0.8174 4
## 127 0.4994 1 0.7226 2
## 128 0.3730 5 2.0604 1
## 129 0.9584 6 0.3758 3
## 130 1.0348 2 0.9032 1
## 131 2.3752 1 0.2236 4
## 132 2.2716 1 1.5996 4
## 133 0.4612 1 0.3452 4
## 134 0.0334 2 0.4210 4
## 135 0.2580 3 1.1848 2
## 136 0.1342 4 0.8448 4
## 137 1.7268 1 0.2176 4
## nilai.lainnya total.nilai.tunai lama.member promo Total_Frekuensi_Pembelian
## 1 0.9990 0 18 0 10
## 2 0.4118 0 9 1 15
## 3 1.2490 0 27 0 12
## 4 0.3550 0 31 1 9
## 5 0.7420 0 1 1 10
## 6 0.2850 0 48 1 13
## 7 1.8380 0 35 1 8
## 8 0.0480 0 12 0 4
## 9 1.0814 0 6 0 9
## 10 2.8132 0 30 0 16
## 11 0.5278 0 28 1 15
## 12 0.6830 0 20 1 12
## 13 1.0532 0 25 0 8
## 14 1.4986 0 18 1 6
## 15 2.1182 0 10 0 10
## 16 0.8732 0 28 0 9
## 17 0.4470 0 49 1 15
## 18 0.9744 0 29 0 8
## 19 0.7448 0 13 1 6
## 20 0.4536 0 45 0 12
## 21 1.3284 0 47 0 5
## 22 0.5374 0 26 0 11
## 23 0.6404 0 39 1 10
## 24 1.3454 0 35 0 7
## 25 0.5674 0 46 0 6
## 26 1.3472 0 33 0 6
## 27 1.0402 0 42 0 10
## 28 0.4000 0 14 0 8
## 29 0.7194 0 5 0 9
## 30 0.5942 0 27 0 10
## 31 0.6004 0 19 0 7
## 32 1.0716 0 4 1 15
## 33 0.7236 0 45 0 14
## 34 1.0066 0 24 1 10
## 35 0.3732 0 40 0 3
## 36 0.6504 0 12 0 6
## 37 0.1142 0 29 0 6
## 38 1.2540 0 5 0 9
## 39 0.4814 0 19 1 8
## 40 1.8594 0 38 1 7
## 41 0.2466 0 15 1 12
## 42 0.1704 0 6 0 10
## 43 0.5376 0 8 0 7
## 44 0.9202 0 33 0 7
## 45 0.6810 0 34 0 12
## 46 0.0490 0 45 0 15
## 47 0.1832 0 15 0 13
## 48 0.8670 0 14 1 7
## 49 1.0746 0 30 0 10
## 50 0.1270 0 45 0 7
## 51 0.7240 0 10 0 10
## 52 1.7536 0 48 0 4
## 53 0.3156 0 15 0 16
## 54 0.1662 0 35 0 7
## 55 0.8304 0 42 0 9
## 56 0.7800 0 32 0 13
## 57 1.8014 0 19 1 10
## 58 0.3632 0 46 0 5
## 59 0.8736 0 44 0 8
## 60 0.6992 0 12 0 14
## 61 0.8610 0 45 0 12
## 62 0.8978 0 4 1 11
## 63 0.6906 0 2 0 6
## 64 0.2854 0 37 0 9
## 65 1.9450 0 6 1 14
## 66 0.7660 0 19 0 5
## 67 1.2314 0 35 0 16
## 68 1.2746 0 23 0 11
## 69 1.3002 0 32 1 10
## 70 3.0334 0 30 1 11
## 71 0.4834 0 10 0 10
## 72 1.4188 0 10 1 15
## 73 0.8754 0 5 1 15
## 74 1.3694 0 18 1 10
## 75 0.9494 0 18 0 12
## 76 1.1386 0 49 1 7
## 77 0.4022 0 38 0 9
## 78 0.5362 0 29 1 8
## 79 0.7624 0 46 1 8
## 80 0.5888 0 15 0 7
## 81 0.6954 0 12 0 3
## 82 0.7014 0 28 0 11
## 83 1.4008 0 9 0 9
## 84 0.8116 0 32 0 13
## 85 0.5770 0 7 0 6
## 86 1.1758 0 3 1 12
## 87 0.4026 0 16 1 8
## 88 1.2034 0 10 1 8
## 89 0.6324 0 46 1 8
## 90 0.7576 0 25 0 6
## 91 0.5100 0 21 0 13
## 92 0.2802 0 48 0 17
## 93 0.9560 0 29 0 8
## 94 1.3788 0 17 1 5
## 95 0.5876 0 19 0 4
## 96 0.5198 0 50 0 8
## 97 0.4562 0 32 1 9
## 98 0.8706 0 13 1 11
## 99 1.4190 0 19 0 11
## 100 1.8842 0 43 0 10
## 101 1.3964 0 44 0 8
## 102 0.2264 0 23 0 6
## 103 1.3592 0 47 1 14
## 104 0.8686 0 5 0 9
## 105 0.7694 0 15 1 9
## 106 1.1726 0 5 1 9
## 107 0.4818 0 4 0 5
## 108 0.7604 0 18 0 11
## 109 0.3928 0 29 0 14
## 110 1.1828 0 21 0 8
## 111 0.1646 0 17 0 6
## 112 1.0740 0 43 0 10
## 113 1.9836 0 22 0 14
## 114 1.3348 0 22 1 3
## 115 0.2798 0 19 0 10
## 116 1.0980 0 28 0 11
## 117 0.8948 0 42 0 12
## 118 0.8962 0 6 0 10
## 119 1.1850 0 37 0 8
## 120 2.9106 0 19 0 11
## 121 0.1858 0 25 1 6
## 122 0.1458 0 35 1 10
## 123 0.5982 0 22 0 9
## 124 0.7122 0 32 0 7
## 125 0.1956 0 2 1 13
## 126 3.0118 0 47 0 11
## 127 0.6204 0 18 0 7
## 128 0.5358 0 46 1 7
## 129 1.2062 0 23 0 12
## 130 0.2020 0 8 0 4
## 131 1.0824 0 4 0 10
## 132 0.4750 0 50 0 9
## 133 0.4640 0 2 1 9
## 134 1.7718 0 15 0 10
## 135 1.0504 0 4 1 7
## 136 0.0224 0 51 0 12
## 137 0.8938 0 45 0 9
## Total_Rata_Pembelian
## 1 2.6138
## 2 1.3356
## 3 1.8742
## 4 1.0734
## 5 2.0720
## 6 4.0944
## 7 4.6114
## 8 0.7456
## 9 2.7992
## 10 3.7548
## 11 2.5770
## 12 2.2724
## 13 2.9316
## 14 2.3096
## 15 3.5600
## 16 2.2546
## 17 1.1756
## 18 2.2702
## 19 1.2028
## 20 1.5258
## 21 2.9174
## 22 0.6976
## 23 4.4280
## 24 3.9088
## 25 2.8354
## 26 2.7704
## 27 2.2432
## 28 0.9460
## 29 1.8704
## 30 1.5474
## 31 2.8644
## 32 5.4532
## 33 2.6544
## 34 2.3778
## 35 1.0838
## 36 2.0180
## 37 0.7710
## 38 3.6044
## 39 3.1288
## 40 5.5952
## 41 1.9174
## 42 2.9012
## 43 3.7454
## 44 1.4122
## 45 1.8866
## 46 2.9050
## 47 1.1236
## 48 2.2596
## 49 2.4864
## 50 1.8286
## 51 1.7064
## 52 5.0106
## 53 1.0484
## 54 1.7644
## 55 2.2488
## 56 4.5092
## 57 3.6806
## 58 2.0382
## 59 2.6298
## 60 2.0396
## 61 1.9030
## 62 3.1186
## 63 2.4342
## 64 1.3548
## 65 2.9694
## 66 2.4204
## 67 2.9484
## 68 2.9562
## 69 2.4058
## 70 4.1232
## 71 1.4694
## 72 1.8240
## 73 4.2730
## 74 3.4376
## 75 1.7046
## 76 3.4076
## 77 2.0346
## 78 2.9042
## 79 1.5632
## 80 1.8086
## 81 1.4628
## 82 2.0534
## 83 2.1416
## 84 2.2424
## 85 2.9344
## 86 2.7222
## 87 1.5936
## 88 3.2388
## 89 3.2208
## 90 1.2966
## 91 1.4916
## 92 2.1358
## 93 3.1324
## 94 2.7180
## 95 1.3838
## 96 1.5372
## 97 0.8984
## 98 1.7834
## 99 3.5396
## 100 3.1372
## 101 2.5664
## 102 0.8620
## 103 3.4338
## 104 3.0344
## 105 2.9846
## 106 3.1338
## 107 1.1546
## 108 4.1910
## 109 3.1220
## 110 1.5086
## 111 1.6026
## 112 2.2130
## 113 4.1408
## 114 3.3070
## 115 1.1188
## 116 3.8114
## 117 1.6558
## 118 2.8988
## 119 3.5332
## 120 3.3978
## 121 1.5260
## 122 4.0460
## 123 2.7008
## 124 2.3864
## 125 2.3252
## 126 5.3860
## 127 1.8424
## 128 2.9692
## 129 2.5404
## 130 2.1400
## 131 3.6812
## 132 4.3462
## 133 1.2704
## 134 2.2262
## 135 2.4932
## 136 1.0014
## 137 2.8382data5 <- anti_join(data4,data_nontunai)## Joining with `by = join_by(no.sampel, cabang, jenis.kelamin, usia, pendidikan,
## frekuensi.fashion, nilai.fashion, frekuensi.footwear, nilai.footwear,
## frekuensi.lainnya, nilai.lainnya, total.nilai.tunai, lama.member, promo,
## Total_Frekuensi_Pembelian, Total_Rata_Pembelian)`data5## no.sampel cabang jenis.kelamin usia pendidikan frekuensi.fashion
## 1 2 2 2 33 2 4
## 2 3 7 1 41 3 5
## 3 5 3 2 37 4 2
## 4 6 4 2 39 3 3
## 5 7 11 2 41 3 4
## 6 9 12 1 45 3 4
## 7 10 13 1 52 3 2
## 8 11 3 2 43 3 5
## 9 13 5 1 41 2 4
## 10 14 14 2 50 3 3
## 11 15 10 1 42 3 2
## 12 17 10 1 44 3 2
## 13 18 9 2 38 4 2
## 14 19 14 1 46 3 4
## 15 20 2 1 40 4 3
## 16 22 10 1 47 4 4
## 17 25 3 1 37 4 4
## 18 26 3 2 39 4 3
## 19 28 9 1 28 3 4
## 20 29 2 1 45 1 5
## 21 32 12 2 37 2 2
## 22 35 9 2 32 4 4
## 23 37 7 1 40 2 2
## 24 38 8 2 47 2 1
## 25 40 7 1 46 3 2
## 26 42 10 2 38 2 4
## 27 43 4 1 33 3 2
## 28 45 4 2 48 3 5
## 29 46 6 1 28 4 1
## 30 50 1 1 44 3 5
## 31 53 10 2 34 4 5
## 32 55 13 1 37 2 2
## 33 56 10 2 39 4 5
## 34 60 4 2 38 3 1
## 35 62 1 1 41 4 1
## 36 68 5 2 42 2 3
## 37 69 2 2 42 4 1
## 38 76 10 2 38 2 4
## 39 78 6 1 40 4 2
## 40 81 9 1 40 3 4
## 41 83 14 1 44 3 3
## 42 84 6 1 39 3 1
## 43 85 14 1 45 3 5
## 44 86 7 2 39 3 3
## 45 87 10 1 38 1 1
## 46 91 8 2 48 2 4
## 47 95 3 1 36 3 2
## 48 102 11 2 39 3 4
## 49 107 3 2 31 4 3
## 50 108 2 2 46 4 4
## 51 109 11 2 39 3 2
## 52 110 3 2 42 2 5
## 53 115 1 1 49 4 1
## 54 116 9 2 36 3 5
## 55 119 4 2 41 3 3
## 56 120 6 2 32 3 3
## 57 122 4 2 35 4 5
## 58 124 8 1 45 3 1
## 59 125 9 2 35 4 1
## 60 127 10 2 42 2 2
## 61 129 3 1 31 2 5
## 62 130 13 2 38 4 4
## 63 132 1 2 44 4 4
## 64 133 10 1 38 3 3
## 65 134 11 1 43 3 5
## 66 135 4 2 39 1 4
## 67 136 11 2 44 3 4
## 68 140 13 2 45 3 3
## 69 144 11 2 38 4 4
## 70 145 6 1 41 4 3
## 71 146 10 1 38 3 4
## 72 147 13 1 43 3 5
## 73 148 9 2 32 3 4
## 74 152 12 2 32 4 2
## 75 155 11 1 44 2 4
## 76 156 6 2 34 3 1
## 77 157 13 2 34 3 3
## 78 159 10 2 38 3 4
## 79 160 1 2 37 4 4
## 80 161 11 2 41 3 2
## 81 163 12 2 48 3 6
## 82 166 12 1 38 3 5
## 83 167 14 2 39 2 2
## 84 168 7 2 46 3 3
## 85 171 10 1 37 3 1
## 86 173 9 1 37 3 3
## 87 175 11 2 40 3 4
## 88 176 9 1 49 2 2
## 89 177 9 2 47 3 6
## 90 180 10 2 46 3 3
## 91 182 