# 1. Alat
if(!require(tidyverse)) install.packages("tidyverse")
if(!require(caret)) install.packages("caret")
library(tidyverse)
library(caret)
set.seed(2024)
n_samples <- 1000
#2. Membuat karakteristik 1000 mahasiswa UNNES
ipk <- runif(n_samples, 1.5, 4.0)
kehadiran <- runif(n_samples, 50, 100)
status_kerja <- rbinom(n_samples, 1, 0.3)
beban_sks <- sample(12:24, n_samples, replace = TRUE)
# Menentukan Logika Dropout (Nasib)
z <- 12 + (-4.5 * ipk) + (-0.05 * kehadiran) + (1.5 * status_kerja) + (-0.1 * beban_sks) + rnorm(n_samples, 0, 2)
prob_dropout <- 1 / (1 + exp(-z))
dropout <- ifelse(prob_dropout > 0.5, 1, 0)
df <- data.frame(
IPK = ipk,
Kehadiran = kehadiran,
Status_Kerja = as.factor(status_kerja),
Beban_SKS = beban_sks,
Dropout = factor(dropout, levels = c(0, 1))
)
#3. Chek up data
head(df, 20)
## IPK Kehadiran Status_Kerja Beban_SKS Dropout
## 1 3.592356 89.04524 0 15 0
## 2 2.302169 68.27175 1 19 0
## 3 3.200908 61.61661 0 15 0
## 4 3.245433 87.15642 0 24 0
## 5 2.642523 89.39329 0 21 0
## 6 3.253551 65.72174 0 18 0
## 7 2.539277 75.31652 0 17 0
## 8 2.258023 86.57884 0 21 0
## 9 3.691470 99.36211 0 20 0
## 10 1.797621 69.00770 1 12 1
## 11 3.754707 60.19130 0 12 0
## 12 3.882770 71.73747 0 18 0
## 13 3.258883 50.91535 0 21 0
## 14 2.789464 68.70674 1 14 0
## 15 2.623641 98.06522 0 14 0
## 16 3.605579 85.99870 0 13 0
## 17 1.775778 73.37827 0 16 1
## 18 3.658038 86.45705 0 24 0
## 19 1.827673 57.05499 0 15 0
## 20 2.238567 86.45970 1 22 0
str(df)
## 'data.frame': 1000 obs. of 5 variables:
## $ IPK : num 3.59 2.3 3.2 3.25 2.64 ...
## $ Kehadiran : num 89 68.3 61.6 87.2 89.4 ...
## $ Status_Kerja: Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 2 ...
## $ Beban_SKS : int 15 19 15 24 21 18 17 21 20 12 ...
## $ Dropout : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
colSums(is.na(df))
## IPK Kehadiran Status_Kerja Beban_SKS Dropout
## 0 0 0 0 0
summary(df)
## IPK Kehadiran Status_Kerja Beban_SKS Dropout
## Min. :1.501 Min. :50.09 0:708 Min. :12.00 0:916
## 1st Qu.:2.165 1st Qu.:62.48 1:292 1st Qu.:15.00 1: 84
## Median :2.786 Median :74.07 Median :18.00
## Mean :2.770 Mean :74.64 Mean :17.73
## 3rd Qu.:3.387 3rd Qu.:87.09 3rd Qu.:21.00
## Max. :3.996 Max. :99.95 Max. :24.00
print(df)
## IPK Kehadiran Status_Kerja Beban_SKS Dropout
## 1 3.592356 89.04524 0 15 0
## 2 2.302169 68.27175 1 19 0
## 3 3.200908 61.61661 0 15 0
## 4 3.245433 87.15642 0 24 0
## 5 2.642523 89.39329 0 21 0
## 6 3.253551 65.72174 0 18 0
## 7 2.539277 75.31652 0 17 0
## 8 2.258023 86.57884 0 21 0
## 9 3.691470 99.36211 0 20 0
## 10 1.797621 69.00770 1 12 1
## 11 3.754707 60.19130 0 12 0
## 12 3.882770 71.73747 0 18 0
## 13 3.258883 50.91535 0 21 0
## 14 2.789464 68.70674 1 14 0
## 15 2.623641 98.06522 0 14 0
## 16 3.605579 85.99870 0 13 0
## 17 1.775778 73.37827 0 16 1
## 18 3.658038 86.45705 0 24 0
## 19 1.827673 57.05499 0 15 0
## 20 2.238567 86.45970 1 22 0
## 21 1.618174 76.44114 0 21 1
## 22 1.779981 62.32212 0 24 0
## 23 3.201090 83.59680 1 15 0
## 24 3.624996 68.52047 0 13 0
## 25 3.493373 58.74031 0 22 0
## 26 2.230768 69.05124 1 22 0
## 27 3.038118 90.64961 0 22 0
## 28 3.538958 83.25786 1 24 0
## 29 1.501324 64.31084 0 20 1
## 30 1.552817 57.40879 0 20 0
## 31 2.602819 59.95583 0 14 0
## 32 2.225724 79.79812 0 16 0
## 33 1.940504 62.51029 1 16 0
## 34 3.933449 73.49042 0 19 0
## 35 3.187907 67.52887 0 12 0
## 36 3.202387 76.29984 0 23 0
## 37 1.635313 70.44698 0 21 1
## 38 3.915300 58.40177 0 24 0
## 39 1.555345 50.84655 0 19 1
## 40 3.118419 58.39554 1 15 0
## 41 3.682130 60.49806 0 19 0
## 42 3.776143 92.46678 0 17 0
## 43 3.059715 66.26567 0 12 0
## 44 2.479856 61.23313 0 18 0
## 45 3.628633 61.09776 0 14 0
## 46 2.620450 79.04027 1 13 0
## 47 2.539176 51.24337 0 12 0
## 48 2.198082 93.86594 0 23 0
## 49 3.135883 67.81951 0 15 0
## 50 3.126208 92.65228 0 23 0
## 51 1.653341 97.70028 0 15 0
## 52 3.352725 95.31308 0 16 0
## 53 2.632387 