Model Datmin (1).docx

> library(party) Loading required Loading required Loading required Loading required Loading required Loading required

package: package: package: package: package: package:

grid mvtnorm modeltools stats4 strucchange zoo

Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric Loading required package: sandwich Warning messages: 1: package ‘ROCR’ was built under R version 3.5.1 2: package ‘gplots’ was built under R version 3.5.1 3: package ‘party’ was built under R version 3.5.1 4: package ‘modeltools’ was built under R version 3.5.1 5: package ‘strucchange’ was built under R version 3.5.1 6: package ‘zoo’ was built under R version 3.5.1 7: package ‘sandwich’ was built under R version 3.5.1 > library(e1071) Warning message: package ‘e1071’ was built under R version 3.5.1 > library(rpart) > library(nnet) > library(caret) Loading required package: lattice Loading required package: ggplot2 Warning messages: 1: package ‘caret’ was built under R version 3.5.1 2: package ‘ggplot2’ was built under R version 3.5.1 > library(ROCR) > library(VIM) Loading required package: colorspace Loading required package: data.table data.table 1.11.6 Latest news: r-datatable.com VIM is ready to use. Since version 4.0.0 the GUI is in its own package VIMGUI. Please use the package to use the new (and old) GUI. Suggestions and bug-reports can be submitted at: https://github.com/alexko wa/VIM/issues Attaching package: ‘VIM’ The following object is masked from ‘package:datasets’: sleep Warning messages: 1: package ‘VIM’ was built under R version 3.5.1 2: package ‘colorspace’ was built under R version 3.5.1 3: package ‘data.table’ was built under R version 3.5.1 > #BUAT SET DATA TRAINING DAN TESTING > dataku<-datajur_ > dataku$TARGET<-as.factor(as.character(dataku$TARGET)) > View(dataku) > n<-nrow(dataku)

> p<-ncol(dataku) > m<-p-1 > Sample<-sample(1:n,0.30*n) #untuk ambil sampel, datanya > testing<-dataku[Sample,] #Sample=nomor sampel #, kosong itu maksudnya se mua kolom > dataku$TARGET<-as.factor(as.character(dataku$TARGET)) > learning<-dataku[-Sample,] > write.table(testing, file="testing.csv", sep=",",row.names=F) > write.table(learning, file="learning.csv", sep=",",row.names=F)

1. CTREE > C1<-print("ID3") [1] "ID3" > library(party) > model_ct <- ctree(TARGET~MIN+PTS+FG.+FT.+REB+AST+STL+BLK+TOV, data=learning) > plot(model_ct) >

> #Prediksi Test Data > prediksi_ct <- predict(model_ct,testing) #prediksi itu bikin satu yang kosong, buat prediksi dari model dengan data testing > prediksi_ct [1] 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 [56] 1 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [111] 1 1 1 0 1 0 1 0 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 [166] 0 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 [221] 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 [276] 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 [331] 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 [386] 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 Levels: 0 1 > > dectree1<-table(testing$TARGET, prediksi_ct)

kolom baru 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1

> print(dectree1) prediksi_ct 0 1 0 47 112 1 26 217 > > akurasiID3 <- (sum(diag(dectree1)))/sum(dectree1) > presisiID3 <- dectree1[1,1]/(dectree1[1,1]+dectree1[2,1]) > recallID3 <- dectree1[1,1]/(dectree1[1,1]+dectree1[1,2]) > F1ID3 <- (2*presisiID3*recallID3)/(presisiID3+recallID3) > tprID3 <-recallID3 > tnrID3 <-dectree1[2,2]/(dectree1[2,2]+dectree1[2,1]) > > roc.prediction1<-prediction(as.numeric(as.factor(prediksi_ct)), as.numeric(a s.factor(testing$TARGET))) > roc.tpr.fpr1<-performance(roc.prediction1, "tpr","fpr") > roc.auc1<-performance(roc.prediction1, "auc") > plot(roc.tpr.fpr1, col="red", lty=3)

> abline(a=0, b=1) > luasaucID3<-print(paste([email protected])) [1] "0.594300799751533" > > cat("AKURASI =",akurasiID3, + "PRESISI =",presisiID3, + "RECALL =",recallID3, + "F1 =",F1ID3, + "TPR =", tprID3, + "TNR =", tnrID3, + "Luas AUC =",luasaucID3) AKURASI = 0.6567164 PRESISI = 0.6438356 RECALL = 0.2955975 F1 = 0.4051724 TPR = 0.2955975 TNR = 0.8930041 Luas AUC = 0.594300799751533