7 2 43 3 4
## 92 186 9 2 41 4 2
## 93 187 12 2 40 3 3
## 94 190 1 1 39 4 5
## 95 191 8 1 43 2 5
## 96 192 8 2 40 3 5
## 97 194 5 2 45 2 5
## 98 196 13 2 38 2 7
## 99 197 13 1 43 4 3
## 100 198 5 1 38 3 5
## 101 199 5 2 34 4 2
## 102 204 2 2 43 3 2
## 103 205 12 1 41 4 6
## 104 206 10 2 49 4 4
## 105 207 1 1 52 2 2
## 106 208 3 1 47 2 4
## 107 210 12 2 45 4 1
## 108 212 8 2 50 4 4
## 109 213 12 2 37 3 2
## 110 214 2 1 35 4 1
## 111 219 9 2 40 3 5
## 112 220 13 2 42 4 2
## 113 222 2 2 37 2 1
## 114 225 14 1 38 4 4
## 115 228 10 1 41 2 3
## 116 229 6 2 47 3 5
## 117 232 5 1 43 3 3
## 118 233 1 1 37 3 3
## 119 234 9 2 30 4 2
## 120 237 9 2 45 3 3
## 121 239 14 2 38 3 4
## 122 240 13 1 45 4 2
## 123 241 12 2 42 3 7
## 124 244 10 1 41 3 3
## 125 245 9 1 40 3 4
## 126 246 4 1 40 2 4
## 127 247 4 2 41 2 8
## 128 250 5 2 41 4 5
## 129 253 4 2 50 3 2
## 130 255 6 2 41 3 4
## 131 260 8 2 42 3 2
## 132 261 6 2 43 3 3
## 133 262 3 1 54 2 3
## 134 265 5 1 45 3 5
## 135 266 4 2 43 3 4
## 136 268 6 2 37 3 2
## 137 269 7 1 35 4 1
## 138 272 4 2 43 3 1
## 139 273 4 2 47 3 3
## 140 276 14 2 36 4 1
## 141 277 5 1 41 4 5
## 142 278 6 2 44 2 3
## 143 281 5 2 30 4 2
## 144 282 10 2 44 3 3
## 145 283 3 2 35 4 3
## 146 288 14 2 39 2 1
## 147 289 4 1 39 4 3
## 148 292 7 1 37 2 1
## 149 293 2 1 42 4 5
## 150 297 13 2 42 2 5
## 151 298 2 2 44 2 3
## 152 299 9 2 45 3 7
## 153 300 4 2 51 3 1
## 154 303 3 2 36 3 1
## 155 305 7 2 36 3 3
## 156 306 4 2 39 3 2
## 157 307 3 1 37 2 2
## 158 308 3 2 40 3 4
## 159 309 12 2 39 3 2
## 160 312 14 1 35 4 2
## 161 314 1 2 35 3 5
## 162 315 5 1 46 2 2
## 163 316 9 1 38 4 2
## 164 318 5 1 36 4 4
## 165 321 7 2 32 1 1
## 166 322 13 1 37 3 5
## 167 325 8 2 38 4 4
## 168 326 3 2 37 4 4
## 169 328 8 2 40 3 3
## 170 329 12 1 33 4 3
## 171 330 8 2 42 4 5
## 172 331 3 2 50 3 5
## 173 333 4 1 36 4 2
## 174 336 9 2 37 3 3
## 175 337 14 1 52 3 2
## 176 338 1 2 40 4 2
## 177 340 7 2 38 4 3
## 178 341 7 2 40 3 3
## 179 342 10 2 40 3 4
## 180 343 1 1 41 3 2
## 181 345 4 2 35 4 2
## 182 346 7 2 37 2 3
## 183 347 4 2 36 4 6
## 184 348 10 1 30 4 3
## 185 349 12 1 51 3 4
## 186 351 3 1 26 1 3
## 187 357 1 1 43 4 3
## 188 358 13 2 45 3 2
## nilai.fashion frekuensi.footwear nilai.footwear frekuensi.lainnya
## 1 0.4964 1 0.9162 4
## 2 0.6008 1 0.0384 4
## 3 0.6572 3 0.1014 1
## 4 0.3894 2 0.2674 3
## 5 1.0230 6 1.1034 4
## 6 1.3172 3 0.8162 2
## 7 2.2226 1 2.0606 2
## 8 0.0566 2 0.6240 5
## 9 0.3160 4 0.9674 4
## 10 1.3258 2 0.1734 3
## 11 0.4534 4 0.5120 2
## 12 0.3632 4 0.8950 2
## 13 0.3916 5 0.2436 2
## 14 0.6452 1 0.8936 4
## 15 2.6276 2 2.4224 3
## 16 1.2012 1 0.7414 4
## 17 0.3396 3 0.3994 4
## 18 0.7618 3 0.4500 3
## 19 0.7848 3 0.7248 4
## 20 1.1748 5 0.8424 5
## 21 0.8108 1 0.9608 1
## 22 0.5366 3 0.8378 4
## 23 0.2558 3 1.5328 2
## 24 0.4054 3 0.6040 1
## 25 0.7342 3 0.0678 2
## 26 0.9574 2 0.3834 3
## 27 0.8312 3 0.0918 2
## 28 1.4842 3 1.6854 5
## 29 0.1166 2 0.8956 1
## 30 0.8812 5 0.5268 4
## 31 0.7514 2 0.7062 5
## 32 0.8472 3 0.3804 1
## 33 0.9188 3 0.2412 4
## 34 0.4186 1 1.0326 1
## 35 0.6080 6 2.1264 1
## 36 0.7610 3 0.6270 3
## 37 0.1620 3 0.6238 1
## 38 1.3980 4 1.0162 3
## 39 0.4590 1 0.6240 2
## 40 0.6330 5 0.7566 4
## 41 0.5388 1 1.5922 2
## 42 0.1296 3 0.3474 1
## 43 0.3400 8 1.9844 4
## 44 0.8304 5 0.4448 2
## 45 0.4600 2 0.1770 1
## 46 1.8744 3 0.9792 3
## 47 0.3940 3 0.5824 2
## 48 0.2186 3 1.2810 3
## 49 0.4082 3 0.0812 3
## 50 1.2380 4 0.3366 4
## 51 0.6294 2 0.5926 2
## 52 1.1454 5 0.6360 4
## 53 0.8804 2 0.4266 1
## 54 0.6694 2 0.1146 5
## 55 0.4748 3 1.1652 3
## 56 0.2250 6 0.2378 2
## 57 0.2012 7 0.6118 4
## 58 0.3678 2 0.4492 1
## 59 0.4078 1 0.3610 1
## 60 0.5706 5 0.4228 2
## 61 1.0592 3 0.9524 5
## 62 0.4648 3 0.4874 4
## 63 1.8310 3 0.5380 1
## 64 0.2178 2 1.1318 3
## 65 1.1836 4 1.0800 3
## 66 0.6136 3 2.2986 3
## 67 0.6268 3 0.4458 4
## 68 0.4804 3 0.8894 3
## 69 0.7324 4 1.0140 4
## 70 1.7468 1 0.4926 3
## 71 0.2702 6 1.2278 4
## 72 2.1268 3 0.8562 5
## 73 0.6314 3 1.1274 3
## 74 0.8290 4 0.4852 2
## 75 1.5322 4 0.5442 4
## 76 0.4980 3 1.1466 1
## 77 0.2708 3 0.3360 3
## 78 0.3660 2 0.9248 3
## 79 0.2474 3 0.2372 3
## 80 1.7790 4 0.6650 2
## 81 0.7540 1 0.2812 4
## 82 0.8986 2 0.5356 5
## 83 1.0564 1 0.7418 1
## 84 0.5976 4 1.5304 2
## 85 1.2262 3 1.6790 1
## 86 0.3568 2 3.0260 3
## 87 0.9808 3 0.2978 4
## 88 0.1730 2 0.1484 1
## 89 2.4374 2 0.5256 5
## 90 1.0552 2 0.5750 2
## 91 0.3686 2 0.3594 3
## 92 1.3294 2 0.7192 2
## 93 0.6842 6 1.3918 3
## 94 1.2764 1 0.6838 5
## 95 0.3400 3 0.3058 5
## 96 0.9326 3 0.9474 5
## 97 0.9354 5 0.6242 4
## 98 0.5800 3 1.9232 7
## 99 0.5530 1 0.4384 3
## 100 1.5734 6 0.4618 3
## 101 0.7866 2 0.4092 2
## 102 1.2214 3 2.0072 2
## 103 2.3964 4 0.9520 6
## 104 0.5972 3 2.1770 4
## 105 0.3590 4 0.3326 2
## 106 0.2492 3 0.2214 4
## 107 0.3408 1 0.3910 1
## 108 0.2436 2 0.7172 4
## 109 0.8956 4 0.4326 2
## 110 1.3954 2 0.2758 1
## 111 0.5784 3 0.2652 5
## 112 0.7702 4 1.2130 2
## 113 0.4772 2 0.6862 1
## 114 0.5862 2 0.2344 4
## 115 0.3496 5 0.8922 3
## 116 0.5366 1 0.8550 4
## 117 0.2268 4 2.5416 3
## 118 0.9056 2 0.7444 3
## 119 0.2576 2 0.0374 1
## 120 0.2506 2 0.9792 3
## 121 1.3828 3 1.0122 4
## 122 0.6338 7 0.1886 1
## 123 1.3204 4 0.5272 6
## 124 0.6636 5 0.7778 3
## 125 1.5552 4 0.5060 4
## 126 1.1446 2 2.0434 3
## 127 0.2612 3 0.3566 7
## 128 0.2798 4 2.0378 3
## 129 0.1890 4 2.0212 2
## 130 0.5016 4 0.5542 4
## 131 0.8986 3 0.3388 2
## 132 0.5750 2 0.5156 3
## 133 0.7252 1 1.2182 3
## 134 1.8028 2 1.0152 4
## 135 1.2912 4 1.2526 4
## 136 1.1040 4 0.8454 2
## 137 1.0292 4 1.1074 1
## 138 0.6970 3 0.2258 1
## 139 0.6342 4 1.3430 3
## 140 0.8420 6 0.0262 1
## 141 0.7420 4 2.6252 4
## 142 0.6414 1 0.0536 2
## 143 0.5034 3 1.7230 2
## 144 1.1954 4 0.1446 3
## 145 0.4268 3 0.2746 2
## 146 0.0296 6 1.4610 1
## 147 0.2522 2 0.6368 3
## 148 1.7900 6 1.6212 1
## 149 0.2348 5 1.0504 5
## 150 1.0890 7 0.7922 5
## 151 0.8942 6 0.1610 3
## 152 1.0590 1 0.7742 7
## 153 2.1186 2 0.9552 1
## 154 0.2456 3 0.6676 1
## 155 1.0658 3 0.6236 3
## 156 0.5338 5 0.1426 2
## 157 0.9668 6 0.5726 2
## 158 0.7010 2 1.7448 4
## 159 1.3036 2 0.9226 2
## 160 1.0974 4 0.7766 2
## 161 0.7478 1 0.4990 4
## 162 0.4428 6 1.8990 1
## 163 1.6220 1 0.1848 2
## 164 0.5714 3 0.9272 4
## 165 0.4264 2 1.0042 1
## 166 0.3324 4 2.8930 5
## 167 0.9878 2 1.8628 4
## 168 1.0482 5 0.4762 3
## 169 0.4860 1 0.3444 3
## 170 0.8978 4 0.7350 3
## 171 0.7516 1 0.6336 5
## 172 1.5280 3 0.5508 5
## 173 0.2638 3 0.4090 2
## 174 0.6184 3 0.4206 3
## 175 0.4828 5 0.4836 2
## 176 0.5936 7 1.5362 2
## 177 1.2514 5 0.2010 1
## 178 0.6860 5 1.8456 3
## 179 0.7442 2 2.1974 4
## 180 1.9094 4 0.9774 2
## 181 0.9236 4 0.2220 2
## 182 0.8974 3 0.7680 3
## 183 0.7058 2 1.7068 6
## 184 0.4888 3 0.4812 3
## 185 1.4932 1 0.9942 4
## 186 0.3590 4 0.7784 2
## 187 0.4186 4 0.8074 3
## 188 0.2066 2 1.1290 2
## nilai.lainnya total.nilai.tunai lama.member promo Total_Frekuensi_Pembelian
## 1 0.2822 0.59 35 1 9
## 2 0.6252 3.05 39 0 10
## 3 0.6478 1.06 51 0 6
## 4 0.5866 0.26 19 0 8
## 5 0.6484 2.85 21 1 14
## 6 1.2326 0.30 5 0 9
## 7 0.2528 6.31 37 0 5
## 8 0.5114 2.33 48 0 12
## 9 0.1570 1.55 4 1 12
## 10 0.2402 2.77 17 0 8
## 11 0.6652 4.01 16 1 8
## 12 1.0706 1.10 38 1 8
## 13 0.3592 2.35 31 0 9
## 14 0.3618 1.08 49 0 9
## 15 0.5100 3.55 19 1 8
## 16 0.2348 4.89 24 0 9
## 17 0.2118 0.70 2 0 11
## 18 0.3044 3.58 31 0 9
## 19 0.3012 1.26 14 0 11
## 20 0.3474 7.22 47 1 15
## 21 1.3954 2.31 49 0 4
## 22 2.2904 6.67 20 0 11
## 23 0.4908 5.89 7 1 7
## 24 0.4736 1.25 46 0 5
## 25 0.1278 1.48 26 1 7
## 26 0.4108 0.86 6 1 9
## 27 