56.38301 0 21 0
## 54 2.723725 94.84537 0 15 0
## 55 3.383082 89.22192 1 23 0
## 56 3.289649 56.94810 0 16 0
## 57 1.549762 88.38091 1 24 0
## 58 1.979807 87.82809 0 12 1
## 59 2.641083 81.51253 0 19 0
## 60 2.195510 85.30625 0 13 0
## 61 1.687407 59.81721 0 23 0
## 62 2.566478 88.71262 0 24 0
## 63 2.431159 56.14806 0 12 0
## 64 3.098525 59.66003 0 15 0
## 65 2.266702 70.11008 0 16 0
## 66 3.537055 89.09851 0 23 0
## 67 3.714383 86.78279 0 13 0
## 68 3.297080 56.22759 0 19 0
## 69 2.272243 79.52924 0 23 0
## 70 2.218098 97.96846 0 16 0
## 71 3.128955 98.93559 0 12 0
## 72 1.996098 94.41954 0 12 0
## 73 3.080167 72.07006 1 18 0
## 74 2.610022 68.05799 0 16 0
## 75 2.280821 60.01582 0 20 0
## 76 3.271287 91.65967 1 21 0
## 77 2.210194 51.67018 1 21 0
## 78 1.589737 59.79243 0 18 1
## 79 3.164517 78.25290 0 24 0
## 80 2.050968 89.79785 0 13 0
## 81 1.978696 56.54662 0 19 0
## 82 2.113391 91.20210 0 12 0
## 83 2.745321 65.66761 1 18 0
## 84 1.544054 73.87969 0 24 0
## 85 3.224581 81.46274 0 19 0
## 86 2.707282 85.60541 1 20 0
## 87 3.939355 57.03343 0 14 0
## 88 2.269749 96.87815 1 24 0
## 89 1.789200 67.34085 0 19 0
## 90 3.646914 75.17727 0 19 0
## 91 2.879726 97.12168 1 13 0
## 92 3.772824 89.13251 0 14 0
## 93 3.072362 87.57323 1 19 0
## 94 1.598744 83.89972 0 12 0
## 95 3.369959 62.38086 0 20 0
## 96 1.958150 58.41140 0 17 0
## 97 3.346466 67.62288 0 15 0
## 98 2.766974 53.52375 0 23 0
## 99 3.934594 75.67204 0 14 0
## 100 2.926379 73.34923 0 12 0
## 101 2.224012 88.21988 1 24 0
## 102 3.176523 95.95823 0 21 0
## 103 2.054810 82.87042 0 19 0
## 104 2.825970 70.96278 0 24 0
## 105 2.147287 83.43034 0 22 0
## 106 3.561302 50.55978 1 23 0
## 107 1.880213 70.89962 0 15 0
## 108 1.660714 82.46145 0 15 0
## 109 2.119920 70.77386 1 17 0
## 110 3.507486 67.49681 0 23 0
## 111 1.689604 79.76090 0 16 1
## 112 3.049122 54.99534 0 21 0
## 113 3.536846 52.23233 1 20 0
## 114 1.720164 54.71969 0 24 0
## 115 2.513823 96.83736 0 17 0
## 116 3.758348 66.08336 0 22 0
## 117 3.603606 69.14442 1 18 0
## 118 1.920500 71.00780 0 16 0
## 119 2.717502 84.87677 1 20 0
## 120 2.151778 73.25480 0 24 0
## 121 3.861035 95.51382 0 18 0
## 122 1.658835 79.43812 0 19 0
## 123 3.512900 98.76150 0 16 0
## 124 1.803739 63.66861 1 22 1
## 125 3.262098 63.16848 0 14 0
## 126 1.539025 90.93181 1 23 0
## 127 2.923892 72.95663 0 21 0
## 128 3.530626 72.75864 1 22 0
## 129 3.875269 52.37484 0 23 0
## 130 3.988280 54.94235 0 17 0
## 131 2.621808 80.08772 1 20 1
## 132 1.535649 97.64527 0 14 0
## 133 1.557485 73.34525 0 23 0
## 134 2.617943 52.64285 1 18 0
## 135 3.406460 84.98888 0 15 0
## 136 3.198882 93.39203 0 24 0
## 137 3.221335 78.42223 0 17 0
## 138 1.730959 55.89038 1 13 1
## 139 1.542633 51.59373 1 24 1
## 140 3.001363 88.09535 0 19 0
## 141 3.677599 57.94922 0 18 0
## 142 2.375660 80.72580 0 14 0
## 143 1.783706 58.50677 0 14 1
## 144 2.660071 96.43828 0 16 0
## 145 3.040834 89.56324 1 22 0
## 146 3.986126 85.82420 1 22 0
## 147 2.824999 53.51724 0 21 0
## 148 2.151905 87.44585 0 16 0
## 149 2.944394 89.75531 1 21 0
## 150 2.447619 95.83036 1 15 0
## 151 2.443080 96.86930 1 16 0
## 152 2.606936 60.74072 0 12 0
## 153 3.805959 64.25424 0 21 0
## 154 1.953529 98.57511 1 22 0
## 155 3.382113 71.61284 0 12 0
## 156 2.593044 87.41330 1 18 0
## 157 1.873589 93.66194 1 18 0
## 158 1.533342 67.56186 0 23 0
## 159 2.765641 53.75183 0 18 0
## 160 1.918281 76.46910 0 13 0
## 161 1.767238 55.54718 1 15 1
## 162 3.418554 70.53756 1 18 0
## 163 3.598417 89.86602 0 22 0
## 164 1.919966 72.41470 1 14 0
## 165 3.515491 51.58292 1 24 0
## 166 3.496310 98.21070 0 23 0
## 167 1.599066 59.25881 0 23 0
## 168 3.054932 82.50389 0 23 0
## 169 2.897869 89.27247 0 12 0
## 170 2.470274 74.12315 0 23 0
## 171 3.666291 78.44661 1 16 0
## 172 3.283502 89.61443 0 24 0
## 173 2.568740 77.73737 0 19 0
## 174 3.535770 72.44937 0 16 0
## 175 2.498223 58.27818 0 12 0
## 176 3.092577 83.84433 0 12 0
