#Mengimput Dataset dan Packages yang di Butuhkan
library(e1071)
library(caret)
## Loading required package: ggplot2
## Loading required package: lattice
library(devtools)
## Loading required package: usethis
library(readxl)
uasbidikmisi <- read_excel("C:/Users/ASUS/Downloads/datauasbidikmisi.xlsx")
head(uasbidikmisi)
## # A tibble: 6 × 13
## X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 1 2 3 2 1 2 3 2 2 2 4
## 2 4 1 2 3 3 1 3 3 2 2 2 2
## 3 3 1 3 2 2 1 3 3 2 1 2 1
## 4 4 1 3 2 2 1 3 3 2 1 2 1
## 5 1 1 2 2 2 1 3 3 1 1 2 5
## 6 1 1 2 2 3 1 2 3 1 1 1 3
## # … with 1 more variable: bidikmisiclass <dbl>
## X1 X2 X3 X4 X5
## 0 0 0 0 0
## X6 X7 X8 X9 X10
## 0 0 0 0 0
## X11 X12 bidikmisiclass
## 0 0 0
#Membuat Data Training untuk Pembentukan MOdel dan Data Testing untuk Melakukan Prediksi
set.seed(123)
intrain<-sample(nrow(uasbidikmisi),nrow(uasbidikmisi)*0.8)
bidikmisi_train<-uasbidikmisi[intrain,]
bidikmisi_test<-uasbidikmisi[-intrain,]
#Pembentukan Model untuk Klasifikasi Naive Bayes Menggunakan Data Training
modelNB<-naiveBayes(bidikmisiclass~.,data=bidikmisi_train)
modelNB
##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## 0 1
## 0.03328882 0.96671118
##
## Conditional probabilities:
## X1
## Y [,1] [,2]
## 0 1.796562 1.246133
## 1 1.726862 1.210809
##
## X2
## Y [,1] [,2]
## 0 1.375358 0.9210063
## 1 1.330291 0.8549766
##
## X3
## Y [,1] [,2]
## 0 2.502865 0.6986872
## 1 2.499186 0.6850191
##
## X4
## Y [,1] [,2]
## 0 2.402579 0.6772021
## 1 2.400123 0.6613406
##
## X5
## Y [,1] [,2]
## 0 2.002149 0.5571262
## 1 1.994425 0.5261072
##
## X6
## Y [,1] [,2]
## 0 1.490688 0.5685162
## 1 1.488234 0.5483007
##
## X7
## Y [,1] [,2]
## 0 2.684814 0.4902768
## 1 2.677997 0.4914367
##
## X8
## Y [,1] [,2]
## 0 2.989971 0.1251797
## 1 2.979896 0.1911105
##
## X9
## Y [,1] [,2]
## 0 2.344556 0.8257802
## 1 2.439911 0.7700809
##
## X10
## Y [,1] [,2]
## 0 1.762178 0.7530311
## 1 1.749556 0.7178181
##
## X11
## Y [,1] [,2]
## 0 1.921920 0.2683939
## 1 1.906216 0.2915315
##
## X12
## Y [,1] [,2]
## 0 2.795845 2.183157
## 1 2.732856 1.460067
#Melakukan Prediksi Menggunakan Data Testing
prediksiNB_test<-predict(modelNB,bidikmisi_test)
hasil_testNB=confusionMatrix(table(prediksiNB_test,bidikmisi_test$bidikmisiclass))
hasil_testNB
## Confusion Matrix and Statistics
##
##
## prediksiNB_test 0 1
## 0 4 65
## 1 357 10058
##
## Accuracy : 0.9597
## 95% CI : (0.9558, 0.9634)
## No Information Rate : 0.9656
## P-Value [Acc > NIR] : 0.9994
##
## Kappa : 0.0076
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.0110803
## Specificity : 0.9935790
## Pos Pred Value : 0.0579710
## Neg Pred Value : 0.9657225
## Prevalence : 0.0344334
## Detection Rate : 0.0003815
## Detection Prevalence : 0.0065815
## Balanced Accuracy : 0.5023297
##
## 'Positive' Class : 0
##