1.0418 0.12 22 0 7
## 28 0.7000 9.88 23 1 13
## 29 0.5226 1.03 25 0 4
## 30 0.2150 5.68 43 0 14
## 31 2.5626 9.48 34 0 12
## 32 0.4930 0.33 45 0 6
## 33 0.8564 6.71 34 0 12
## 34 0.3490 0.08 27 0 3
## 35 0.5546 1.96 24 0 8
## 36 0.8622 2.62 14 1 9
## 37 0.4818 2.43 15 0 5
## 38 0.1332 6.59 4 1 11
## 39 2.4766 4.22 15 0 5
## 40 0.2178 2.35 12 0 13
## 41 0.4408 1.79 39 0 6
## 42 1.0612 2.03 3 1 5
## 43 2.5246 23.02 35 1 17
## 44 0.9076 6.17 44 1 10
## 45 0.9354 1.74 7 1 4
## 46 0.4656 1.03 15 1 10
## 47 0.9508 0.50 16 0 7
## 48 0.1830 2.94 18 1 10
## 49 0.8782 1.51 38 0 9
## 50 0.3918 3.65 39 1 12
## 51 0.9090 1.98 29 0 6
## 52 0.4534 8.80 44 0 14
## 53 1.6912 1.30 8 0 4
## 54 0.6176 6.45 43 0 12
## 55 1.0652 5.88 30 0 9
## 56 1.5124 3.12 44 0 11
## 57 1.6506 8.44 9 0 16
## 58 0.6074 1.31 31 0 4
## 59 0.6848 0.48 28 0 3
## 60 1.2076 0.25 11 0 9
## 61 1.2964 8.80 40 1 13
## 62 0.2152 2.36 34 1 11
## 63 0.2534 3.01 24 0 8
## 64 0.0688 1.62 33 0 8
## 65 0.0214 1.82 25 0 12
## 66 1.3102 7.18 43 0 10
## 67 1.0148 4.18 26 1 11
## 68 0.6438 2.41 23 0 9
## 69 1.1898 5.51 27 1 12
## 70 1.1866 4.40 19 1 7
## 71 0.6104 6.11 6 0 14
## 72 0.2924 13.32 49 1 13
## 73 0.5010 4.77 42 0 10
## 74 0.2536 1.00 50 0 8
## 75 0.1308 2.08 41 0 12
## 76 0.7042 2.37 26 1 5
## 77 1.6844 0.67 15 0 9
## 78 0.3974 0.56 37 0 9
## 79 0.4072 1.80 13 0 10
## 80 0.2986 1.29 3 1 8
## 81 1.3696 4.80 33 0 11
## 82 0.8170 8.48 6 0 12
## 83 1.1100 0.11 32 0 4
## 84 1.2146 9.73 2 1 9
## 85 1.3128 5.34 25 0 5
## 86 2.2050 8.43 13 0 8
## 87 0.9728 7.55 46 0 11
## 88 0.4654 1.05 1 0 5
## 89 1.8728 21.26 16 0 13
## 90 0.1638 1.01 32 0 7
## 91 0.8560 0.67 13 1 9
## 92 0.7204 5.12 18 0 6
## 93 0.5094 0.78 47 0 12
## 94 0.3480 0.68 21 0 11
## 95 0.7630 2.79 11 0 13
## 96 1.1862 8.32 5 0 13
## 97 0.2732 7.26 28 1 14
## 98 0.8286 15.53 8 1 17
## 99 0.2408 2.51 37 1 7
## 100 0.7502 3.81 49 0 14
## 101 2.5248 2.44 48 1 6
## 102 0.7556 3.65 16 1 7
## 103 1.4082 22.95 13 0 16
## 104 0.1834 0.31 18 1 11
## 105 0.7360 1.83 45 0 8
## 106 0.6562 3.21 23 1 11
## 107 0.5796 0.47 17 0 3
## 108 0.3580 2.51 1 1 10
## 109 1.2678 3.71 42 0 8
## 110 0.3122 0.12 45 1 4
## 111 0.8686 2.24 51 0 13
## 112 0.6332 7.00 40 0 8
## 113 1.6842 0.17 48 0 4
## 114 0.9690 2.28 27 0 10
## 115 1.1760 1.36 2 1 11
## 116 2.7596 9.92 44 1 10
## 117 0.4828 3.06 35 1 10
## 118 0.9008 1.90 30 0 8
## 119 2.1968 1.65 41 0 5
## 120 0.2686 0.14 10 0 8
## 121 0.7388 9.80 46 0 11
## 122 0.2176 2.09 43 1 10
## 123 0.4314 2.57 21 0 17
## 124 0.9384 6.99 34 0 11
## 125 0.8770 2.58 39 0 12
## 126 0.5912 6.57 31 1 9
## 127 0.4206 3.71 36 0 18
## 128 0.8114 9.42 40 1 12
## 129 1.0794 1.05 27 1 8
## 130 0.1032 1.40 14 1 12
## 131 0.5216 3.81 8 0 7
## 132 1.1764 1.69 14 1 8
## 133 0.2212 1.39 15 0 7
## 134 0.6226 9.53 45 1 11
## 135 0.3670 4.23 24 0 12
## 136 1.6854 5.95 18 0 8
## 137 0.4236 4.83 35 0 6
## 138 0.8224 1.39 6 0 5
## 139 0.6236 4.39 39 1 10
## 140 1.8082 0.48 28 0 8
## 141 0.8674 2.74 36 1 13
## 142 0.8636 0.24 20 0 6
## 143 0.9586 5.91 7 0 7
## 144 1.5308 6.24 38 0 10
## 145 0.8832 3.13 31 0 8
## 146 0.9144 8.69 9 0 8
## 147 1.1860 0.16 40 0 8
## 148 2.2588 0.70 35 1 8
## 149 0.4182 4.60 38 1 15
## 150 0.2330 10.32 42 1 17
## 151 0.9728 5.47 25 0 12
## 152 0.2486 9.78 19 0 15
## 153 0.9372 4.75 8 1 4
## 154 1.6538 0.71 20 0 5
## 155 0.7504 6.42 27 1 9
## 156 0.5528 2.55 45 0 9
## 157 1.1496 0.77 2 0 10
## 158 0.9168 2.46 32 0 10
## 159 1.1124 2.38 1 0 6
## 160 0.4480 5.40 41 0 8
## 161 1.5842 8.92 42 0 10
## 162 0.7786 11.40 17 1 9
## 163 0.4350 2.89 47 0 5
## 164 0.4814 3.61 36 0 11
## 165 0.4658 1.08 24 1 4
## 166 0.4912 8.61 23 1 14
## 167 0.2658 1.19 38 0 10
## 168 2.5678 4.65 33 0 12
## 169 0.5506 1.32 50 0 7
## 170 0.7904 0.46 8 0 10
## 171 0.3482 5.00 7 0 11
## 172 0.9748 12.26 8 1 13
## 173 0.8000 2.49 48 0 7
## 174 0.0928 1.89 17 0 9
## 175 1.5290 1.04 46 0 9
## 176 0.0934 3.71 15 0 11
## 177 1.4606 5.34 42 1 9
## 178 0.8286 8.12 43 1 11
## 179 0.4026 0.45 38 0 10
## 180 0.7258 7.87 7 0 8
## 181 0.6366 2.81 9 0 8
## 182 0.5416 6.60 24 1 9
## 183 0.3698 0.99 16 0 14
## 184 0.0484 0.14 22 0 9
## 185 0.6908 0.18 22 0 9
## 186 0.2330 1.40 44 0 9
## 187 1.9580 7.54 43 0 10
## 188 0.3798 0.07 37 1 6
## Total_Rata_Pembelian
## 1 1.6948
## 2 1.2644
## 3 1.4064
## 4 1.2434
## 5 2.7748
## 6 3.3660
## 7 4.5360
## 8 1.1920
## 9 1.4404
## 10 1.7394
## 11 1.6306
## 12 2.3288
## 13 0.9944
## 14 1.9006
## 15 5.5600
## 16 2.1774
## 17 0.9508
## 18 1.5162
## 19 1.8108
## 20 2.3646
## 21 3.1670
## 22 3.6648
## 23 2.2794
## 24 1.4830
## 25 0.9298
## 26 1.7516
## 27 1.9648
## 28 3.8696
## 29 1.5348
## 30 1.6230
## 31 4.0202
## 32 1.7206
## 33 2.0164
## 34 1.8002
## 35 3.2890
## 36 2.2502
## 37 1.2676
## 38 2.5474
## 39 3.5596
## 40 1.6074
## 41 2.5718
## 42 1.5382
## 43 4.8490
## 44 2.1828
## 45 1.5724
## 46 3.3192
## 47 1.9272
## 48 1.6826
## 49 1.3676
## 50 1.9664
## 51 2.1310
## 52 2.2348
## 53 2.9982
## 54 1.4016
## 55 2.7052
## 56 1.9752
## 57 2.4636
## 58 1.4244
## 59 1.4536
## 60 2.2010
## 61 3.3080
## 62 1.1674
## 63 2.6224
## 64 1.4184
## 65 2.2850
## 66 4.2224
## 67 2.0874
## 68 2.0136
## 69 2.9362
## 70 3.4260
## 71 2.1084
## 72 3.2754
## 73 2.2598
## 74 1.5678
## 75 2.2072
## 76 2.3488
## 77 2.2912
## 78 1.6882
## 79 0.8918
## 80 2.7426
## 81 2.4048
## 82 2.2512
## 83 2.9082
## 84 3.3426
## 85 4.2180
## 86 5.5878
## 87 2.2514
## 88 0.7868
## 89 4.8358
## 90 1.7940
## 91 1.5840
## 92 2.7690
## 93 2.5854
## 94 2.3082
## 95 1.4088
## 96 3.0662
## 97 1.8328
## 98 3.3318
## 99 1.2322
## 100 2.7854
## 101 3.7206
## 102 3.9842
## 103 4.7566
## 104 2.9576
## 105 1.4276
## 106 1.1268
## 107 1.3114
## 108 1.3188
## 109 2.5960
## 110 1.9834
## 111 1.7122
## 112 2.6164
## 113 2.8476
## 114 1.7896
## 115 2.4178
## 116 4.1512
## 117 3.2512
## 118 2.5508
## 119 2.4918
## 120 1.4984
## 121 3.1338
## 122 1.0400
## 123 2.2790
## 124 2.3798
## 125 2.9382
## 126 3.7792
## 127 1.0384
## 128 3.1290
## 129 3.2896
## 130 1.1590
## 131 1.7590
## 132 2.2670
## 133 2.1646
## 134 3.4406
## 135 2.9108
## 136 3.6348
## 137 2.5602
## 138 1.7452
## 139 2.6008
## 140 2.6764
## 141 4.2346
## 142 1.5586
## 143 3.1850
## 144 2.8708
## 145 1.5846
## 146 2.4050
## 147 2.0750
## 148 5.6700
## 149 1.7034
## 150 2.1142
## 151 2.0280
## 152 2.0818
## 153 4.0110
## 154 2.5670
## 155 2.4398
## 156 1.2292
## 157 2.6890
## 158 3.3626
## 159 3.3386
## 160 2.3220
## 161 2.8310
## 162 3.1204
## 163 2.2418
## 164 1.9800
## 165 1.8964
## 166 3.7166
## 167 3.1164
## 168 4.0922
## 169 1.3810
## 170 2.4232
## 171 1.7334
## 172 3.0536
## 173 1.4728
## 174 1.1318
## 175 2.4954
## 176 2.2232
## 177 2.9130
## 178 3.3602
## 179 3.3442
## 180 3.6126
## 181 1.7822
## 182 2.2070
## 183 2.7824
## 184 1.0184
## 185 3.1782
## 186 1.3704
## 187 3.1840
## 188 1.7154lm(total.nilai.tunai~Total_Frekuensi_Pembelian+Total_Rata_Pembelian, data = data5)##
## Call:
## lm(formula = total.nilai.tunai ~ Total_Frekuensi_Pembelian +
## Total_Rata_Pembelian, data = data5)
##
## Coefficients:
## (Intercept) Total_Frekuensi_Pembelian
## -5.6495 0.5662
## Total_Rata_Pembelian
## 1.7833data4n <- data4 %>% select(-no.sampel, -frekuensi.fashion, -frekuensi.footwear, -frekuensi.lainnya, -Total_Rata_Pembelian)Fix Anomaly
data2n <- data2 %>% select(-frekuensi.fashion,-frekuensi.footwear, -frekuensi.lainnya)
#data3_newvar <- data3_newvar %>% select(-frekuensi.fashion,-frekuensi.footwear, -frekuensi.lainnya, -nilai.fashion, -nilai.footwear,-nilai.lainnya)
data4_fix <- data4
datadiskret <- data3_diskret %>% select(-frekuensi.fashion,-frekuensi.footwear, -frekuensi.lainnya)