## 177 2.502329 98.24624 1 24 0
## 178 1.521966 64.68251 1 16 1
## 179 3.482319 96.58231 0 22 0
## 180 2.948352 83.56716 0 15 0
## 181 1.774583 75.23586 0 19 1
## 182 3.820887 87.32381 0 17 0
## 183 2.252758 55.43429 1 13 0
## 184 3.361116 62.19611 0 12 0
## 185 2.101236 54.34051 0 16 0
## 186 3.985427 66.35590 0 13 0
## 187 3.385241 84.27441 0 17 0
## 188 3.358290 79.38560 1 17 0
## 189 3.270036 69.55341 0 14 0
## 190 1.814434 50.82196 0 17 0
## 191 1.548553 68.81799 0 15 1
## 192 1.567162 66.53418 0 14 1
## 193 1.804610 68.41827 0 17 0
## 194 1.702358 68.30747 1 20 1
## 195 1.797873 96.57787 0 19 0
## 196 1.608931 53.00586 0 16 1
## 197 2.720324 84.80216 0 13 0
## 198 2.701457 74.23895 1 13 0
## 199 3.688103 68.96162 0 12 0
## 200 3.789167 70.40666 0 17 0
## 201 3.966002 50.24509 0 14 0
## 202 1.575870 52.67772 0 18 0
## 203 3.175199 95.06276 0 12 0
## 204 1.799309 84.98078 0 19 0
## 205 3.628911 86.34992 0 17 0
## 206 1.592855 91.67627 1 15 0
## 207 2.875015 77.58982 0 21 0
## 208 2.516540 83.92847 0 15 0
## 209 1.757227 79.27933 0 13 0
## 210 3.595034 97.32526 1 13 0
## 211 3.114829 60.06779 1 17 0
## 212 3.731100 94.22055 1 17 0
## 213 2.552233 72.76631 1 14 0
## 214 2.653440 73.16911 0 14 0
## 215 3.689776 50.90069 0 18 0
## 216 2.627504 67.92011 0 24 0
## 217 2.217896 73.27437 0 23 0
## 218 3.517574 74.47475 0 12 0
## 219 1.819028 65.51987 0 18 0
## 220 1.862697 96.35159 1 20 0
## 221 1.744854 55.36158 0 20 0
## 222 2.892487 54.01746 0 17 0
## 223 3.674164 94.20073 0 22 0
## 224 2.741634 85.55898 0 19 0
## 225 2.104128 62.85724 0 13 0
## 226 3.660063 70.38758 0 24 0
## 227 2.721205 97.22641 1 14 0
## 228 3.898856 78.42607 1 17 0
## 229 2.164984 84.20183 0 18 0
## 230 2.699016 92.69820 0 15 0
## 231 1.583994 59.67281 0 21 1
## 232 1.949513 55.38357 0 22 0
## 233 3.928991 90.56712 0 15 0
## 234 2.215156 56.63974 0 19 1
## 235 3.254790 99.42081 1 24 0
## 236 3.827139 82.48050 1 20 0
## 237 2.488963 75.99674 0 24 0
## 238 1.854009 63.21653 0 23 0
## 239 3.544589 77.83867 0 15 0
## 240 3.841764 72.67706 0 20 0
## 241 2.488503 89.63378 0 12 0
## 242 1.599191 56.62875 0 13 0
## 243 2.428103 57.07454 0 15 0
## 244 2.519541 70.06489 1 12 0
## 245 1.740430 68.33379 0 16 0
## 246 1.857396 74.73673 0 21 0
## 247 2.554501 61.49933 0 17 0
## 248 3.556914 68.57752 1 22 0
## 249 2.096014 71.05928 1 20 0
## 250 3.485321 62.40456 0 13 0
## 251 2.778761 90.54055 1 20 0
## 252 1.855934 77.49483 0 18 0
## 253 1.853811 69.47011 1 18 1
## 254 2.771489 52.98726 1 18 0
## 255 1.843656 56.21654 0 14 1
## 256 2.534970 85.22337 0 13 0
## 257 2.211687 71.84082 1 15 0
## 258 2.244114 92.83927 1 13 1
## 259 3.245786 71.82903 0 12 0
## 260 1.508529 60.89149 1 24 0
## 261 1.914949 54.20582 0 18 0
## 262 1.655192 75.89089 1 19 0
## 263 2.882771 57.88762 0 15 0
## 264 3.303532 67.71511 0 14 0
## 265 2.144844 52.29554 0 13 0
## 266 2.956009 85.12316 0 24 0
## 267 2.786625 68.50676 1 14 0
## 268 1.986312 56.00012 1 24 0
## 269 3.886408 60.49936 0 14 0
## 270 3.970229 95.59944 1 16 0
## 271 1.999035 94.55265 1 14 0
## 272 1.703069 87.28462 0 16 1
## 273 1.787300 56.71314 0 12 0
## 274 1.642674 86.17165 1 17 1
## 275 3.253193 87.86914 0 15 0
## 276 2.662465 55.99155 0 20 0
## 277 3.069606 78.64106 1 20 0
## 278 1.797037 88.08344 1 22 0
## 279 2.455308 96.01407 0 18 0
## 280 3.970278 77.28036 0 17 0
## 281 3.273590 60.83841 0 16 0
## 282 2.969478 63.49439 1 16 0
## 283 1.765724 65.37876 0 21 0
## 284 2.911523 65.52191 0 24 0
## 285 2.032211 87.90148 0 13 0
## 286 2.411927 90.77569 0 13 0
## 287 3.898409 88.83337 1 18 0
## 288 3.966403 96.48493 1 15 0
## 289 3.515550 78.81620 0 14 0
## 290 1.595969 93.64980 1 19 0
## 291 3.419182 54.83239 0 23 0
## 292 3.645663 87.64617 0 22 0
## 293 3.896611 58.68941 1 19 0
## 294 3.340588 70.62641 0 22 0
## 295 2.887572 98.88361 0 23 0
## 296 2.457747 96.42538 0 13 0
## 297 3.521029 65.56486 0 16 0
## 298 3.346216 79.86721 0 16 0
