Langkah Pertama, kita masukan library neuralnet untuk proses perhitungan neural network
library(neuralnet)## Warning: package 'neuralnet' was built under R version 4.1.2langkah kedua,setelah memasukan library, kita panggil data penelitian yang sudah disiapkan.
dataku=read.csv("D:/AANG/KULIAH/DATA SCIENCE/TUGAS AKHIR/DATASEKOLAHOK.csv",sep=";",header=TRUE)
names(dataku) <- c("foto","video","berita","kegiatan","pengumuman","danabos","livechat","prestasi","gurukaryawan","struktur","bukutamu","ppdb","elearning","hasil")
dataku## foto video berita kegiatan pengumuman danabos livechat prestasi gurukaryawan
## 1 1 1 1 1 1 0 0 0 1
## 2 1 1 1 1 1 0 0 1 1
## 3 1 1 1 1 1 0 0 1 1
## 4 1 1 1 1 1 0 0 1 1
## 5 1 0 1 0 1 0 0 1 0
## 6 0 0 1 1 1 0 0 1 0
## 7 1 1 1 1 1 0 0 0 1
## 8 1 1 1 1 1 0 0 1 1
## 9 1 0 1 1 1 0 0 1 0
## 10 1 1 1 1 1 0 0 1 1
## 11 1 1 1 1 1 0 0 1 0
## 12 1 1 1 1 1 1 0 0 1
## 13 1 1 1 1 1 0 0 1 1
## 14 1 0 1 1 1 0 0 1 1
## 15 1 1 1 1 1 0 1 1 1
## 16 1 0 1 1 1 0 0 1 0
## 17 1 1 1 1 1 0 0 0 1
## 18 1 1 1 1 1 1 0 1 1
## 19 1 1 1 1 1 1 0 1 1
## 20 0 0 1 1 0 0 0 1 0
## 21 1 1 1 1 1 0 0 1 0
## 22 1 1 1 1 1 0 0 1 1
## 23 1 1 1 1 1 0 1 1 1
## 24 1 1 1 1 0 0 0 0 1
## 25 1 1 1 1 1 0 0 1 1
## 26 1 1 1 1 1 0 1 1 0
## 27 0 0 1 1 1 0 0 1 1
## 28 1 0 1 1 1 0 1 1 1
## 29 1 0 1 1 1 0 0 1 0
## 30 0 0 1 1 1 0 0 1 1
## 31 1 0 1 1 1 0 0 1 1
## 32 1 1 1 1 1 0 0 1 1
## 33 1 1 1 1 1 0 1 1 0
## 34 1 1 1 1 1 0 1 1 1
## 35 1 0 1 1 1 1 0 1 1
## 36 1 1 1 1 1 0 0 1 1
## 37 1 0 1 1 1 0 1 1 0
## 38 1 1 1 1 1 0 0 1 1
## 39 1 0 1 1 1 0 0 0 1
## 40 1 1 1 1 1 0 1 0 1
## 41 1 1 1 1 1 0 1 0 1
## 42 1 1 1 1 1 0 1 1 1
## 43 1 1 1 1 1 0 0 0 1
## 44 1 1 1 1 1 0 0 1 1
## 45 1 1 1 1 1 0 1 0 0
## 46 1 1 1 1 1 0 0 0 0
## 47 1 1 1 1 1 0 0 0 1
## 48 1 1 1 1 1 0 0 0 1
## 49 1 0 1 1 1 0 0 1 0
## 50 1 1 1 1 1 0 0 1 1
## 51 1 0 1 1 1 0 0 1 1
## 52 1 1 1 1 1 0 0 1 1
## 53 1 1 1 1 1 0 0 1 1
## 54 1 1 1 1 1 0 1 1 1
## 55 1 0 1 1 1 0 0 1 1
## 56 1 1 1 1 1 0 0 1 1
## 57 0 0 1 1 1 0 0 1 0
## 58 0 0 