datadiskret2 <- data3_diskret2 %>% select(-frekuensi.fashion,-frekuensi.footwear, -frekuensi.lainnya)Mengatasi Oulier
step_outlier_fix <- function(b){
for (i in 1:(ncol(b))){
x <- b[,i]
if (!is.factor(x)){
# Menentukan kuantil
Q1 <- quantile(x, 0.25)
Q3 <- quantile(x, 0.75)
IQR <- Q3 - Q1
# Batas Outlier
down_outlier_min <- Q1 - (1.5*IQR)
down_outlier_max <- Q1 - (3.0*IQR)
up_outlier_min <- Q3 + (1.5*IQR)
up_outlier_max <- Q3 + (3.0*IQR)
# Inisialisasi Nilai Variabel
up_outliers <- 0
up_extreme_outliers <- 0
down_outliers <- 0
down_extreme_outliers <- 0
up_extreme_value <- NULL
down_extreme_value <- NULL
for (j in 1:length(x)){
if (x[j] < down_outlier_min && x[j] >= down_outlier_max){
# Increment jumlah outlier
down_outliers <- down_outliers + 1
x[j] <- mean(x)
}
else if (x[j] < down_outlier_max){
# Increment jumlah outlier
down_extreme_outliers <- down_extreme_outliers + 1
x[j] <- mean(x)
}
else if (x[j] > up_outlier_min && x[j] <= up_outlier_max){
# Increment jumlah outlier
up_outliers <- up_outliers + 1
x[j] <- mean(x)
}
else if (x[j] > up_outlier_max){
# Increment jumlah outlier
up_extreme_outliers <- up_extreme_outliers + 1
x[j] <- mean(x)
}
b[j, i] <- x[j]
}
cat("Jumlah Outlier atas x",i, " :", up_outliers, "\n")
cat("Jumlah Outlier bawah x",i, " :", down_outliers, "\n")
cat("Jumlah Ekstrem atas x",i, " :", up_extreme_outliers, "\n")
cat("Jumlah Ekstrem bawah x",i, " :", down_extreme_outliers, "\n \n")
}
}
return(b)
}step_outlier_fix2 <- function(b){
for (i in 1:(ncol(b))){
x <- b[,i]
if (!is.factor(x)){
# Menentukan kuantil
Q1 <- quantile(x, 0.25)
Q3 <- quantile(x, 0.75)
IQR <- Q3 - Q1
# Batas Outlier
down_outlier_min <- Q1 - (1.5*IQR)
down_outlier_max <- Q1 - (3.0*IQR)
up_outlier_min <- Q3 + (1.5*IQR)
up_outlier_max <- Q3 + (3.0*IQR)
# Inisialisasi Nilai Variabel
up_outliers <- 0
up_extreme_outliers <- 0
down_outliers <- 0
down_extreme_outliers <- 0
up_extreme_value <- NULL
down_extreme_value <- NULL
for (j in 1:length(x)){
if (x[j] < down_outlier_min && x[j] >= down_outlier_max){
# Increment jumlah outlier
down_outliers <- down_outliers + 1
#x[j] <- mean(x)
}
else if (x[j] < down_outlier_max){
# Increment jumlah outlier
down_extreme_outliers <- down_extreme_outliers + 1
x[j] <- mean(x)
}
else if (x[j] > up_outlier_min && x[j] <= up_outlier_max){
# Increment jumlah outlier
up_outliers <- up_outliers + 1
#x[j] <- mean(x)
}
else if (x[j] > up_outlier_max){
# Increment jumlah outlier
up_extreme_outliers <- up_extreme_outliers + 1
x[j] <- mean(x)
}
b[j, i] <- x[j]
}
cat("Jumlah Outlier atas x",i, " :", up_outliers, "\n")
cat("Jumlah Outlier bawah x",i, " :", down_outliers, "\n")
cat("Jumlah Ekstrem atas x",i, " :", up_extreme_outliers, "\n")
cat("Jumlah Ekstrem bawah x",i, " :", down_extreme_outliers, "\n \n")
}
}
return(b)
}#data2_outlier <- step_outlier_fix(data2n)
#data2_outlier2 <- step_outlier_fix2(data2n)#data3_outlier <- step_outlier_fix(data3n)
#data3_outlier2 <- step_outlier_fix2(data3n) Raw Data Splitting
set.seed(123)
basic_split <- initial_split(data = data[-1],
prop = 0.8,
strata = "promo")
tidy(basic_split) %>% count(Data)## # A tibble: 2 × 2
## Data n
## <chr> <int>
## 1 Analysis 286
## 2 Assessment 72data_training <- training(basic_split)
#df.train <- df.train
data_testing <- testing(basic_split)
#df.test <- df.test
dim(data_training);dim(data_testing)## [1] 286 13## [1] 72 13Data Splitting FENOM
set.seed(123)
basic_split1 <- initial_split(data = data2n,
prop = 0.8,
strata = "promo")
tidy(basic_split1) %>% count(Data)## # A tibble: 2 × 2
## Data n
## <chr> <int>
## 1 Analysis 259
## 2 Assessment 66data_training_fenom <- training(basic_split1)
data_testing_fenom <- testing(basic_split1)
data_training_fenom <- data_training_fenom %>% select(-no.sampel)
data_testing_fenom <- data_testing_fenom %>% select(-no.sampel)
dim(data_training_fenom);dim(data_testing_fenom)## [1] 259 11## [1] 66 11Data Splitting One Hot Encoding + FE + Anomaly data + Diskretasi usia dan lama member
Praproses dulu gan
pra_proses1 <- recipe(promo~., data = datadiskret2[-1]) %>%
step_dummy(all_factor_predictors()) %>%
step_normalize(all_numeric_predictors(), means= 0, sds = 1)
pra_proses1f <- pra_proses1 %>%
prep() %>%
bake(new_data = NULL)set.seed(121)
basic_split2 <- initial_split(data = pra_proses1f,
prop = 0.8,
strata = "promo")
tidy(basic_split2) %>% count(Data)## # A tibble: 2 × 2
## Data n
## <chr> <int>
## 1 Analysis 259
## 2 Assessment 66data_training_diskret2 <- training(basic_split2)
data_testing_diskret2 <- testing(basic_split2)
#data_training_diskret2 <- data_training_diskret2 %>% select(-no.sampel)
#data_testing_diskret2 <- data_testing_diskret2 %>% select(-no.sampel)
dim(data_training_diskret2);dim(data_testing_diskret2)## [1] 259 29## [1] 66 29Data Splitting One Hot Encoding + FE + Anomaly DAta + Diskretasi Usia
Praproses dulu gan
pra_proses2 <- recipe(promo~., data = datadiskret[-1]) %>%
step_dummy(all_factor_predictors()) %>%
step_normalize(all_numeric_predictors(), means= 0, sds = 1)
pra_proses2f <- pra_proses2 %>%
prep() %>%
bake(new_data = NULL)set.seed(123)
basic_split3 <- initial_split(data = pra_proses2f,
prop = 0.8,
strata = "promo")
tidy(basic_split3) %>% count(Data)## # A tibble: 2 × 2
## Data n
## <chr> <int>
## 1 Analysis 259
## 2 Assessment 66data_training_diskret <- training(basic_split3)
data_testing_diskret <- testing(basic_split3)
data_training_diskret <- data_training_diskret
data_testing_diskret <- data_testing_diskret
dim(data_training_diskret);dim(data_testing_diskret)## [1] 259 26## [1] 66 26Data Splitting Standarisasi all
Praproses dulu gan
data_standard <- recipe(promo~.,data = data) %>%
#step_corr(all_numeric_predictors(), threshold = .8) %>%
step_normalize(all_numeric_predictors(),means = 0, sds = 1)data_standardfix <- data_standard %>%
prep() %>%
bake(new_data = NULL)set.seed(123)
basic_split4 <- initial_split(data = data_standardfix,
prop = 0.8,
strata = "promo")
tidy(basic_split4) %>% count(Data)## # A tibble: 2 × 2
## Data n
## <chr> <int>
## 1 Analysis 286
## 2 Assessment 72data_training_anom <- training(basic_split4)
data_testing_anom <- testing(basic_split4)
data_training_anom <- data_training_anom[-1]
data_testing_anom <- data_testing_anom[-1]
dim(data_training_anom);dim(data_testing_anom)## [1] 286 13## [1] 72 13Data Splitting FENOM Standarisasi
Praproses dulu gan
data_standardfenom <- recipe(promo~., data = data2n[-1]) %>%
step_normalize(all_numeric_predictors(), means= 0, sds = 1)
data_standardfenomfix <- data_standardfenom %>%
prep() %>%
bake(new_data = NULL)set.seed(123)
basic_split4 <- initial_split(data = data_standardfenomfix,
prop = 0.8,
strata = "promo")
tidy(basic_split4) %>% count(Data)## # A tibble: 2 × 2
## Data n
## <chr> <int>
## 1 Analysis 259
## 2 Assessment 66data_training_sfenom <- training(basic_split4)
data_testing_sfenom <- testing(basic_split4)
data_training_sfenom <- data_training_sfenom
data_testing_sfenom <- data_testing_sfenom
dim(data_training_sfenom);dim(data_testing_sfenom)## [1] 259 11## [1] 66 11Data Splitting FENOM +regresi
Praproses dulu gan
data_fenomregresi <- recipe(promo~., data = data4n) %>%
step_normalize(all_numeric_predictors(), means= 0, sds = 1)
data_fenomregresifix <- data_fenomregresi %>%
prep() %>%
bake(new_data = NULL)set.seed(123)
basic_split4 <- initial_split(data = data_fenomregresifix,
prop = 0.8,
strata = "promo")
tidy(basic_split4) %>% count(Data)## # A tibble: 2 × 2
## Data n
## <chr> <int>
## 1 Analysis 259
## 2 Assessment 66data_training_fenomregresi <- training(basic_split4)
data_testing_fenomregresi <- testing(basic_split4)