2. CART > > #CART > C2<-print("CART") [1] "CART" > library(rpart) > dectree2 <- rpart(TARGET~MIN+PTS+FG.+FT.+REB+AST+STL+BLK+TOV, data=learn ing, + control = rpart.control(minsplit = 2)) > par(xpd = NA, mar= rep (0.7,4)) > plot(dectree2, compress= TRUE)

> text(dectree2, cex = 0.7, use.n=TRUE, fancy=FALSE, all=TRUE) > > prediksi2<- predict(dectree2,testing,type = "class") > prediksi2 988 649 1057 659 959 92 186 806 905 493 957 1211 204 1331 747 821 694 169 502 797 1099 1250 0 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 0 1 0 1 1 968 702 775 1278 271 452 1277 135 633 11 133 816 423 526 435 224 1226 1130 197 1058 184 921 1 1 0 1 0 1 1 1 1 0 1 1 0 1 0 1 0 1 1 1 1 1 867 551 910 240 536 91 811 1196 1173 41 623 851 145 8 805 256 876 636 516 792 412 53 1 1 0 1 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 1 0 916 1318 433 1086 474 395 1195 1301 1117 1080 125 108 1050 118 233 634 579 130 960 1306 622 843

697 1 32 1 393 0 538

0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 1 1 0 1 0 956 101 978 1032 1111 926 177 740 1160 1185 113 1124 244 1034 679 176 525 2 1310 478 1002 270 674 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 653 444 688 975 940 704 162 317 232 153 1031 554 54 739 583 533 1060 407 519 1247 168 1120 134 0 1 0 0 0 1 0 1 0 0 1 1 0 0 0 0 1 1 0 0 1 1 1 592 497 67 196 930 539 1286 175 228 1249 928 1337 332 25 315 1165 648 330 637 537 545 421 951 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 104 1055 736 936 1104 1141 1303 654 370 384 1272 1240 84 1115 129 766 1065 212 964 754 128 379 630 1 0 0 1 0 0 1 0 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 344 324 1041 347 424 350 1059 467 788 282 63 1154 397 1218 79 261 1159 216 599 1215 831 173 530 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 1 0 640 1243 206 550 1208 356 247 1001 1112 727 279 886 1294 571 62 544 619 860 1271 447 1207 1126 687 1 1 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1 591 93 998 195 787 4 741 791 518 1235 720 580 853 715 602 42 1264 1169 57 1155 166 189 1246 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 0 0 1 1 0 115 1157 295 673 650 190 808 1007 331 638 986 349 594 1323 306 391 1119 1190 207 170 430 122 285 0 0 1 1 1 1 0 1 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 782 165 836 328 683 854 214 1177 246 477 889 913 1010 241 778 411 1075 404 473 529 457 105 357 1 0 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 0 0 0 0 1 732 341 677 802 1293 1320 979 596 98 923 718 217 527 764 103 773 420 1192 112 512 380 179 1268 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1 598 1110 499 568 1254 427 39 49 336 278 1106 273 726 684 1028 373 70 1221 1233 686 491 126 1036 1 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1140 1087 36 1182 761 1073 208 906 1334 311 759 47 617 20 268 875 796 879 1267 981 355 942 976 1 0 1 0 1 1 1 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1 372 1325 40 803 33 1066 508 749 709 991 99 287 1285 1093 446 146 625 1248 657 248 781 575 346 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1189 585 235 553 486 716 201 22 950 19 297 0 0 1 1 0 0 0 1 0 1 1 Levels: 0 1

> CM2<-table(testing$TARGET, prediksi2) > print(CM2) prediksi2 0 1 0 88 71 1 65 178 > prediksi2dec<- predict(dectree2,testing) [,2] > pred<-prediction(prediksi2dec, testing$TARGET) > > akurasiCART <- (sum(diag(CM2)))/sum(CM2) > presisiCART <- CM2[1,1]/(CM2[1,1]+CM2[2,1]) > recallCART <- CM2[1,1]/(CM2[1,1]+CM2[1,2]) > F1CART <- (2*presisiCART*recallCART)/(presisiCART+recallCART) > tprCART<-recallCART > tnrCART <-CM2[2,2]/(CM2[2,2]+CM2[2,1]) > > > roc.prediction2<-prediction(as.numeric(as.factor(prediksi2)), as.numeric (as.factor(testing$TARGET))) > roc.tpr.fpr2<-performance(roc.prediction2, "tpr","fpr") > roc.auc2<-performance(roc.prediction2, "auc") > plot(roc.tpr.fpr2, col="red", lty=3)