1 1 1 0 1 1 0
## 59 1 1 1 1 1 0 0 1 1
## 60 1 0 1 1 1 0 0 1 1
## 61 1 1 1 1 1 0 0 1 1
## 62 1 1 1 1 1 0 0 1 1
## 63 1 1 1 1 1 0 0 1 1
## 64 0 0 1 1 1 0 0 0 1
## 65 1 0 1 1 1 0 0 1 1
## 66 1 0 1 1 1 0 1 1 1
## 67 1 0 1 0 1 0 0 1 0
## 68 1 1 1 1 1 0 0 1 1
## 69 1 0 1 1 1 0 0 0 1
## 70 1 0 1 1 1 0 0 1 0
## 71 1 1 1 1 1 0 0 1 1
## 72 1 0 1 1 1 0 0 0 1
## 73 1 1 1 1 1 0 0 1 0
## struktur bukutamu ppdb elearning hasil
## 1 1 0 0 0 1
## 2 1 1 0 0 2
## 3 1 0 1 1 2
## 4 1 0 0 0 2
## 5 1 0 1 1 2
## 6 0 0 0 0 1
## 7 1 0 1 1 2
## 8 0 0 0 0 1
## 9 0 1 1 1 2
## 10 1 1 1 1 3
## 11 0 0 0 0 1
## 12 0 1 1 0 3
## 13 0 1 1 1 3
## 14 1 0 0 0 1
## 15 1 1 1 0 3
## 16 1 1 1 1 2
## 17 1 1 1 0 2
## 18 1 1 1 0 3
## 19 0 1 0 0 2
## 20 0 0 0 0 1
## 21 1 1 1 1 3
## 22 1 1 1 1 3
## 23 1 1 1 1 3
## 24 1 1 0 1 2
## 25 1 1 0 0 2
## 26 1 1 1 0 3
## 27 1 0 0 0 1
## 28 1 0 0 0 2
## 29 1 0 1 0 2
## 30 1 0 1 0 2
## 31 1 1 1 1 3
## 32 1 1 1 0 2
## 33 0 1 1 1 3
## 34 1 0 1 1 3
## 35 1 0 0 1 2
## 36 1 1 0 0 2
## 37 1 1 1 0 3
## 38 1 1 1 1 3
## 39 1 0 1 1 2
## 40 1 0 1 0 2
## 41 1 1 1 0 3
## 42 0 1 1 1 3
## 43 1 0 0 0 1
## 44 1 1 0 1 2
## 45 1 1 0 1 3
## 46 0 1 1 0 2
## 47 1 1 0 1 2
## 48 1 0 1 0 2
## 49 0 0 0 1 1
## 50 1 0 1 1 2
## 51 0 0 0 0 1
## 52 0 0 1 0 2
## 53 1 1 1 0 2
## 54 1 1 1 1 3
## 55 0 0 0 0 1
## 56 1 0 0 1 2
## 57 1 0 0 1 1
## 58 0 0 0 0 1
## 59 0 0 0 0 1
## 60 1 1 0 0 2
## 61 1 1 0 1 2
## 62 1 1 0 0 2
## 63 1 1 0 0 2
## 64 1 0 1 0 1
## 65 1 0 0 1 2
## 66 0 0 1 0 2
## 67 1 1 0 0 1
## 68 1 0 1 1 2
## 69 1 0 1 0 2
## 70 1 0 1 0 2
## 71 1 1 1 1 3
## 72 1 1 1 0 2
## 73 0 1 0 0 2Langkah selanjutnya
NN = neuralnet(hasil ~ foto + video + berita + kegiatan + pengumuman + danabos + livechat + prestasi + gurukaryawan + struktur + bukutamu + ppdb + elearning, dataku, hidden = 4 , linear.output = T)
plot(NN)dapat dilihat bahwa training dilakukan sampai beberapa kali sampai mendapatkan bobot yang tepat. Langkah selanjutnya, kita lakukan pengetesan selanjutnya dengan cara yang sama.