## 299 3.632995 51.02864 0 19 0
## 300 1.972167 64.36488 0 20 0
## 301 1.819194 91.87393 0 24 0
## 302 3.077178 67.44199 0 20 0
## 303 2.706472 60.09095 1 12 0
## 304 3.255765 95.32203 0 16 0
## 305 3.979141 67.04008 0 14 0
## 306 1.729626 75.81686 0 24 0
## 307 3.733328 69.55133 1 13 0
## 308 3.003025 72.48980 0 15 0
## 309 2.053694 85.50511 0 14 0
## 310 3.593572 68.45182 0 21 0
## 311 1.503953 93.07447 0 15 0
## 312 2.182637 88.65053 0 16 0
## 313 3.072531 62.86694 0 17 0
## 314 2.382189 84.05403 1 20 0
## 315 2.449733 68.81733 0 12 0
## 316 1.654830 80.30373 1 16 1
## 317 2.095167 71.29258 0 20 0
## 318 3.981626 96.66282 0 18 0
## 319 3.037379 75.53593 0 19 0
## 320 1.619879 77.46943 0 21 0
## 321 3.897444 86.11386 0 12 0
## 322 3.114529 62.29666 1 17 0
## 323 2.747435 64.16383 1 15 0
## 324 3.788159 50.98543 0 18 0
## 325 2.745548 77.95602 0 19 0
## 326 2.487630 73.51150 0 15 0
## 327 3.852138 90.14186 0 13 0
## 328 3.905441 74.07070 1 20 0
## 329 2.379462 52.30967 0 16 0
## 330 1.977447 56.26623 0 21 1
## 331 2.158510 84.91240 0 13 0
## 332 3.944471 82.28553 0 12 0
## 333 2.886230 61.80957 0 22 0
## 334 3.305203 71.73428 1 21 0
## 335 1.605589 90.17070 0 19 0
## 336 2.844092 71.02604 0 23 0
## 337 3.723293 73.75748 0 20 0
## 338 3.757602 58.31747 0 22 0
## 339 3.964889 67.18853 1 13 0
## 340 3.777861 50.08641 0 14 0
## 341 2.390604 84.56095 1 14 0
## 342 3.007651 54.39563 0 20 0
## 343 3.556884 72.31642 0 22 0
## 344 3.802854 69.79872 0 14 0
## 345 2.280629 69.77948 0 20 0
## 346 2.488201 60.09264 0 22 0
## 347 2.955527 61.12411 1 22 1
## 348 2.454596 51.75876 0 17 0
## 349 3.136830 66.72763 1 23 0
## 350 2.835876 88.05827 0 12 0
## 351 3.690418 59.21818 1 23 0
## 352 2.822424 83.49826 0 15 0
## 353 3.732265 74.30696 0 18 0
## 354 3.437506 66.78789 0 15 0
## 355 2.914715 73.89254 1 12 0
## 356 2.057865 96.23418 0 15 0
## 357 3.406907 86.06642 1 18 0
## 358 3.561264 57.21962 0 22 0
## 359 2.381413 54.03172 0 12 0
## 360 3.253468 69.49551 0 14 0
## 361 3.391558 72.40981 1 13 0
## 362 1.672884 81.97683 0 15 0
## 363 3.001213 84.65970 1 14 0
## 364 3.202226 98.52143 0 14 0
## 365 3.371461 80.87066 0 21 0
## 366 3.540977 96.58064 1 12 0
## 367 2.132679 99.91412 0 22 0
## 368 2.948264 62.22536 0 22 0
## 369 2.384343 85.75515 1 23 0
## 370 3.221409 69.63551 0 14 0
## 371 3.435120 70.38612 1 22 0
## 372 3.780927 51.18693 0 24 0
## 373 3.601274 87.07421 0 16 0
## 374 1.564136 74.79460 1 17 1
## 375 2.398275 75.78574 1 14 1
## 376 3.096775 92.49849 0 16 0
## 377 2.128524 64.27907 0 17 0
## 378 1.629004 56.42994 0 12 1
## 379 3.209626 56.05437 0 15 0
## 380 1.872778 96.01879 1 22 0
## 381 2.195094 68.38009 1 15 1
## 382 2.273668 62.66445 1 23 0
## 383 1.869886 70.04168 0 23 0
## 384 3.234049 95.82924 0 21 0
## 385 3.276674 89.79019 1 13 0
## 386 2.553001 53.12197 1 16 0
## 387 3.641700 91.95140 1 22 0
## 388 3.080899 51.55470 0 19 0
## 389 3.752665 89.06338 1 23 0
## 390 2.445736 74.48840 1 21 1
## 391 3.300533 60.50685 0 15 0
## 392 1.938649 60.16035 0 12 0
## 393 3.742271 67.20138 0 22 0
## 394 1.824080 78.10512 1 15 1
## 395 3.218430 97.70845 0 24 0
## 396 3.667133 89.86660 1 16 0
## 397 3.972944 97.99102 0 19 0
## 398 3.737115 52.45396 1 15 0
## 399 1.587259 52.85387 0 22 1
## 400 3.402541 87.25725 0 21 0
## 401 2.752184 58.41392 0 21 0
## 402 3.714994 63.90537 0 12 0
## 403 3.206841 93.93095 1 14 0
## 404 3.972655 81.28445 0 17 0
## 405 2.045901 71.62598 0 16 0
## 406 1.638884 89.06620 0 24 0
## 407 2.953404 58.16609 1 13 0
## 408 2.915278 77.23552 0 13 0
## 409 3.616846 65.02963 0 20 0
## 410 3.393199 84.65681 0 17 0
## 411 1.890573 57.42571 0 23 0
## 412 2.518801 73.12757 0 23 0
## 413 2.065114 74.45654 0 14 0
## 414 2.688515 70.68380 1 17 0
## 415 2.893314 64.34313 0 23 0
## 416 3.511195 63.16879 0 22 0
## 417 3.526804 85.28986 1 13 0
## 418 2.502321 80.64737 0 14 0
## 419 3.167243 86.22729 0 17 0
## 420 1.678112 67.02982 0 15 1
## 