> abline(a=0, b=1) > luasaucCART<-print(paste([email protected])) [1] "0.642984703781349" > > cat("AKURASI =",akurasiCART, + "PRESISI =",presisiCART, + "RECALL =",recallCART, + "F1 =",F1CART, + "TPR =", tprCART, + "TNR =", tnrCART, + "Luas AUC =",luasaucCART) AKURASI = 0.6616915 PRESISI = 0.5751634 RECALL = 0.5534591 F1 = 0.5641026 TPR = 0.5534591 TNR = 0.7325103 Luas AUC = 0.642984703781349

3. NAÏVE BAYES > #NAIVE BAYES = C3 > c3<-print("Naive Bayes") [1] "Naive Bayes" > bayes1 <- naiveBayes(TARGET~MIN+PTS+FG.+FT.+REB+AST+STL+BLK+TOV, data=le arning) > bayes1 Naive Bayes Classifier for Discrete Predictors Call: naiveBayes.default(x = X, y = Y, laplace = laplace) A-priori probabilities: Y 0 1 0.3731343 0.6268657 Conditional probabilities: MIN Y [,1] [,2] 0 13.97457 6.123360 1 19.62058 8.522495 PTS Y

[,1] [,2] 0 4.841143 2.681170 1 7.751020 4.588217 FG.

Y

[,1] [,2] 0 42.04629 6.444684 1 45.12177 5.494638 FT.

Y

[,1] [,2] 0 69.24657 10.830173 1 70.99048 9.942363 REB

Y

[,1] [,2] 0 2.181429 1.336129 1 3.427721 2.050445 AST

Y

[,1] [,2] 0 1.133143 0.9221495 1 1.565136 1.2524152 STL

Y

[,1] [,2] 0 0.4920000 0.3257682 1 0.6785714 0.4195601 BLK

Y

[,1] [,2] 0 0.2428571 0.2564757 1 0.3867347 0.3686998 TOV

Y

[,1] [,2] 0 0.9122857 0.5203514 1 1.3324830 0.7571011

> > prediksib1 <- predict(bayes1,testing) > prediksib1 [1] 0 0 1 0 1 0 1 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 0 0 1 0 0 0 1 1 1 [45] 1 1 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 [89] 1 0 0 0 1 0 0 1 0 1 0 1 1 0 0 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 1 [133] 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 1 [177] 0 1 0 0 1 1 0 1 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 1 0 0 1 1 0 1 1 1 0 0 1 0 0 [221] 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 [265] 0 1 1 0 0 1 0 0 0 0 1 1 1 0 1 1 0 1 0 0 1 0 0 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 0 [309] 1 0 0 0 0 0 1 1 1 0 1 0 0 1 1 0 1 0 0 0 1 0 1 1 0 1 0 0 1 1 1 1 1 1 0 0 0 0 1 0 [353] 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1 0 0 0 0 0 1 0 [397] 0 1 1 0 1 0 Levels: 0 1 > > CMnb<-table(testing$TARGET, prediksib1) > print(CMnb) prediksib1 0 1 0 114 45 1 103 140 > > akurasinb<-(sum(diag(CMnb)))/sum(CMnb) > presisinb<-CMnb[1,1]/(CMnb[1,1]+CMnb[2,1]) > recallnb<-CMnb[1,1]/(CMnb[1,1]+CMnb[1,2]) > F1nb<-(2*presisinb*recallnb)/(presisinb+recallnb) > tprnb<-recallnb > tnrnb<-CMnb[2,2]/(CMnb[2,2]+CMnb[2,1]) > > roc.predictionnb<-prediction(as.numeric(as.factor(prediksib1)), ic(as.factor(testing$TARGET))) > roc.tpr.fprnb<-performance(roc.predictionnb, "tpr","fpr") > roc.aucnb<-performance(roc.predictionnb, "auc") > plot(roc.tpr.fprnb, col="red", lty=3)