predict_testNN = compute(NN, dataku)
print(predict_testNN)## $neurons
## $neurons[[1]]
## foto video berita kegiatan pengumuman danabos livechat prestasi
## [1,] 1 1 1 1 1 1 0 0 0
## [2,] 1 1 1 1 1 1 0 0 1
## [3,] 1 1 1 1 1 1 0 0 1
## [4,] 1 1 1 1 1 1 0 0 1
## [5,] 1 1 0 1 0 1 0 0 1
## [6,] 1 0 0 1 1 1 0 0 1
## [7,] 1 1 1 1 1 1 0 0 0
## [8,] 1 1 1 1 1 1 0 0 1
## [9,] 1 1 0 1 1 1 0 0 1
## [10,] 1 1 1 1 1 1 0 0 1
## [11,] 1 1 1 1 1 1 0 0 1
## [12,] 1 1 1 1 1 1 1 0 0
## [13,] 1 1 1 1 1 1 0 0 1
## [14,] 1 1 0 1 1 1 0 0 1
## [15,] 1 1 1 1 1 1 0 1 1
## [16,] 1 1 0 1 1 1 0 0 1
## [17,] 1 1 1 1 1 1 0 0 0
## [18,] 1 1 1 1 1 1 1 0 1
## [19,] 1 1 1 1 1 1 1 0 1
## [20,] 1 0 0 1 1 0 0 0 1
## [21,] 1 1 1 1 1 1 0 0 1
## [22,] 1 1 1 1 1 1 0 0 1
## [23,] 1 1 1 1 1 1 0 1 1
## [24,] 1 1 1 1 1 0 0 0 0
## [25,] 1 1 1 1 1 1 0 0 1
## [26,] 1 1 1 1 1 1 0 1 1
## [27,] 1 0 0 1 1 1 0 0 1
## [28,] 1 1 0 1 1 1 0 1 1
## [29,] 1 1 0 1 1 1 0 0 1
## [30,] 1 0 0 1 1 1 0 0 1
## [31,] 1 1 0 1 1 1 0 0 1
## [32,] 1 1 1 1 1 1 0 0 1
## [33,] 1 1 1 1 1 1 0 1 1
## [34,] 1 1 1 1 1 1 0 1 1
## [35,] 1 1 0 1 1 1 1 0 1
## [36,] 1 1 1 1 1 1 0 0 1
## [37,] 1 1 0 1 1 1 0 1 1
## [38,] 1 1 1 1 1 1 0 0 1
## [39,] 1 1 0 1 1 1 0 0 0
## [40,] 1 1 1 1 1 1 0 1 0
## [41,] 1 1 1 1 1 1 0 1 0
## [42,] 1 1 1 1 1 1 0 1 1
## [43,] 1 1 1 1 1 1 0 0 0
## [44,] 1 1 1 1 1 1 0 0 1
## [45,] 1 1 1 1 1 1 0 1 0
## [46,] 1 1 1 1 1 1 0 0 0
## [47,] 1 1 1 1 1 1 0 0 0
## [48,] 1 1 1 1 1 1 0 0 0
## [49,] 1 1 0 1 1 1 0 0 1
## [50,] 1 1 1 1 1 1 0 0 1
## [51,] 1 1 0 1 1 1 0 0 1
## [52,] 1 1 1 1 1 1 0 0 1
## [53,] 1 1 1 1 1 1 0 0 1
## [54,] 1 1 1 1 1 1 0 1 1
## [55,] 1 1 0 1 1 1 0 0 1
## [56,] 1 1 1 1 1 1 0 0 1
## [57,] 1 0 0 1 1 1 0 0 1
## [58,] 1 0 0 1 1 1 0 1 1
## [59,] 1 1 1 1 1 1 0 0 1
## [60,] 1 1 0 1 1 1 0 0 1
## [61,] 1 1 1 1 1 1 0 0 1
## [62,] 1 1 1 1 1 1 0 0 1
## [63,] 1 1 1 1 1 1 0 0 1
## [64,] 1 0 0 1 1 1 0 0 0
## [65,] 1 1 0 1 1 1 0 0 1
## [66,] 1 1 0 1 1 1 0 1 1
## [67,] 1 1 0 1 0 1 0 0 1
## [68,] 1 1 1 1 1 1 0 0 1
## [69,] 1 1 0 1 1 1 0 0 0
## [70,] 1 1 0 1 1 1 0 0 1
## [71,] 1 1 1 1 1 1 0 0 1
## [72,] 1 1 0 1 1 1 0 0 0
## [73,] 1 1 1 1 1 1 0 0 1
## gurukaryawan struktur bukutamu ppdb elearning
## [1,] 1 1 0 0 0
## [2,] 1 1 1 0 0
## [3,] 1 1 0 1 1
## [4,] 1 1 0 0 0
## [5,] 0 1 0 1 1
## [6,] 0 0 0 0 0
## [7,] 1 1 0 1 1
## [8,] 1 0 0 0 0
## [9,] 0 0 1 1 1
## [10,] 1 1 1 1 1
## [11,] 0 0 0 0 0
## [12,] 1 0 1 1 0
## [13,] 1 0 1 1 1
## [14,] 1 1 0 0 0
## [15,] 1 1 1 1 