data_training_fenomregresi <- data_training_fenomregresi
data_testing_fenomregresi <- data_testing_fenomregresi
dim(data_training_fenomregresi);dim(data_testing_fenomregresi)## [1] 259 11## [1] 66 11a <- data_training_sfenom %>%
count(promo) %>%
mutate(percent=n*100/sum(n),label=str_c(round(percent,2),"%")) %>%
ggplot(aes(x="",y=n,fill=promo))+
geom_col()+
geom_text(aes(label = label),
position = position_stack(vjust = 0.5)) +
coord_polar(theta = "y")+
theme_cowplot()
ggsave("Imbalance.png", a)## Saving 7 x 5 in imageUnderbalancing Data
data_training_under <- ovun.sample(promo ~ ., data = data_training_sfenom, method = "under",N = 174)$data
table(data_training_under$promo)##
## 0 1
## 87 87a <- data_training_under %>%
count(promo) %>%
mutate(percent = n * 100 / sum(n),
label = str_c("", n, ": ", round(percent, 2), "%")) %>% # Menambahkan jumlah observasi
ggplot(aes(x = "", y = n, fill = factor(promo))) + # Pastikan promo adalah faktor
geom_col() +
geom_text(aes(label = label), position = position_stack(vjust = 0.5)) +
coord_polar(theta = "y") +
theme_cowplot()
ggsave("Imbalance.png", a, width = 10, height = 8) # Spesifikasikan ukuran sesuai kebutuhanOverbalancing Data
data_training_ovr <- ovun.sample(promo ~ ., data = data_training_sfenom, method = "over",N = 344)$data
table(data_training_ovr$promo)##
## 0 1
## 172 172Hybridbalancing Data
data_training_hyb <- ovun.sample(promo ~ ., data = data_training_sfenom, method = "over",N = 304)$data
table(data_training_hyb$promo)##
## 0 1
## 172 132data_training_hyb_final <- ovun.sample(promo ~ ., data = data_training_hyb, method = "under",N = 264)$data
table(data_training_hyb_final$promo)##
## 0 1
## 132 132a <- data_training_hyb_final %>%
count(promo) %>%
mutate(percent = n * 100 / sum(n),
label = str_c("", n, ": ", round(percent, 2), "%")) %>% # Menambahkan jumlah observasi
ggplot(aes(x = "", y = n, fill = factor(promo))) + # Pastikan promo adalah faktor
geom_col() +
geom_text(aes(label = label), position = position_stack(vjust = 0.5)) +
coord_polar(theta = "y") +
theme_cowplot()
ggsave("Hybridsampling.png", a, width = 10, height = 8) # Spesifikasikan ukuran sesuai kebutuhandata_training_under <- ovun.sample(promo ~ ., data = data_training_sfenom, method = "under",N = 174)$data
table(data_training_under$promo)##
## 0 1
## 87 87a <- data_training_ovr %>%
count(promo) %>%
mutate(percent = n * 100 / sum(n),
label = str_c("", n, ": ", round(percent, 2), "%")) %>% # Menambahkan jumlah observasi
ggplot(aes(x = "", y = n, fill = factor(promo))) + # Pastikan promo adalah faktor
geom_col() +
geom_text(aes(label = label), position = position_stack(vjust = 0.5)) +
coord_polar(theta = "y") +
theme_cowplot()
ggsave("Oversampling.png", a, width = 10, height = 8) # Spesifikasikan ukuran sesuai kebutuhanTest Praproses
pra_proses1 <- recipe(promo~.,data = data_training_under) %>%
#step_corr(all_numeric_predictors(), threshold = .8) %>%
step_discretize(all_factor_predictors(), min_unique = 4)
#step_discretize(lama.member, min_unique = 5) Pemodelan SVM 1
SVM Model
library(e1071)## Warning: package 'e1071' was built under R version 4.3.3##
## Attaching package: 'e1071'## The following object is masked from 'package:mlr3verse':
##
## tune## The following object is masked from 'package:tune':
##
## tune## The following object is masked from 'package:rsample':
##
## permutations## The following object is masked from 'package:parsnip':
##
## tuneModel SVM Data training
set.seed(123)
tuningsvm <- tune(svm,promo~.,data= data_training,
ranges=list(kernel=c("radial"),cost = c(1,2,5,10,13,15),
gamma = c(0.01,0.1,1,2,5,10)))
trainonly <- tuningsvm$best.model
trainonly##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training,
## ranges = list(kernel = c("radial"), cost = c(1, 2, 5, 10, 13,
## 15), gamma = c(0.01, 0.1, 1, 2, 5, 10)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 10
##
## Number of Support Vectors: 190Model SVM Underbalancing + standarfenom
set.seed(123)
tuningsvm_under <- tune(svm,promo~.,data= data_training_under,
ranges=list(kernel=c("polynomial","sigmoid", "linear"),cost = c(1,2,3,4,5,8,10), gamma = c(0.01,0.1,0.5,0.9)))
trainonly_under <- tuningsvm_under$best.model
trainonly_under##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training_under,
## ranges = list(kernel = c("polynomial", "sigmoid", "linear"),
## cost = c(1, 2, 3, 4, 5, 8, 10), gamma = c(0.01, 0.1, 0.5,
## 0.9)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 5
##
## Number of Support Vectors: 93summary(tuningsvm_under)##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## kernel cost gamma
## linear 5 0.01
##
## - best performance: 0.2571895
##
## - Detailed performance results:
## kernel cost gamma error dispersion
## 1 polynomial 1 0.01 0.5800654 0.05518588
## 2 sigmoid 1 0.01 0.4460784 0.11656537
## 3 linear 1 0.01 0.2862745 0.13342515
## 4 polynomial 2 0.01 0.5800654 0.05518588
## 5 sigmoid 2 0.01 0.4287582 0.12735132
## 6 linear 2 0.01 0.2859477 0.11338661
## 7 polynomial 3 0.01 0.5800654 0.05518588
## 8 sigmoid 3 0.01 0.4062092 0.11221683
## 9 linear 3 0.01 0.2741830 0.11175483
## 10 polynomial 4 0.01 0.5800654 0.05518588
## 11 sigmoid 4 0.01 0.3888889 0.11989501
## 12 linear 4 0.01 0.2624183 0.10218829
## 13 polynomial 5 0.01 0.5800654 0.05518588
## 14 sigmoid 5 0.01 0.3885621 0.12445456
## 15 linear 5 0.01 0.2571895 0.11084433
## 16 polynomial 8 0.01 0.5800654 0.05518588
## 17 sigmoid 8 0.01 0.3375817 0.11202633
## 18 linear 8 0.01 0.2735294 0.12814320
## 19 polynomial 10 0.01 0.5800654 0.05518588
## 20 sigmoid 10 0.01 0.3369281 0.13042314
## 21 linear 10 0.01 0.2735294 0.12814320
## 22 polynomial 1 0.10 0.4290850 0.15755001
## 23 sigmoid 1 0.10 0.3437908 0.11606088
## 24 linear 1 0.10 0.2862745 0.13342515
## 25 polynomial 2 0.10 0.4405229 0.16865053
## 26 sigmoid 2 0.10 0.3333333 0.07513971
## 27 linear 2 0.10 0.2859477 0.11338661
## 28 polynomial 3 0.10 0.3892157 0.15831938
## 29 sigmoid 3 0.10 0.3506536 0.09571253
## 30 linear 3 0.10 0.2741830 0.11175483
## 31 polynomial 4 0.10 0.3950980 0.14523153
## 32 sigmoid 4 0.10 0.3627451 0.11520091
## 33 linear 4 0.10 0.2624183 0.10218829
## 34 polynomial 5 0.10 0.3552288 0.12348397
## 35 sigmoid 5 0.10 0.3673203 0.11805183
## 36 linear 5 0.10 0.2571895 0.11084433
## 37 polynomial 8 0.10 0.3493464 0.12188223
## 38 sigmoid 8 0.10 0.3735294 0.09915906
## 39 linear 8 0.10 0.2735294 0.12814320
## 40 polynomial 10 0.10 0.3379085 0.12207437
## 41 sigmoid 10 0.10 0.3562092 0.09791810
## 42 linear 10 0.10 0.2735294 0.12814320
## 43 polynomial 1 0.50 0.3496732 0.12091994
## 44 sigmoid 1 0.50 0.4137255 0.10824620
## 45 linear 1 0.50 0.2862745 0.13342515
## 46 polynomial 2 0.50 0.3496732 0.12091994
## 47 sigmoid 2 0.50 0.3738562 0.12982812
## 48 linear 2 0.50 0.2859477 0.11338661
## 49 polynomial 3 0.50 0.3496732 0.12091994
## 50 sigmoid 3 0.50 0.4084967 0.12096900
## 51 linear 3 0.50 0.2741830 0.11175483
## 52 polynomial 4 0.50 0.3496732 0.12091994
## 53 sigmoid 4 0.50 0.4366013 0.15289555
## 54 linear 4 0.50 0.2624183 0.10218829
## 55 polynomial 5 0.50 0.3496732 0.12091994
## 56 sigmoid 5 0.50 0.4254902 0.07434271
## 57 linear 5 0.50 0.2571895 0.11084433
## 58 polynomial 8 0.50 0.3496732 0.12091994
## 59 sigmoid 8 0.50 0.4026144 0.08610214
## 60 linear 8 0.50 0.2735294 0.12814320
## 61 polynomial 10 0.50 0.3496732 0.12091994
## 62 sigmoid 10 0.50 0.3849673 0.07453401
## 63 linear 10 0.50 0.2735294 0.12814320
## 64 polynomial 1 0.90 0.3496732 0.12091994
## 65 sigmoid 1 0.90 0.4254902 0.06897756
## 66 linear 1 0.90 0.2862745 0.13342515
## 67 polynomial 2 0.90 0.3496732 0.12091994
## 68 sigmoid 2 0.90 0.4362745 0.11695157
## 69 linear 2 0.90 0.2859477 0.11338661
## 70 polynomial 3 0.90 0.3496732 0.12091994
## 71 sigmoid 3 0.90 0.4316993 0.13998097
## 72 linear 3 0.90 0.2741830 0.11175483
## 73 polynomial 4 0.90 0.3496732 0.12091994
## 74 sigmoid 4 0.90 0.4532680 0.09168529
## 75 linear 4 0.90 0.2624183 0.10218829
## 76 polynomial 5 0.90 0.3496732 0.12091994
## 77 sigmoid 5 0.90 0.4186275 0.07080526
## 78 linear 5 0.90 0.2571895 0.11084433
## 79 polynomial 8 0.90 0.3496732 0.12091994
## 80 sigmoid 8 0.90 0.4245098 0.07770863
## 81 linear 8 0.90 0.2735294 0.12814320
## 82 polynomial 10 0.90 0.3496732 0.12091994
## 83 sigmoid 10 0.90 0.4362745 0.08817812
## 84 linear 10 0.90 0.2735294 0.12814320Model SVM Overbalancing
set.seed(123)