421 2.111999 65.38069 0 19 0
## 422 3.981891 76.20098 1 22 0
## 423 3.280089 66.18006 1 16 0
## 424 3.549051 80.67030 1 12 0
## 425 2.216121 77.39394 0 16 0
## 426 3.965671 69.90692 1 13 0
## 427 1.507626 81.89122 0 18 0
## 428 2.290957 58.04505 1 18 1
## 429 3.349462 97.39090 1 18 0
## 430 3.962115 68.02006 0 21 0
## 431 3.137842 67.88900 0 20 0
## 432 2.107036 77.53663 0 20 0
## 433 3.394086 71.20940 1 18 0
## 434 3.064091 56.10708 0 16 0
## 435 3.248310 98.20382 0 19 0
## 436 3.791699 55.19133 0 21 0
## 437 2.188532 85.23028 0 12 0
## 438 3.787094 89.22611 1 20 0
## 439 3.649610 79.43361 0 18 0
## 440 2.857933 78.86771 0 14 0
## 441 2.142946 90.25511 1 23 0
## 442 2.688512 54.94106 1 16 0
## 443 3.831513 56.44561 0 15 0
## 444 2.113701 76.79168 0 19 0
## 445 2.883640 63.81063 0 17 0
## 446 3.701184 74.90010 0 12 0
## 447 2.676744 74.44154 0 17 0
## 448 2.675468 51.78986 0 18 0
## 449 2.776202 59.04919 0 24 0
## 450 1.836550 81.53997 0 22 0
## 451 2.986429 91.68402 1 12 0
## 452 3.412043 88.54407 0 21 0
## 453 2.665693 89.52537 0 21 0
## 454 2.190597 65.82479 0 13 0
## 455 3.192226 92.96295 0 15 0
## 456 2.551158 92.97815 0 12 0
## 457 2.497544 90.48341 0 20 0
## 458 3.267467 54.96459 1 21 0
## 459 3.327592 72.38371 0 18 0
## 460 3.504989 52.70498 0 19 0
## 461 3.777572 57.67769 1 21 0
## 462 2.170974 99.70220 0 22 0
## 463 3.376063 92.87008 0 18 0
## 464 2.432290 84.38967 0 12 0
## 465 2.532617 54.51447 0 13 0
## 466 2.356708 85.58269 0 23 0
## 467 2.386573 77.19316 0 14 0
## 468 3.064341 56.50833 1 17 0
## 469 2.718100 52.57452 0 18 0
## 470 1.548652 56.26596 0 22 1
## 471 2.836939 67.93623 0 17 0
## 472 1.664236 90.55738 0 17 0
## 473 2.686655 93.00948 0 23 0
## 474 2.136611 76.47462 1 14 0
## 475 2.257623 83.07011 0 19 0
## 476 2.087836 75.65230 0 22 0
## 477 2.827792 97.74732 0 20 0
## 478 3.304616 75.23326 1 24 0
## 479 1.949132 55.66118 0 12 0
## 480 3.576426 59.91385 0 20 0
## 481 2.965186 72.33606 1 12 0
## 482 3.454255 85.52749 0 17 0
## 483 3.656288 54.77397 0 22 0
## 484 2.437445 76.38601 0 15 0
## 485 3.784678 90.02196 1 19 0
## 486 1.814342 70.99796 0 19 0
## 487 3.679103 63.05483 1 14 0
## 488 2.707142 90.96236 1 14 0
## 489 1.953381 55.20005 0 19 1
## 490 2.581973 57.84434 0 16 0
## 491 3.926830 76.62951 0 18 0
## 492 3.788833 86.51511 0 17 0
## 493 2.215361 71.16566 0 15 0
## 494 3.484960 77.23399 0 20 0
## 495 3.004332 68.40940 0 17 0
## 496 3.653575 54.04804 0 16 0
## 497 1.827352 92.55212 0 16 0
## 498 3.639017 94.49304 0 12 0
## 499 2.902118 67.03695 0 12 0
## 500 3.508158 66.63004 0 14 0
## 501 3.895269 70.69341 0 22 0
## 502 2.587467 57.83559 0 13 0
## 503 2.921087 86.88316 1 12 0
## 504 2.256069 74.52100 0 24 0
## 505 3.618864 98.65385 0 18 0
## 506 1.601953 92.70395 1 17 0
## 507 2.476536 74.36804 0 22 0
## 508 2.648176 51.73417 1 12 1
## 509 3.018031 93.27610 0 18 0
## 510 3.637911 82.85983 1 15 0
## 511 1.644316 67.20329 0 24 0
## 512 1.723756 50.15621 0 18 0
## 513 2.896987 73.04679 1 13 0
## 514 3.600229 57.07390 0 16 0
## 515 2.590018 78.21345 0 24 0
## 516 1.908374 97.12857 0 19 0
## 517 3.274165 55.79460 0 17 0
## 518 2.722327 89.96769 0 14 0
## 519 1.812230 99.54724 0 16 0
## 520 3.889277 80.40217 0 17 0
## 521 2.514146 58.01060 0 17 0
## 522 3.530214 64.71492 1 21 0
## 523 2.413993 71.95830 0 14 0
## 524 3.891937 89.42892 0 16 0
## 525 3.254396 51.70543 0 15 0
## 526 3.269106 75.52754 0 22 0
## 527 2.061930 70.91790 0 18 0
## 528 2.939791 96.11710 1 21 0
## 529 1.513826 51.30600 0 19 1
## 530 2.135930 83.05183 1 14 0
## 531 3.321354 60.67222 1 23 0
## 532 3.641672 91.39399 0 16 0
## 533 2.311622 67.89813 0 15 0
## 534 3.017715 81.45013 1 15 0
## 535 3.417319 80.29305 0 20 0
## 536 1.596514 90.11787 0 18 0
## 537 3.263400 50.08854 0 23 0
## 538 2.263926 63.03140 0 13 0
## 539 3.556298 69.66249 1 19 0
## 540 2.837753 60.49041 0 12 0
## 541 3.536416 65.79617 0 16 0
## 542 2.812938 91.12958 1 22 0
## 