0
## [16,] 0 1 1 1 1
## [17,] 1 1 1 1 0
## [18,] 1 1 1 1 0
## [19,] 1 0 1 0 0
## [20,] 0 0 0 0 0
## [21,] 0 1 1 1 1
## [22,] 1 1 1 1 1
## [23,] 1 1 1 1 1
## [24,] 1 1 1 0 1
## [25,] 1 1 1 0 0
## [26,] 0 1 1 1 0
## [27,] 1 1 0 0 0
## [28,] 1 1 0 0 0
## [29,] 0 1 0 1 0
## [30,] 1 1 0 1 0
## [31,] 1 1 1 1 1
## [32,] 1 1 1 1 0
## [33,] 0 0 1 1 1
## [34,] 1 1 0 1 1
## [35,] 1 1 0 0 1
## [36,] 1 1 1 0 0
## [37,] 0 1 1 1 0
## [38,] 1 1 1 1 1
## [39,] 1 1 0 1 1
## [40,] 1 1 0 1 0
## [41,] 1 1 1 1 0
## [42,] 1 0 1 1 1
## [43,] 1 1 0 0 0
## [44,] 1 1 1 0 1
## [45,] 0 1 1 0 1
## [46,] 0 0 1 1 0
## [47,] 1 1 1 0 1
## [48,] 1 1 0 1 0
## [49,] 0 0 0 0 1
## [50,] 1 1 0 1 1
## [51,] 1 0 0 0 0
## [52,] 1 0 0 1 0
## [53,] 1 1 1 1 0
## [54,] 1 1 1 1 1
## [55,] 1 0 0 0 0
## [56,] 1 1 0 0 1
## [57,] 0 1 0 0 1
## [58,] 0 0 0 0 0
## [59,] 1 0 0 0 0
## [60,] 1 1 1 0 0
## [61,] 1 1 1 0 1
## [62,] 1 1 1 0 0
## [63,] 1 1 1 0 0
## [64,] 1 1 0 1 0
## [65,] 1 1 0 0 1
## [66,] 1 0 0 1 0
## [67,] 0 1 1 0 0
## [68,] 1 1 0 1 1
## [69,] 1 1 0 1 0
## [70,] 0 1 0 1 0
## [71,] 1 1 1 1 1
## [72,] 1 1 1 1 0
## [73,] 0 0 1 0 0
##
## $neurons[[2]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 1.000000e+00 9.673396e-14 4.626789e-17 1.023374e-01
## [2,] 1 1.000000e+00 2.977179e-06 1.742296e-13 1.643113e-102
## [3,] 1 1.000000e+00 7.723812e-03 7.082465e-14 1.377581e-120
## [4,] 1 1.000000e+00 4.900871e-10 8.248444e-16 3.941199e-03
## [5,] 1 9.999837e-01 9.823654e-01 9.688793e-01 7.947132e-121
## [6,] 1 6.093954e-04 5.143312e-01 3.933835e-01 9.380685e-03
## [7,] 1 1.000000e+00 1.536400e-06 3.972758e-15 3.969124e-119
## [8,] 1 1.000000e+00 9.712311e-09 7.537765e-15 1.024427e-01
## [9,] 1 1.000000e+00 9.999998e-01 9.980446e-01 5.078456e-219
## [10,] 1 1.000000e+00 9.792900e-01 1.496009e-11 5.720591e-220
## [11,] 1 1.000000e+00 1.552828e-08 1.822493e-09 1.024545e-01
## [12,] 1 1.000000e+00 9.910588e-01 2.362049e-03 0.000000e+00
## [13,] 1 1.000000e+00 9.989340e-01 1.367114e-10 1.650123e-218
## [14,] 1 3.992301e-02 1.737026e-06 1.273693e-08 1.216114e-03
## [15,] 1 1.000000e+00 9.999982e-01 4.910545e-15 1.296934e-259
## [16,] 1 1.000000e+00 9.999963e-01 9.824109e-01 1.760582e-220
## [17,] 1 1.000000e+00 2.176501e-06 7.836008e-18 3.388621e-218
## [18,] 1 1.000000e+00 9.999647e-01 4.597654e-03 0.000000e+00
## [19,] 1 1.000000e+00 9.934477e-01 9.813583e-01 4.783373e-221
## [20,] 1 3.117522e-01 7.600030e-01 6.866848e-01 3.678209e-02
## [21,] 1 