tuningsvm_over <- tune(svm,promo~.,data= data_training_ovr,
ranges=list(kernel=c("polynomial","sigmoid", "linear"),cost = c(1,2,3,4,5,8,10), gamma = c(0.01,0.1,0.5,0.9)))
trainonly_over <- tuningsvm_over$best.model
trainonly_over##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training_ovr,
## ranges = list(kernel = c("polynomial", "sigmoid", "linear"),
## cost = c(1, 2, 3, 4, 5, 8, 10), gamma = c(0.01, 0.1, 0.5,
## 0.9)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: polynomial
## cost: 10
## degree: 3
## coef.0: 0
##
## Number of Support Vectors: 216summary(tuningsvm_over)##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## kernel cost gamma
## polynomial 10 0.1
##
## - best performance: 0.1654622
##
## - Detailed performance results:
## kernel cost gamma error dispersion
## 1 polynomial 1 0.01 0.5467227 0.03584821
## 2 sigmoid 1 0.01 0.3513445 0.05388128
## 3 linear 1 0.01 0.2902521 0.07615875
## 4 polynomial 2 0.01 0.5467227 0.03584821
## 5 sigmoid 2 0.01 0.3221008 0.06124283
## 6 linear 2 0.01 0.2728571 0.07279147
## 7 polynomial 3 0.01 0.5467227 0.03584821
## 8 sigmoid 3 0.01 0.3220168 0.07167673
## 9 linear 3 0.01 0.2757143 0.08106759
## 10 polynomial 4 0.01 0.5467227 0.03584821
## 11 sigmoid 4 0.01 0.3190756 0.07482406
## 12 linear 4 0.01 0.2786555 0.07996421
## 13 polynomial 5 0.01 0.5467227 0.03584821
## 14 sigmoid 5 0.01 0.3163025 0.07718518
## 15 linear 5 0.01 0.2815966 0.08230476
## 16 polynomial 8 0.01 0.5467227 0.03584821
## 17 sigmoid 8 0.01 0.2872269 0.08210275
## 18 linear 8 0.01 0.2932773 0.06674312
## 19 polynomial 10 0.01 0.5467227 0.03584821
## 20 sigmoid 10 0.01 0.2929412 0.09824884
## 21 linear 10 0.01 0.2932773 0.06674312
## 22 polynomial 1 0.10 0.2237815 0.10201237
## 23 sigmoid 1 0.10 0.3163866 0.08051869
## 24 linear 1 0.10 0.2902521 0.07615875
## 25 polynomial 2 0.10 0.1801681 0.07511517
## 26 sigmoid 2 0.10 0.3602521 0.07137423
## 27 linear 2 0.10 0.2728571 0.07279147
## 28 polynomial 3 0.10 0.1830252 0.07276532
## 29 sigmoid 3 0.10 0.3865546 0.08515130
## 30 linear 3 0.10 0.2757143 0.08106759
## 31 polynomial 4 0.10 0.1832773 0.05846959
## 32 sigmoid 4 0.10 0.3950420 0.08382462
## 33 linear 4 0.10 0.2786555 0.07996421
## 34 polynomial 5 0.10 0.1803361 0.06117124
## 35 sigmoid 5 0.10 0.3690756 0.09192695
## 36 linear 5 0.10 0.2815966 0.08230476
## 37 polynomial 8 0.10 0.1714286 0.07135329
## 38 sigmoid 8 0.10 0.4036134 0.08760304
## 39 linear 8 0.10 0.2932773 0.06674312
## 40 polynomial 10 0.10 0.1654622 0.07637300
## 41 sigmoid 10 0.10 0.3837815 0.08278346
## 42 linear 10 0.10 0.2932773 0.06674312
## 43 polynomial 1 0.50 0.1800000 0.06475594
## 44 sigmoid 1 0.50 0.4505882 0.05867502
## 45 linear 1 0.50 0.2902521 0.07615875
## 46 polynomial 2 0.50 0.1800000 0.06475594
## 47 sigmoid 2 0.50 0.4213445 0.08662684
## 48 linear 2 0.50 0.2728571 0.07279147
## 49 polynomial 3 0.50 0.1800000 0.06475594
## 50 sigmoid 3 0.50 0.4503361 0.07143621
## 51 linear 3 0.50 0.2757143 0.08106759
## 52 polynomial 4 0.50 0.1800000 0.06475594
## 53 sigmoid 4 0.50 0.4651261 0.05668827
## 54 linear 4 0.50 0.2786555 0.07996421
## 55 polynomial 5 0.50 0.1800000 0.06475594
## 56 sigmoid 5 0.50 0.4157983 0.06306541
## 57 linear 5 0.50 0.2815966 0.08230476
## 58 polynomial 8 0.50 0.1800000 0.06475594
## 59 sigmoid 8 0.50 0.4501681 0.08719277
## 60 linear 8 0.50 0.2932773 0.06674312
## 61 polynomial 10 0.50 0.1800000 0.06475594
## 62 sigmoid 10 0.50 0.4646218 0.07510498
## 63 linear 10 0.50 0.2932773 0.06674312
## 64 polynomial 1 0.90 0.1800000 0.06475594
## 65 sigmoid 1 0.90 0.4243697 0.06404957
## 66 linear 1 0.90 0.2902521 0.07615875
## 67 polynomial 2 0.90 0.1800000 0.06475594
## 68 sigmoid 2 0.90 0.4851261 0.06449278
## 69 linear 2 0.90 0.2728571 0.07279147
## 70 polynomial 3 0.90 0.1800000 0.06475594
## 71 sigmoid 3 0.90 0.4682353 0.06635972
## 72 linear 3 0.90 0.2757143 0.08106759
## 73 polynomial 4 0.90 0.1800000 0.06475594
## 74 sigmoid 4 0.90 0.4505042 0.05598521
## 75 linear 4 0.90 0.2786555 0.07996421
## 76 polynomial 5 0.90 0.1800000 0.06475594
## 77 sigmoid 5 0.90 0.4327731 0.08097612
## 78 linear 5 0.90 0.2815966 0.08230476
## 79 polynomial 8 0.90 0.1800000 0.06475594
## 80 sigmoid 8 0.90 0.4470588 0.06938868
## 81 linear 8 0.90 0.2932773 0.06674312
## 82 polynomial 10 0.90 0.1800000 0.06475594
## 83 sigmoid 10 0.90 0.4266387 0.08073454
## 84 linear 10 0.90 0.2932773 0.06674312Model SVM Hybridbalancing
set.seed(123)
tuningsvm_1 <- tune(svm,promo~.,data= data_training_hyb_final,
ranges=list(kernel=c("polynomial","sigmoid", "linear"),cost = c(1,2,3,4,5,8,10), gamma = c(0.01,0.1,0.5,0.9)))
trainonly_1 <- tuningsvm_1$best.model
trainonly_1##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training_hyb_final,
## ranges = list(kernel = c("polynomial", "sigmoid", "linear"),
## cost = c(1, 2, 3, 4, 5, 8, 10), gamma = c(0.01, 0.1, 0.5,
## 0.9)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: polynomial
## cost: 4
## degree: 3
## coef.0: 0
##
## Number of Support Vectors: 198summary(tuningsvm_1)##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## kernel cost gamma
## polynomial 4 0.1
##
## - best performance: 0.1666667
##
## - Detailed performance results:
## kernel cost gamma error dispersion
## 1 polynomial 1 0.01 0.5522792 0.06677133
## 2 sigmoid 1 0.01 0.3111111 0.08004070
## 3 linear 1 0.01 0.2158120 0.07327610
## 4 polynomial 2 0.01 0.5522792 0.06677133
## 5 sigmoid 2 0.01 0.3113960 0.09048593
## 6 linear 2 0.01 0.2233618 0.06504981
## 7 polynomial 3 0.01 0.5522792 0.06677133
## 8 sigmoid 3 0.01 0.3112536 0.09523289
## 9 linear 3 0.01 0.2195157 0.06569699
## 10 polynomial 4 0.01 0.5522792 0.06677133
## 11 sigmoid 4 0.01 0.3109687 0.07953445
## 12 linear 4 0.01 0.2196581 0.07513425
## 13 polynomial 5 0.01 0.5522792 0.06677133
## 14 sigmoid 5 0.01 0.3074074 0.09456658
## 15 linear 5 0.01 0.2196581 0.07513425
## 16 polynomial 8 0.01 0.5484330 0.06377285
## 17 sigmoid 8 0.01 0.2730769 0.09570240
## 18 linear 8 0.01 0.2233618 0.06992095
## 19 polynomial 10 0.01 0.5484330 0.06377285
## 20 sigmoid 10 0.01 0.2655271 0.09619856
## 21 linear 10 0.01 0.2196581 0.07513425
## 22 polynomial 1 0.10 0.2353276 0.06342827
## 23 sigmoid 1 0.10 0.3074074 0.09785909
## 24 linear 1 0.10 0.2158120 0.07327610
## 25 polynomial 2 0.10 0.2086895 0.04873586
## 26 sigmoid 2 0.10 0.2994302 0.08623379
## 27 linear 2 0.10 0.2233618 0.06504981
## 28 polynomial 3 0.10 0.1856125 0.05411460
## 29 sigmoid 3 0.10 0.3294872 0.07959962
## 30 linear 3 0.10 0.2195157 0.06569699
## 31 polynomial 4 0.10 0.1666667 0.05390566
## 32 sigmoid 4 0.10 0.3186610 0.05978401
## 33 linear 4 0.10 0.2196581 0.07513425
## 34 polynomial 5 0.10 0.1742165 0.04782899
## 35 sigmoid 5 0.10 0.3450142 0.08280317
## 36 linear 5 0.10 0.2196581 0.07513425
## 37 