543 2.571805 88.54282 1 20 0
## 544 3.146328 89.29255 1 13 0
## 545 1.724460 67.04883 1 24 1
## 546 3.091488 81.18531 0 16 0
## 547 2.018917 74.57870 1 20 0
## 548 1.782891 73.42668 0 22 0
## 549 3.290461 68.05666 0 23 0
## 550 3.084266 88.19835 0 18 0
## 551 2.780353 90.48829 1 24 0
## 552 2.840752 87.65442 0 20 0
## 553 2.168792 55.83029 0 18 0
## 554 3.763266 84.40213 0 18 0
## 555 1.640293 56.89858 0 12 0
## 556 2.868112 70.41095 1 15 0
## 557 1.936952 84.75383 0 12 0
## 558 3.449907 87.39690 0 16 0
## 559 2.631880 87.99915 0 16 0
## 560 3.595037 75.02331 0 18 0
## 561 3.367420 77.94752 0 20 0
## 562 2.827749 82.68122 1 21 0
## 563 2.986863 95.85316 0 23 0
## 564 3.083479 99.76048 0 17 0
## 565 3.784214 71.66662 1 22 0
## 566 1.698612 97.44103 1 21 0
## 567 2.971103 64.32040 0 14 0
## 568 1.898665 98.00714 0 16 0
## 569 3.323008 80.26505 1 14 0
## 570 3.472597 87.58202 0 22 0
## 571 2.369002 58.69231 1 14 0
## 572 2.960129 86.02578 0 15 0
## 573 2.044974 52.45893 1 16 0
## 574 1.530926 90.63085 0 15 0
## 575 2.813726 74.93180 0 24 0
## 576 2.382849 95.54965 0 14 0
## 577 3.659282 85.46172 0 21 0
## 578 1.939744 97.83443 1 14 0
## 579 3.985871 69.43912 1 17 0
## 580 3.493234 82.61925 0 17 0
## 581 2.112843 86.39716 0 23 0
## 582 1.568615 92.67155 0 17 1
## 583 3.473022 61.32120 0 20 0
## 584 2.481322 73.05213 0 17 0
## 585 3.878300 97.04696 1 14 0
## 586 3.245611 82.97381 1 24 0
## 587 2.345808 84.03305 0 17 0
## 588 2.789159 60.31652 0 13 0
## 589 3.080841 58.82858 0 23 0
## 590 3.077156 93.23254 0 15 0
## 591 3.399086 99.94842 1 19 0
## 592 1.510475 56.96315 0 15 1
## 593 3.763318 92.39226 1 24 0
## 594 3.648049 68.41831 1 17 0
## 595 1.773821 93.67896 0 12 0
## 596 3.784024 75.22181 0 18 0
## 597 1.753242 59.87008 0 23 0
## 598 3.287580 50.19304 0 16 0
## 599 2.885749 94.05922 0 17 0
## 600 3.125755 65.12755 1 19 0
## 601 3.403802 71.37962 1 16 0
## 602 3.477788 98.66243 0 14 0
## 603 2.158926 71.07805 1 13 0
## 604 3.145077 87.38346 0 24 0
## 605 2.438191 93.25610 0 16 0
## 606 3.299840 94.98266 0 21 0
## 607 3.642818 67.11817 1 15 0
## 608 2.175406 58.10241 1 24 0
## 609 3.860841 55.10459 0 20 0
## 610 3.550555 95.57549 0 17 0
## 611 3.183534 93.99843 1 14 0
## 612 3.306745 78.19022 1 13 0
## 613 2.642341 56.95172 1 15 0
## 614 2.500750 77.87307 0 16 0
## 615 3.407105 64.75181 1 18 0
## 616 3.946077 80.11661 0 16 0
## 617 2.928397 79.94745 1 17 0
## 618 2.455232 53.57817 0 20 0
## 619 3.765865 96.22811 0 14 0
## 620 3.277013 81.92685 1 18 0
## 621 3.129001 63.93510 0 16 0
## 622 2.652054 65.41247 1 19 0
## 623 1.727277 91.89543 0 18 0
## 624 1.696690 99.54659 0 12 0
## 625 1.634361 69.86412 1 16 1
## 626 3.640189 96.54344 1 19 0
## 627 1.980507 56.70822 0 15 1
## 628 3.421048 96.47061 1 12 0
## 629 3.995770 77.09512 0 20 0
## 630 2.762296 57.74640 0 24 0
## 631 2.341664 72.43452 0 16 1
## 632 3.861948 74.53197 1 22 0
## 633 2.710286 84.60816 0 20 0
## 634 2.243640 76.18519 0 20 0
## 635 2.788017 57.33439 1 23 0
## 636 3.628058 82.71266 0 12 0
## 637 3.628560 62.16253 0 17 0
## 638 2.741673 73.44464 0 19 0
## 639 2.151637 72.08239 0 22 1
## 640 2.368525 90.17416 0 17 0
## 641 1.856638 58.23089 0 17 1
## 642 1.760035 68.02594 0 13 1
## 643 2.948004 65.00796 1 22 0
## 644 2.901736 75.19612 0 22 0
## 645 3.127630 73.20635 0 12 0
## 646 1.734662 51.54059 1 21 1
## 647 2.121065 86.84220 0 20 0
## 648 3.366789 58.04223 0 16 0
## 649 3.990150 62.88879 0 19 0
## 650 2.536072 92.84469 0 24 0
## 651 2.587901 94.34827 0 19 0
## 652 1.577129 64.83332 1 13 1
## 653 1.882079 69.25215 1 14 1
## 654 2.945879 83.80278 0 15 0
## 655 3.384049 70.74174 0 17 0
## 656 3.613117 88.33547 0 15 0
## 657 1.563552 56.35333 0 15 1
## 658 3.227755 77.19353 0 22 0
## 659 2.394238 57.61638 1 18 1
## 660 3.391248 74.20415 0 20 0
## 661 2.833383 79.46448 0 12 0
## 662 3.039324 65.94856 0 18 0
## 663 2.164987 54.62375 0 21 0
## 664 2.824257 54.76517 1 19 0
## 665 