1.000000e+00 9.869455e-01 3.617062e-06 5.721327e-220
## [22,] 1 1.000000e+00 9.792900e-01 1.496009e-11 5.720591e-220
## [23,] 1 1.000000e+00 1.000000e+00 5.258681e-10 6.308314e-260
## [24,] 1 1.000000e+00 7.535178e-06 3.537127e-09 9.285911e-101
## [25,] 1 1.000000e+00 2.977179e-06 1.742296e-13 1.643113e-102
## [26,] 1 1.000000e+00 9.999989e-01 1.187280e-09 1.297101e-259
## [27,] 1 5.859506e-06 3.234255e-02 2.934998e-07 3.281360e-04
## [28,] 1 4.620589e-06 9.885685e-01 4.477206e-07 1.342689e-43
## [29,] 1 9.874590e-01 1.018149e-02 2.469177e-06 8.716393e-121
## [30,] 1 1.380259e-03 9.919868e-01 2.353278e-10 2.349495e-121
## [31,] 1 1.000000e+00 9.999940e-01 2.309546e-04 1.760355e-220
## [32,] 1 1.000000e+00 1.090665e-02 1.396970e-16 1.176103e-219
## [33,] 1 1.000000e+00 1.000000e+00 1.160557e-03 1.819888e-258
## [34,] 1 1.000000e+00 9.999974e-01 2.489585e-12 1.519111e-160
## [35,] 1 1.000000e+00 9.999478e-01 1.000000e+00 5.977091e-124
## [36,] 1 1.000000e+00 2.977179e-06 1.742296e-13 1.643113e-102
## [37,] 1 1.000000e+00 1.000000e+00 1.800345e-02 3.991472e-260
## [38,] 1 1.000000e+00 9.792900e-01 1.496009e-11 5.720591e-220
## [39,] 1 9.999929e-01 5.416020e-03 6.134579e-08 1.221389e-119
## [40,] 1 1.000000e+00 1.752452e-02 1.304030e-18 8.998529e-159
## [41,] 1 1.000000e+00 9.908556e-01 2.754466e-16 3.736762e-258
## [42,] 1 1.000000e+00 1.000000e+00 4.805598e-09 1.819654e-258
## [43,] 1 1.000000e+00 9.673396e-14 4.626789e-17 1.023374e-01
## [44,] 1 1.000000e+00 1.260593e-02 1.865816e-08 7.992135e-103
## [45,] 1 1.000000e+00 9.950392e-01 8.816507e-03 2.539620e-141
## [46,] 1 1.000000e+00 6.895724e-05 1.731366e-11 9.775846e-217
## [47,] 1 1.000000e+00 2.519932e-06 1.046590e-09 2.302716e-101
## [48,] 1 1.000000e+00 3.582835e-10 3.709754e-20 8.160168e-119
## [49,] 1 9.998045e-01 1.909468e-01 9.996683e-01 1.679857e-02
## [50,] 1 1.000000e+00 7.723812e-03 7.082465e-14 1.377581e-120
## [51,] 1 3.502565e-01 3.442243e-05 1.163952e-07 3.393023e-02
## [52,] 1 1.000000e+00 3.597119e-05 6.043770e-18 8.169528e-119
## [53,] 1 1.000000e+00 1.090665e-02 1.396970e-16 1.176103e-219
## [54,] 1 1.000000e+00 1.000000e+00 5.258681e-10 6.308314e-260
## [55,] 1 3.502565e-01 3.442243e-05 1.163952e-07 3.393023e-02
## [56,] 1 1.000000e+00 2.101602e-06 8.833221e-11 1.920896e-03
## [57,] 1 5.266535e-02 9.956551e-01 9.998684e-01 1.596535e-04
## [58,] 1 6.775572e-08 1.000000e+00 9.579748e-01 1.044240e-42
## [59,] 1 1.000000e+00 