polynomial 8 0.10 0.1856125 0.06983464
## 38 sigmoid 8 0.10 0.3297721 0.07952651
## 39 linear 8 0.10 0.2233618 0.06992095
## 40 polynomial 10 0.10 0.1854701 0.07577967
## 41 sigmoid 10 0.10 0.3028490 0.07861103
## 42 linear 10 0.10 0.2196581 0.07513425
## 43 polynomial 1 0.50 0.2119658 0.07530211
## 44 sigmoid 1 0.50 0.3819088 0.09899220
## 45 linear 1 0.50 0.2158120 0.07327610
## 46 polynomial 2 0.50 0.2119658 0.07530211
## 47 sigmoid 2 0.50 0.4085470 0.07806880
## 48 linear 2 0.50 0.2233618 0.06504981
## 49 polynomial 3 0.50 0.2119658 0.07530211
## 50 sigmoid 3 0.50 0.3562678 0.08924463
## 51 linear 3 0.50 0.2195157 0.06569699
## 52 polynomial 4 0.50 0.2119658 0.07530211
## 53 sigmoid 4 0.50 0.3633903 0.05219048
## 54 linear 4 0.50 0.2196581 0.07513425
## 55 polynomial 5 0.50 0.2119658 0.07530211
## 56 sigmoid 5 0.50 0.3517094 0.07515721
## 57 linear 5 0.50 0.2196581 0.07513425
## 58 polynomial 8 0.50 0.2119658 0.07530211
## 59 sigmoid 8 0.50 0.3633903 0.07506415
## 60 linear 8 0.50 0.2233618 0.06992095
## 61 polynomial 10 0.50 0.2119658 0.07530211
## 62 sigmoid 10 0.50 0.3478632 0.08264509
## 63 linear 10 0.50 0.2196581 0.07513425
## 64 polynomial 1 0.90 0.2119658 0.07530211
## 65 sigmoid 1 0.90 0.4018519 0.09131994
## 66 linear 1 0.90 0.2158120 0.07327610
## 67 polynomial 2 0.90 0.2119658 0.07530211
## 68 sigmoid 2 0.90 0.4044160 0.09989458
## 69 linear 2 0.90 0.2233618 0.06504981
## 70 polynomial 3 0.90 0.2119658 0.07530211
## 71 sigmoid 3 0.90 0.3974359 0.07064340
## 72 linear 3 0.90 0.2195157 0.06569699
## 73 polynomial 4 0.90 0.2119658 0.07530211
## 74 sigmoid 4 0.90 0.4088319 0.07137686
## 75 linear 4 0.90 0.2196581 0.07513425
## 76 polynomial 5 0.90 0.2119658 0.07530211
## 77 sigmoid 5 0.90 0.3977208 0.08450655
## 78 linear 5 0.90 0.2196581 0.07513425
## 79 polynomial 8 0.90 0.2119658 0.07530211
## 80 sigmoid 8 0.90 0.4009972 0.09794073
## 81 linear 8 0.90 0.2233618 0.06992095
## 82 polynomial 10 0.90 0.2119658 0.07530211
## 83 sigmoid 10 0.90 0.4048433 0.08706386
## 84 linear 10 0.90 0.2196581 0.07513425Model SVM Diskretasi 1 + standarisasi + fenom
set.seed(123)
tuningsvm_2 <- tune(svm, promo~., data= data_training_diskret,
ranges=list(kernel=c("polynomial"),
cost = c(1,2,5,10,13),
degree = c(1,2,5,10)))
trainonly_2 <- tuningsvm_2$best.model
trainonly_2##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training_diskret,
## ranges = list(kernel = c("polynomial"), cost = c(1, 2, 5, 10,
## 13), degree = c(1, 2, 5, 10)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: polynomial
## cost: 2
## degree: 2
## coef.0: 0
##
## Number of Support Vectors: 167Model SVM FE + Anomaly Data
set.seed(123)
tuningsvm_3 <- tune(svm,promo~.,data = data_training_fenom,
ranges=list(kernel=c("radial"),cost = c(1,2,5,10,13),
gamma = c(0.01,0.1,1,2,5,10)))
trainonly_3 <- tuningsvm_3$best.model
trainonly_3##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training_fenom,
## ranges = list(kernel = c("radial"), cost = c(1, 2, 5, 10, 13),
## gamma = c(0.01, 0.1, 1, 2, 5, 10)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 10
##
## Number of Support Vectors: 174Model SVM standarisasi all
set.seed(123)
tuningsvm_5 <- tune(svm,promo~.,data= data_training_anom,
ranges=list(kernel=c("radial"),
cost = c(1,2,5,10,13),
gamma = c(0.01,0.1,1,2,5,10)))
trainonly_5 <- tuningsvm_5$best.model
trainonly_5##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training_anom,
## ranges = list(kernel = c("radial"), cost = c(1, 2, 5, 10, 13),
## gamma = c(0.01, 0.1, 1, 2, 5, 10)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 10
##
## Number of Support Vectors: 190Model SVM SFENOM
set.seed(123)
tuningsvm_6 <- tune(svm,promo~.,data= data_training_sfenom,
ranges=list(kernel=c("radial"),
cost = c(1,2,5,10,13),
gamma = c(0.01,0.1,1,2,5,10)))
trainonly_6 <- tuningsvm_6$best.model
trainonly_6##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training_sfenom,
## ranges = list(kernel = c("radial"), cost = c(1, 2, 5, 10, 13),
## gamma = c(0.01, 0.1, 1, 2, 5, 10)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 10
##
## Number of Support Vectors: 174Model SVM Fenomregresi
set.seed(123)
tuningsvm_7 <- tune(svm,promo~.,data= data_training_fenomregresi,
ranges=list(kernel=c( "linear"),
cost = c(1,2,5,10,13),
gamma = c(0.01,0.1,1,2,5,10)))
trainonly_7 <- tuningsvm_7$best.model
trainonly_7##
## Call:
## best.tune(METHOD = svm, train.x = promo ~ ., data = data_training_fenomregresi,
## ranges = list(kernel = c("linear"), cost = c(1, 2, 5, 10, 13),
## gamma = c(0.01, 0.1, 1, 2, 5, 10)))
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 2
##
## Number of Support Vectors: 134Prediksi
#datLatih.Prediksi1 = predict(svm.RBF1, newdata = datLatih)
pred <- predict(trainonly,newdata = data_testing)
predtrain <- predict(trainonly,newdata = data_training)
# Prediksi Data Praproses under Sampling + sfenom
pred_under <- predict(trainonly_under, newdata = data_testing_sfenom)
pred_undertrain <- predict(trainonly_under, newdata = data_training_sfenom)
# Prediksi Data Praproses Over Sampling + sfenom
pred_ovr <- predict(trainonly_over, newdata = data_testing_sfenom)
pred_ovrtrain <- predict(trainonly_over, newdata = data_training_sfenom)
# Prediksi Data Praproses Hybrid Sampling + sfenom
pred_hyb <- predict(trainonly_1, newdata = data_testing_sfenom)
pred_hybtrain <- predict(trainonly_1, newdata = data_training_sfenom)
# Prediksi Data Praproses Data Diskretasi
pred_2 <- predict(trainonly_2, newdata = data_testing_diskret)
pred_2train <- predict(trainonly_2, newdata = data_training_diskret)
# Data training Oversample, diskretasi, dan one hot
pred_3 <- predict(trainonly_3, newdata = data_testing_fenom)
pred_3train <- predict(trainonly_3, newdata = data_training_fenom)
# pred_4 <- predict(trainonly_4, newdata = data_testing_newvar)
# pred_4train <- predict(trainonly_4, newdata = data_training_newvar)
# Standarisasi All
pred_5 <- predict(trainonly_5, newdata = data_testing_anom)
pred_5train <- predict(trainonly_5, newdata = data_training_anom)
pred_6 <- predict(trainonly_6, newdata = data_testing_sfenom)
pred_6train <- predict(trainonly_6, newdata = data_training_sfenom)
pred_7 <- predict(trainonly_7, newdata = data_testing_fenomregresi)
pred_7train <- predict(trainonly_7, newdata = data_training_fenomregresi)Tabel Prediksi
cm <- table(data_testing$promo, pred)
cf <- confusionMatrix(cm)
akurasi <- data.frame("Accuracy" = cf$overall[["Accuracy"]],
"Balanced Accuracy" = cf$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf$byClass[["Sensitivity"]],
"Specificity" = cf$byClass[["Specificity"]])
cmtrain <- table(data_training$promo, predtrain)
cftrain <- confusionMatrix(cmtrain)
akurasitrain <- data.frame("Accuracy" = cftrain$overall[["Accuracy"]],
"Balanced Accuracy" = cftrain$byClass[["Balanced Accuracy"]],
"Sensitivity" = cftrain$byClass[["Sensitivity"]],
"Specificity" = cftrain$byClass[["Specificity"]])
cm_under <- table(data_testing_sfenom$promo, pred_under)
cf_under <- confusionMatrix(cm_under)
akurasi_under <- data.frame("Accuracy" = cf_under$overall[["Accuracy"]],
"Balanced Accuracy" = cf_under$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_under$byClass[["Sensitivity"]],
"Specificity" = cf_under$byClass[["Specificity"]])
cm_undertrain <- table(data_training_sfenom$promo, pred_undertrain)
cf_undertrain <- confusionMatrix(cm_undertrain)
akurasi_undertrain <- data.frame("Accuracy" = cf_under$overall[["Accuracy"]],
"Balanced Accuracy" = cf_under$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_under$byClass[["Sensitivity"]],