3.774052 71.75742 0 16 0
## 666 1.624835 52.55527 0 15 0
## 667 2.290601 90.25451 0 19 0
## 668 2.762791 67.73546 0 24 0
## 669 3.677557 59.50467 0 19 0
## 670 1.854326 78.97333 0 16 1
## 671 1.662208 81.94406 0 22 0
## 672 3.159885 52.33538 0 15 0
## 673 3.786759 64.83693 0 13 0
## 674 2.875566 85.84689 1 17 0
## 675 1.547465 54.79327 0 15 1
## 676 2.128007 57.56982 0 13 0
## 677 2.758632 94.76666 0 19 0
## 678 2.596219 64.30981 0 22 0
## 679 3.177745 59.06581 1 12 0
## 680 3.599555 91.83290 1 23 0
## 681 2.141813 52.69400 1 21 1
## 682 3.012690 77.83428 0 23 0
## 683 1.639552 99.21706 1 22 0
## 684 2.618602 81.55734 1 18 0
## 685 3.070830 99.28554 0 22 0
## 686 2.692478 68.84982 0 15 0
## 687 2.785118 51.77196 1 20 0
## 688 2.152972 57.50103 0 23 0
## 689 3.414094 91.60280 1 14 0
## 690 1.661187 55.15959 0 18 1
## 691 3.837174 97.38004 0 14 0
## 692 2.286309 66.73764 0 12 0
## 693 1.812374 93.71361 0 16 0
## 694 2.856484 50.83737 0 21 0
## 695 3.209978 84.28282 0 14 0
## 696 1.976548 81.45302 1 17 0
## 697 1.815003 99.56471 0 21 0
## 698 3.843216 99.08323 1 19 0
## 699 3.756530 59.14144 0 14 0
## 700 1.576484 97.55533 0 19 0
## 701 2.353228 81.47442 0 19 0
## 702 3.009403 99.63820 0 19 0
## 703 3.467818 75.60612 1 16 0
## 704 1.816404 66.87545 0 13 0
## 705 3.412841 97.54987 0 12 0
## 706 1.723455 61.42827 0 20 0
## 707 2.060585 67.97304 1 12 0
## 708 2.334531 79.02739 1 16 0
## 709 1.551699 52.65625 0 14 0
## 710 2.224611 55.16250 1 14 0
## 711 2.263540 83.47893 1 24 0
## 712 3.649569 59.24414 1 16 0
## 713 3.417603 96.85780 1 22 0
## 714 2.557715 64.09871 1 23 0
## 715 3.481350 60.63754 1 23 0
## 716 1.757499 73.17622 0 20 0
## 717 3.699844 67.94539 0 24 0
## 718 2.050067 66.18694 0 18 0
## 719 1.837764 65.67390 0 22 0
## 720 3.268385 69.40216 1 16 0
## 721 2.507733 56.41787 0 14 0
## 722 2.041197 86.35570 0 18 0
## 723 3.272083 53.98761 0 14 0
## 724 3.961503 69.37452 1 14 0
## 725 2.067068 63.39020 1 20 0
## 726 1.864929 50.21425 0 19 0
## 727 2.142910 79.48918 0 12 0
## 728 3.415305 79.40619 1 13 0
## 729 3.176723 82.85725 0 13 0
## 730 3.147060 63.00186 0 13 0
## 731 2.245901 68.24687 0 14 0
## 732 1.917433 65.15832 0 12 1
## 733 2.920052 93.77548 0 20 0
## 734 3.632098 97.15272 1 24 0
## 735 2.865945 72.78429 0 17 0
## 736 1.664277 94.83561 0 12 0
## 737 3.306461 61.47632 0 24 0
## 738 2.739890 52.63759 0 18 0
## 739 2.096469 75.73895 0 18 0
## 740 1.876789 64.71945 0 15 0
## 741 3.898573 93.17431 0 14 0
## 742 1.813452 66.95683 1 18 1
## 743 2.338796 88.23177 0 14 0
## 744 2.743419 74.84610 0 13 0
## 745 2.349980 71.03543 1 20 0
## 746 3.469197 50.52815 1 13 0
## 747 2.601700 59.93597 0 17 0
## 748 2.579983 83.67257 0 18 0
## 749 2.798899 97.35568 0 22 0
## 750 3.132585 55.73539 1 24 0
## 751 2.620783 99.06730 0 18 0
## 752 2.952043 98.26658 0 19 0
## 753 2.543195 63.46994 0 24 0
## 754 1.682258 99.01758 0 14 0
## 755 2.304298 79.82977 0 18 0
## 756 2.730672 54.56282 0 13 0
## 757 3.974939 75.17686 0 14 0
## 758 1.681366 82.75190 1 19 1
## 759 1.999435 63.35286 0 15 0
## 760 3.364612 76.12781 1 18 0
## 761 2.922594 89.61326 0 17 0
## 762 3.439669 70.78211 0 14 0
## 763 2.508376 51.33362 1 23 0
## 764 2.835925 82.26962 1 12 0
## 765 3.010715 67.51426 0 17 0
## 766 3.977412 57.68965 1 20 0
## 767 2.780872 79.48537 0 22 0
## 768 2.682100 51.97339 0 13 0
## 769 3.841776 87.18732 0 18 0
## 770 2.604056 76.27713 0 13 0
## 771 3.129732 85.92412 0 19 0
## 772 1.724073 81.64875 0 16 0
## 773 1.632903 83.89956 0 20 0
## 774 1.552747 61.53400 1 17 1
## 775 3.485490 96.17689 0 18 0
## 776 2.692402 67.07557 1 22 0
## 777 2.618365 86.98712 0 18 0
## 778 2.908557 77.02301 1 20 0
## 779 3.715292 91.19898 0 16 0
## 780 3.925461 97.62359 0 23 0
## 781 2.764299 67.18617 0 15 0
## 782 3.888400 99.92048 0 14 0
## 783 3.428500 96.31568 1 15 0
## 784 1.774792 88.27057 1 22 0
## 785 3.535656 64.76451 1 12 0
## 786 2.432557 96.20632 0 18 0
## 787 