9.712311e-09 7.537765e-15 1.024427e-01
## [60,] 1 1.000000e+00 1.044194e-02 2.690378e-06 5.056229e-103
## [61,] 1 1.000000e+00 1.260593e-02 1.865816e-08 7.992135e-103
## [62,] 1 1.000000e+00 2.977179e-06 1.742296e-13 1.643113e-102
## [63,] 1 1.000000e+00 2.977179e-06 1.742296e-13 1.643113e-102
## [64,] 1 1.656834e-02 2.385184e-02 1.320021e-11 6.769430e-120
## [65,] 1 9.800588e-01 7.393707e-03 1.362134e-03 5.918904e-04
## [66,] 1 1.393252e-02 9.999998e-01 3.280524e-09 2.772227e-159
## [67,] 1 1.000000e+00 2.086215e-02 9.871113e-01 9.478961e-103
## [68,] 1 1.000000e+00 7.723812e-03 7.082465e-14 1.377581e-120
## [69,] 1 9.917054e-01 1.269873e-06 5.728458e-13 2.511068e-119
## [70,] 1 9.874590e-01 1.018149e-02 2.469177e-06 8.716393e-121
## [71,] 1 1.000000e+00 9.792900e-01 1.496009e-11 5.720591e-220
## [72,] 1 1.000000e+00 7.655194e-03 1.210006e-10 1.042755e-218
## [73,] 1 1.000000e+00 9.432250e-05 3.849600e-07 4.740222e-101
##
##
## $net.result
## [,1]
## [1,] 1.0010144
## [2,] 1.9979109
## [3,] 2.0057367
## [4,] 1.9595157
## [5,] 1.9999663
## [6,] 0.9994420
## [7,] 1.9979095
## [8,] 0.9999881
## [9,] 1.9879463
## [10,] 2.9905112
## [11,] 0.9998728
## [12,] 3.0000175
## [13,] 3.0104222
## [14,] 1.0014123
## [15,] 3.0115008
## [16,] 2.0039761
## [17,] 1.9979101
## [18,] 3.0067517
## [19,] 1.9984180
## [20,] 0.9998381
## [21,] 2.9982670
## [22,] 2.9905112
## [23,] 3.0115027
## [24,] 1.9979155
## [25,] 1.9979109
## [26,] 3.0115015
## [27,] 1.0019036
## [28,] 1.9743243
## [29,] 1.9953633
## [30,] 1.9792003
## [31,] 3.0112598
## [32,] 2.0089628
## [33,] 3.0103125
## [34,] 3.0115001
## [35,] 1.9858882
## [36,] 1.9979109
## [37,] 2.9930390
## [38,] 2.9905112
## [39,] 2.0033902
## [40,] 2.0156707
## [41,] 3.0022340
## [42,] 3.0115027
## [43,] 1.0010144
## [44,] 2.0106852
## [45,] 2.9974326
## [46,] 1.9979778
## [47,] 1.9979105
## [48,] 1.9979079
## [49,] 1.0023898
## [50,] 2.0057367
## [51,] 1.0010457
## [52,] 1.9979444
## [53,] 2.0089628
## [54,] 3.0115027
## [55,] 1.0010457
## [56,] 1.9791981
## [57,] 1.0085345
## [58,] 1.0034448
## [59,] 0.9999881
## [60,] 2.0084891
## [61,] 2.0106852
## [62,] 1.9979109
## [63,] 1.9979109
## [64,] 1.0134806
## [65,] 1.9777879
## [66,] 2.0001958
## [67,] 1.0067103
## [68,] 2.0057367
## [69,] 1.9894023
## [70,] 1.9953633
## [71,] 2.9905112
## [72,] 2.0056672
## [73,] 1.9980031dari hasil output tersebut, dapat dilihat bahwa hasil output sudah sesuai atau mendekati data aslinya.