1 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1

as.numer

> abline(a=0, b=1) > luasaucnb<-print(paste([email protected])) [1] "0.646556409659135" > > cat("AKURASI =",akurasinb, + "PRESISI =",presisinb, + "RECALL =",recallnb, + "F1 =",F1nb, + "TPR =", tprnb, + "TNR =", tnrnb, + "Luas AUC =",luasaucnb) AKURASI = 0.6318408 PRESISI = 0.5253456 RECALL = 0.7169811 F1 = 0.606383 T PR = 0.7169811 TNR = 0.5761317 Luas AUC = 0.646556409659135

4. NEURAL NETWORK > #NEURAL NETWORK = C4 > > C4<-print("Neural Network") [1] "Neural Network" > > neuraldatanba <- train(TARGET~MIN+PTS+FG.+FT.+REB+AST+STL+BLK+TOV, data = learning, method="nnet", + trace = FALSE, preProc = c("center","scale"))#tra in > > prediksinnt <- predict(neuraldatanba,testing) # predict > CMnn<-table(testing$TARGET, prediksinnt) > print(CMnn) prediksinnt 0 1 0 80 79 1 46 197

> > akurasinn <- (sum(diag(CMnn)))/sum(CMnn) > presisinn <- CMnn[1,1]/(CMnn[1,1]+CMnn[2,1]) > recallnn <- CMnn[1,1]/(CMnn[1,1]+CMnn[1,2]) > F1nn <- (2*presisinn*recallnn)/(presisinn+recallnn) > tprnn<-recallnn > tnrnn<-CMnn[2,2]/(CMnn[2,2]+CMnn[2,1]) > > roc.predictionnn<-prediction(as.numeric(as.factor(prediksinnt)), as.nume ric(as.factor(testing$TARGET))) > roc.tpr.fprnn<-performance(roc.predictionnn, "tpr","fpr") > roc.aucnn<-performance(roc.predictionnn, "auc") > plot(roc.tpr.fprnn, col="red", lty=3)

> abline(a=0, b=1) > luasaucnn<-print(paste([email protected])) [1] "0.656922121282708" > > cat("AKURASI =",akurasinn, + "PRESISI =",presisinn, + "RECALL =",recallnn, + "F1 =",F1nn, + "TPR =", tprnn, + "TNR =", tnrnn, + "Luas AUC =",luasaucnn) AKURASI = 0.6890547 PRESISI = 0.6349206 RECALL = 0.5031447 F1 = 0.5614035 TPR = 0.5031447 TNR = 0.8106996 Luas AUC = 0.656922121282708

5. SVM > #SVM=C5 > C5<-print("SVM") [1] "SVM" > svm_model <- svm(TARGET~MIN+PTS+FG.+FT.+REB+AST+STL+BLK+TOV, data=learni ng) > summary(svm_model) Call: svm(formula = TARGET ~ MIN + PTS + FG. + FT. + REB + AST + STL + BLK + TOV , data = learning) Parameters: SVM-Type: SVM-Kernel: cost: gamma:

C-classification radial 1 0.1111111

Number of Support Vectors:

680

( 312 368 ) Number of Classes:

2

Levels: 0 1

> > prediksisvm <- predict(svm_model,testing) # predict > CMSVM<-table(testing$TARGET, prediksisvm) > print(CMSVM) prediksisvm 0 1 0 60 99 1 29 214 > roc.prediction5<-prediction(as.numeric(as.factor(prediksisvm)), as.numer ic(as.factor(testing$TARGET))) > roc.tpr.fpr5<-performance(roc.prediction5, "tpr","fpr") > roc.auc5<-performance(roc.prediction5, "auc") > plot(roc.tpr.fpr5, col="red", lty=3)

> abline(a=0, b=1) > luasauc5<-print(paste([email protected])) [1] "0.629008463390015" > > akurasi5 <- (sum(diag(CMSVM)))/sum(CMSVM) > presisi5 <- CMSVM[1,1]/(CMSVM[1,1]+CMSVM[2,1]) > recall5 <- CMSVM[1,1]/(CMSVM[1,1]+CMSVM[1,2]) > F15 <- (2*presisi5*recall5)/(presisi5+recall5) > tpr5<-recall5 > tnr5<-CMSVM[2,2]/(CMSVM[2,2]+CMSVM[2,1]) > > cat("AKURASI =",akurasi5, + "PRESISI =",presisi5, + "RECALL =",recall5, + "F1 =",F15, + "TPR =", tpr5, + "TNR =", tnr5, + "Luas AUC =",luasauc5) AKURASI = 0.681592 PRESISI = 0.6741573 RECALL = 0.3773585 F1 = 0.483871 TP R = 0.3773585 TNR = 0.8806584 Luas AUC = 0.629008463390015