"Specificity" = cf_under$byClass[["Specificity"]])
cm_ovrtest <- table(data_testing_sfenom$promo, pred_ovr)
cf_ovrtest <- confusionMatrix(cm_ovrtest)
akurasi_ovr <- data.frame("Accuracy" = cf_ovrtest$overall[["Accuracy"]],
"Balanced Accuracy" = cf_ovrtest$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_ovrtest$byClass[["Sensitivity"]],
"Specificity" = cf_ovrtest$byClass[["Specificity"]])
cm_ovrtrain <- table(data_training_sfenom$promo, pred_ovrtrain)
cf_ovrtrain <- confusionMatrix(cm_ovrtrain)
akurasi_ovrtrain <- data.frame("Accuracy" = cf_ovrtrain$overall[["Accuracy"]],
"Balanced Accuracy" = cf_ovrtrain$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_ovrtrain$byClass[["Sensitivity"]],
"Specificity" = cf_ovrtrain$byClass[["Specificity"]])
cm_hyb <- table(data_testing_sfenom$promo, pred_hyb)
cf_hyb <- confusionMatrix(cm_hyb)
akurasi_hyb <- data.frame("Accuracy" = cf_hyb$overall[["Accuracy"]],
"Balanced Accuracy" = cf_hyb$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_hyb$byClass[["Sensitivity"]],
"Specificity" = cf_hyb$byClass[["Specificity"]])
cm_hybtrain <- table(data_training_sfenom$promo, pred_hybtrain)
cf_hybtrain <- confusionMatrix(cm_hybtrain)
akurasi_hybtrain <- data.frame("Accuracy" = cf_hybtrain$overall[["Accuracy"]],
"Balanced Accuracy" = cf_hybtrain$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_hybtrain$byClass[["Sensitivity"]],
"Specificity" = cf_hybtrain$byClass[["Specificity"]])
cm_2 <- table(data_testing_diskret$promo, pred_2)
cf_2 <- confusionMatrix(cm_2)
akurasi_2 <- data.frame("Accuracy" = cf_2$overall[["Accuracy"]],
"Balanced Accuracy" = cf_2$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_2$byClass[["Sensitivity"]],
"Specificity" = cf_2$byClass[["Specificity"]])
cm_2train <- table(data_training_diskret$promo, pred_2train)
cf_2train <- confusionMatrix(cm_2train)
akurasi_2train <- data.frame("Accuracy" = cf_2train$overall[["Accuracy"]],
"Balanced Accuracy" = cf_2train$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_2train$byClass[["Sensitivity"]],
"Specificity" = cf_2train$byClass[["Specificity"]])
cm_3 <- table(data_testing_fenom$promo, pred_3)
cf_3 <- confusionMatrix(cm_3)
akurasi_3 <- data.frame("Accuracy" = cf_3$overall[["Accuracy"]],
"Balanced Accuracy" = cf_3$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_3$byClass[["Sensitivity"]],
"Specificity" = cf_3$byClass[["Specificity"]])
cm_3train <- table(data_training_fenom$promo, pred_3train)
cf_3train <- confusionMatrix(cm_3train)
akurasi_3train <- data.frame("Accuracy" = cf_3train$overall[["Accuracy"]],
"Balanced Accuracy" = cf_3train$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_3train$byClass[["Sensitivity"]],
"Specificity" = cf_3train$byClass[["Specificity"]])
# cm_4 <- table(data_testing_newvar$promo, pred_4)
# cf_4 <- confusionMatrix(cm_4)
# akurasi_4 <- data.frame("Accuracy" = cf_4$overall[["Accuracy"]],
# "Balanced Accuracy" = cf_4$byClass[["Balanced Accuracy"]],
# "Sensitivity" = cf_4$byClass[["Sensitivity"]],
# "Specificity" = cf_4$byClass[["Specificity"]])
# cm_4train <- table(data_training_newvar$promo, pred_4train)
# cf_4train <- confusionMatrix(cm_4train)
# akurasi_4train <- data.frame("Accuracy" = cf_4train$overall[["Accuracy"]],
# "Balanced Accuracy" = cf_4train$byClass[["Balanced Accuracy"]],
# "Sensitivity" = cf_4train$byClass[["Sensitivity"]],
# "Specificity" = cf_4train$byClass[["Specificity"]])
cm_5 <- table(data_testing_anom$promo, pred_5)
cf_5 <- confusionMatrix(cm_5)
akurasi_5 <- data.frame("Accuracy" = cf_5$overall[["Accuracy"]],
"Balanced Accuracy" = cf_5$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_5$byClass[["Sensitivity"]],
"Specificity" = cf_5$byClass[["Specificity"]])
cm_5train <- table(data_training_anom$promo, pred_5train)
cf_5train <- confusionMatrix(cm_5train)
akurasi_5train <- data.frame("Accuracy" = cf_5train$overall[["Accuracy"]],
"Balanced Accuracy" = cf_5train$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_5train$byClass[["Sensitivity"]],
"Specificity" = cf_5train$byClass[["Specificity"]])
# Model SFENOM
cm_6 <- table(data_testing_sfenom$promo, pred_6)
cf_6 <- confusionMatrix(cm_6)
akurasi_6 <- data.frame("Accuracy" = cf_6$overall[["Accuracy"]],
"Balanced Accuracy" = cf_6$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_6$byClass[["Sensitivity"]],
"Specificity" = cf_6$byClass[["Specificity"]])
cm_6train <- table(data_training_sfenom$promo, pred_6train)
cf_6train <- confusionMatrix(cm_6train)
akurasi_6train <- data.frame("Accuracy" = cf_6train$overall[["Accuracy"]],
"Balanced Accuracy" = cf_6train$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_6train$byClass[["Sensitivity"]],
"Specificity" = cf_6train$byClass[["Specificity"]])
# Model FENOM Regresi
cm_7 <- table(data_testing_fenomregresi$promo, pred_7)
cf_7 <- confusionMatrix(cm_7)
akurasi_7 <- data.frame("Accuracy" = cf_7$overall[["Accuracy"]],
"Balanced Accuracy" = cf_7$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_7$byClass[["Sensitivity"]],
"Specificity" = cf_7$byClass[["Specificity"]])
cm_7train <- table(data_training_fenomregresi$promo, pred_7train)
cf_7train <- confusionMatrix(cm_7train)
akurasi_7train <- data.frame("Accuracy" = cf_7train$overall[["Accuracy"]],
"Balanced Accuracy" = cf_7train$byClass[["Balanced Accuracy"]],
"Sensitivity" = cf_7train$byClass[["Sensitivity"]],
"Specificity" = cf_7train$byClass[["Specificity"]])Akurasi
Akurasi Testing
akurasitest <- rbind("Under Balancing" = akurasi_under, "Hybrid Balancing" = akurasi_hyb, "Over Balancing" = akurasi_ovr,"Train" = akurasi, "Diskretasi" = akurasi_2, "FENOM" = akurasi_3, "Anom" = akurasi_5)
akurasitest## Accuracy Balanced.Accuracy Sensitivity Specificity
## Under Balancing 0.7424242 0.7255359 0.8648649 0.5862069
## Hybrid Balancing 0.7575758 0.7417582 0.7692308 0.7142857
## Over Balancing 0.7121212 0.6716687 0.7551020 0.5882353
## Train 0.7916667 0.7823529 0.8000000 0.7647059
## Diskretasi 0.7727273 0.7444444 0.8222222 0.6666667
## FENOM 0.8030303 0.8193033 0.7924528 0.8461538
## Anom 0.7916667 0.7823529 0.8000000 0.7647059(akurasitest[6,3] + akurasitest[6,4])/2## [1] 0.8193033Akurasi Train
akurasitraining <- rbind("Under Balancing" = akurasi_undertrain, "Hybrid Balancing" = akurasi_hybtrain, "Over Balancing" = akurasi_ovrtrain, "Train" = akurasitrain, "Diskretasi" = akurasi_2train, "FENOM" = akurasi_3train, "Anom" = akurasi_5train)
akurasitraining## Accuracy Balanced.Accuracy Sensitivity Specificity
## Under Balancing 0.7424242 0.7255359 0.8648649 0.5862069
## Hybrid Balancing 0.8880309 0.8861447 0.8907104 0.8815789
## Over Balancing 0.9575290 0.9623603 0.9497207 0.9750000
## Train 0.8251748 0.8705882 0.8000000 0.9411765
## Diskretasi 0.9150579 0.9242156 0.9032258 0.9452055
## FENOM 0.7992278 0.8157994 0.7884615 0.8431373
## Anom 0.8251748 0.8705882 0.8000000 0.9411765Akurasi all
data.frame("Keterangan" = rownames(akurasitest),
"Test_Accuracy" = akurasitest$Accuracy,
"Train_Accuracy" = akurasitraining$Accuracy,
"Test_B.Accuracy" = akurasitest$Balanced.Accuracy,
"Train_B.Accuracy" = akurasitraining$Balanced.Accuracy)## Keterangan Test_Accuracy Train_Accuracy Test_B.Accuracy
## 1 Under Balancing 0.7424242 0.7424242 0.7255359
## 2 Hybrid Balancing 0.7575758 0.8880309 0.7417582
## 3 Over Balancing 0.7121212 0.9575290 0.6716687
## 4 Train 0.7916667 0.8251748 0.7823529
## 5 Diskretasi 0.7727273 0.9150579 0.7444444
## 6 FENOM 0.8030303 0.7992278 0.8193033
## 7 Anom 0.7916667 0.8251748 0.7823529
## Train_B.Accuracy
## 1 0.7255359
## 2 0.8861447
## 3 0.9623603
## 4 0.8705882
## 5 0.9242156
## 6 0.8157994
## 7 0.8705882