library(e1071)
## Warning: package 'e1071' was built under R version 3.5.3
library(devtools)
## Warning: package 'devtools' was built under R version 3.5.3
## Loading required package: usethis
## Warning: package 'usethis' was built under R version 3.5.3
library(caret)
## Warning: package 'caret' was built under R version 3.5.3
## Loading required package: lattice
## Warning: package 'lattice' was built under R version 3.5.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.5.3
data=read.delim("clipboard", header=T)
databaru=data[,2:24]
#90:10
#Training sample with n observation
n.90=round(nrow(data)*0.9)
n.90
## [1] 176
#Training Sample with n observations
set.seed(12345)
sample.90=sample(seq_len(nrow(databaru)),size = n.90)
train.90=data[sample.90,]
test.90=data[-sample.90,]
##SUPPORT VECTOR MACHINE
data.svm.90 <- svm(Status ~., data = train.90) #
## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'MDVP_Jitter_Abs' constant. Cannot scale data.
data.svm.90
##
## Call:
## svm(formula = Status ~ ., data = train.90)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
##
## Number of Support Vectors: 176
#pengujian model SVM data training
pred.90 <- predict(data.svm.90,train.90)
pred.90
## 141 170 147 171 88 32 62
## Parkinson Sehat Parkinson Sehat Parkinson Sehat Sehat
## 96 137 185 7 29 135 1
## Parkinson Parkinson Sehat Parkinson Parkinson Parkinson Parkinson
## 71 84 70 72 190 168 80
## Parkinson Parkinson Parkinson Parkinson Sehat Sehat Parkinson
## 57 176 122 111 67 119 92
## Parkinson Sehat Parkinson Parkinson Parkinson Parkinson Parkinson
## 38 81 131 182 31 192 60
## Parkinson Parkinson Parkinson Parkinson Sehat Sehat Parkinson
## 58 139 143 97 21 172 194
## Parkinson Parkinson Parkinson Parkinson Parkinson Sehat Sehat
## 142 118 40 49 9 186 149
## Parkinson Parkinson Parkinson Sehat Parkinson Sehat Parkinson
## 173 140 120 46 163 104 179
## Sehat Parkinson Parkinson Sehat Parkinson Parkinson Parkinson
## 102 12 161 33 107 35 132
## Parkinson Parkinson Parkinson Sehat Parkinson Sehat Parkinson
## 100 129 184 123 20 77 144
## Parkinson Parkinson Sehat Parkinson Parkinson Parkinson Parkinson
## 87 63 158 41 6 75 115
## Parkinson Sehat Parkinson Parkinson Parkinson Parkinson Parkinson
## 78 187 18 101 59 164 3
## Parkinson Sehat Parkinson Parkinson Parkinson Parkinson Parkinson
## 17 34 90 55 86 148 98
## Parkinson Sehat Parkinson Parkinson Parkinson Parkinson Parkinson
## 85 193 189 73 52 178 74
## Parkinson Sehat Sehat Parkinson Sehat Parkinson Parkinson
## 10 39 28 114 91 174 48
## Parkinson Parkinson Parkinson Parkinson Parkinson Sehat Sehat
## 82 146 127 22 19 133 136
## Parkinson Parkinson Parkinson Parkinson Parkinson Parkinson Parkinson
## 124 177 65 169 152 165 113
## Parkinson Sehat Sehat Sehat Parkinson Parkinson Parkinson
## 25 42 188 106 50 51 89
## Parkinson Parkinson Sehat Parkinson Sehat Sehat Parkinson
## 66 13 183 195 112 44 69
## Sehat Parkinson Parkinson Sehat Parkinson Sehat Parkinson
## 126 160 53 159 11 121 79
## Parkinson Parkinson Sehat Parkinson Parkinson Parkinson Parkinson
## 130 167 138 180 8 93 150
## Parkinson Sehat Parkinson Parkinson Parkinson Parkinson Parkinson
## 24 64 116 54 76 134 15
## Parkinson Sehat Parkinson Sehat Parkinson Parkinson Parkinson
## 103 109 27 162 151 125 26
## Parkinson Parkinson Parkinson Parkinson Parkinson Parkinson Parkinson
## 156 108 105 117 94 83 5
## Parkinson Parkinson Parkinson Parkinson Parkinson Parkinson Parkinson
## 23 61 47 95 56 175 181
## Parkinson Sehat Sehat Parkinson Parkinson Sehat Parkinson
## 16
## Parkinson
## Levels: Parkinson Sehat
#80:20
#Training sample with n observation
n.80=round(nrow(data)*0.8)
n.80
## [1] 156
#Training Sample with n observations
set.seed(12345)
sample.80=sample(seq_len(nrow(databaru)),size = n.80)
train.80=data[sample.80,]
test.80=data[-sample.80,]
##SUPPORT VECTOR MACHINE
data.svm.80 <- svm(Status ~., data = train.80) #
## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'MDVP_Jitter_Abs' constant. Cannot scale data.
data.svm.80
##
## Call:
## svm(formula = Status ~ ., data = train.80)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
##
## Number of Support Vectors: 156
#pengujian model SVM data training
pred.80 <- predict(data.svm.80,train.80)
#70:30
#Training sample with n observation
n.70=round(nrow(data)*0.7)
n.70
## [1] 136
#Training Sample with n observations
set.seed(12345)
sample.70=sample(seq_len(nrow(databaru)),size = n.70)
train.70=data[sample.70,]
test70=data[-sample.70,]
##SUPPORT VECTOR MACHINE
data.svm.70 <- svm(Status ~., data = train.70) #
## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'MDVP_Jitter_Abs' constant. Cannot scale data.
data.svm.70
##
## Call:
## svm(formula = Status ~ ., data = train.70)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
##
## Number of Support Vectors: 136
#pengujian model SVM data training
pred.70 <- predict(data.svm.70,train.70)