6. KNN > #KNN > c6<-print("kNN") [1] "kNN" > modelknn <- train(TARGET~MIN+PTS+FG.+FT.+REB+AST+STL+BLK+TOV, data=learn ing, method="knn") > summary(modelknn) Length Class Mode learn 2 -nonelist k 1 -nonenumeric theDots 0 -nonelist xNames 9 -nonecharacter problemType 1 -nonecharacter tuneValue 1 data.frame list obsLevels 2 -nonecharacter param 0 -nonelist > modelknn<-train(x=learning[,-1], y=learning[,1], method = "knn") > modelknn k-Nearest Neighbors 938 samples 9 predictor 2 classes: '0', '1' No pre-processing Resampling: Bootstrapped (25 reps) Summary of sample sizes: 938, 938, 938, 938, 938, 938, ... Resampling results across tuning parameters: k 5 7 9

Accuracy 0.6376204 0.6466751 0.6442037

Kappa 0.2184198 0.2333960 0.2232223

Accuracy was used to select the optimal model using the largest value. The final value used for the model was k = 7. > > prediksiknn<- predict(modelknn,testing) # predict > CMknn<-table(testing$TARGET, prediksiknn) > print(CMknn) prediksiknn 0 1 0 70 89 1 58 185 > > akurasiknn <- (sum(diag(CMknn)))/sum(CMknn) > presisiknn <- CMknn[1,1]/(CMknn[1,1]+CMknn[2,1]) > recallknn <- CMknn[1,1]/(CMknn[1,1]+CMknn[1,2]) > F1knn <- (2*presisiknn*recallknn)/(presisiknn+recallknn) > tprknn<-recallknn > tnrknn<-CMknn[2,2]/(CMknn[2,2]+CMknn[2,1]) > > roc.predictionknn<-prediction(as.numeric(as.factor(prediksiknn)), as.num eric(as.factor(testing$TARGET))) > roc.tpr.fprknn<-performance(roc.predictionknn, "tpr","fpr") > roc.aucknn<-performance(roc.predictionknn, "auc") > plot(roc.tpr.fprknn, col="red", lty=3)

> abline(a=0, b=1) > luasaucknn<-print(paste([email protected])) [1] "0.600784222377514" > > cat("AKURASI =",akurasiknn, + "PRESISI =",presisiknn, + "RECALL =",recallknn, + "F1 =",F1knn, + "TPR =", tprknn, + "TNR =", tnrknn, + "Luas AUC =",luasaucknn) AKURASI = 0.6343284 PRESISI = 0.546875 RECALL = 0.4402516 F1 = 0.4878049 T PR = 0.4402516 TNR = 0.7613169 Luas AUC = 0.600784222377514

###MENCARI METODE YANG TERBAIK > judul<-c("Metode", "Akurasi","Presisi","Recall","F1","TPR","TNR","AUC") > kolom1<-c(C1,akurasiID3,presisiID3,recallID3,F1ID3,tprID3,tnrID3,luasauc ID3) > kolom2<-c(C2,akurasiCART,presisiCART,recallCART,F1CART,tprCART,tnrCART,l uasaucCART) > kolom3<-c(C3,akurasinb,presisinb,recallnb,F1nb,tprnb,tnrnb,luasaucnb) > kolom4<-c(C4,akurasinn,presisinn,recallnn,F1nn,tprnn,tnrnn,luasaucnn) > kolom5<-c(C5,akurasiSVM,presisiSVM,recallSVM,F1SVM,tprSVM,tnrSVM,luasauc SVM) > kolom6<-c(C6,akurasiknn,presisiknn,recallknn,F1knn,tprknn,tnrknn,luasauc knn) > > kolom<-rbind(judul,kolom1,kolom2,kolom3,kolom4,kolom5, kolom6) > kolom<-data.frame(kolom) > print(kolom)