1.815856 57.51959 0 15 0
## 788 3.106802 61.96746 1 19 0
## 789 3.804898 91.99765 0 19 0
## 790 1.733681 64.22199 1 23 1
## 791 3.273759 51.45924 0 16 0
## 792 2.989186 67.65289 0 22 0
## 793 2.348313 77.77500 0 15 0
## 794 2.384472 94.67662 0 16 0
## 795 2.591319 99.45076 0 14 0
## 796 3.037632 72.96220 0 15 0
## 797 1.720815 94.90838 0 19 0
## 798 2.056040 52.35468 1 16 0
## 799 2.568421 75.69410 1 20 0
## 800 2.008649 72.18382 0 16 0
## 801 3.943183 84.07843 0 23 0
## 802 1.942227 93.91156 0 19 0
## 803 3.461925 63.83909 1 12 0
## 804 2.051759 68.73179 0 13 0
## 805 2.938269 52.83802 0 21 0
## 806 2.882741 64.16568 1 12 0
## 807 2.008579 79.19628 0 22 0
## 808 2.595192 52.49110 1 15 0
## 809 3.577393 91.95144 1 15 0
## 810 1.952620 82.31580 0 16 0
## 811 1.549226 75.00202 1 18 0
## 812 2.243441 80.10963 0 15 0
## 813 2.993101 62.69620 0 16 0
## 814 2.732483 66.43314 1 16 0
## 815 2.645696 70.32674 0 12 0
## 816 2.783197 67.51388 0 14 0
## 817 3.066002 75.74743 0 23 0
## 818 2.778898 94.55364 1 13 0
## 819 3.398929 98.60484 0 24 0
## 820 2.330406 62.13030 0 24 0
## 821 3.797322 73.33277 1 19 0
## 822 2.752068 82.47558 0 15 0
## 823 2.826363 70.25380 0 19 0
## 824 2.091080 80.00846 0 13 0
## 825 3.484901 55.78180 0 20 0
## 826 2.174098 71.63197 1 18 0
## 827 3.420222 86.74370 1 24 0
## 828 2.013193 64.01996 0 12 1
## 829 3.425516 73.25361 0 17 0
## 830 1.954600 82.34797 0 19 0
## 831 2.826266 61.75497 0 23 0
## 832 2.915519 73.25955 0 19 0
## 833 1.850087 86.61459 0 20 0
## 834 2.200985 85.79979 0 16 0
## 835 2.026434 79.18826 0 16 0
## 836 2.054258 63.46384 0 24 0
## 837 1.648565 75.31522 0 20 1
## 838 2.715355 57.70706 0 12 0
## 839 3.323103 82.75940 0 13 0
## 840 3.001367 94.58698 1 14 0
## 841 1.607389 78.73596 0 17 0
## 842 2.448023 93.90595 0 23 0
## 843 3.855982 86.28888 1 24 0
## 844 3.571908 72.03191 1 21 0
## 845 2.648566 92.12542 0 20 0
## 846 3.028278 61.01765 0 15 0
## 847 2.331652 76.32490 0 22 0
## 848 2.468214 72.48130 0 24 0
## 849 3.507810 65.15162 0 16 0
## 850 3.191542 54.16499 0 15 0
## 851 2.462121 67.54660 1 23 0
## 852 1.800000 99.91109 0 16 0
## 853 3.819502 52.45991 1 12 0
## 854 2.409789 51.62046 0 18 0
## 855 1.707778 89.65427 0 22 0
## 856 3.055686 99.78611 1 17 0
## 857 3.474286 91.66931 0 15 0
## 858 2.864357 54.39387 0 21 0
## 859 3.803894 96.83209 0 24 0
## 860 3.660921 70.07002 0 13 0
## 861 3.955194 62.83342 0 24 0
## 862 3.681437 51.69470 1 21 0
## 863 3.814384 93.32557 0 24 0
## 864 2.411413 72.24897 0 21 0
## 865 3.591634 55.11285 0 17 0
## 866 3.075224 94.76909 0 18 0
## 867 1.949435 90.50584 0 20 1
## 868 2.507706 80.97445 0 13 0
## 869 3.598309 99.26912 1 23 0
## 870 2.498652 80.06683 0 24 0
## 871 3.596160 78.91403 0 22 0
## 872 3.896482 95.53657 0 14 0
## 873 1.628980 95.59315 0 17 0
## 874 2.054507 59.08856 0 16 1
## 875 1.964383 78.60556 1 12 1
## 876 3.026293 77.97296 0 18 0
## 877 2.192938 59.88325 0 21 0
## 878 2.286885 78.52011 1 12 0
## 879 2.736887 62.35187 0 22 0
## 880 3.023848 87.41897 1 15 0
## 881 2.172603 59.82418 1 19 0
## 882 3.568607 62.92300 1 24 0
## 883 3.852168 94.77656 0 16 0
## 884 2.974154 75.85624 1 22 0
## 885 2.537223 59.10469 0 20 0
## 886 2.666608 64.10644 0 16 0
## 887 2.320376 67.94476 0 12 0
## 888 3.332499 89.61143 0 15 0
## 889 3.280551 82.35139 1 18 0
## 890 2.816643 51.39545 0 16 0
## 891 2.361150 94.69001 0 19 0
## 892 2.614681 64.67205 0 24 0
## 893 1.775722 74.06592 0 18 0
## 894 3.770336 50.94882 0 17 0
## 895 2.251148 82.80223 0 19 0
## 896 2.093557 86.00954 1 15 0
## 897 2.936999 58.40209 1 23 0
## 898 2.530350 77.69108 0 15 0
## 899 2.352789 86.44548 0 18 0
## 900 2.982593 61.85640 0 15 0
## 901 1.824501 97.65028 1 16 0
## 902 2.667572 90.79450 0 14 0
## 903 3.534833 96.47765 0 13 0
## 904 2.711127 56.16491 1 13 0
## 905 1.989620 51.32088 0 20 0
## 906 3.240183 55.37696 0 12 0
## 907 1.529358 86.19448 1 17 1
## 908 2.974615 52.13689 0 15 0
## 909 3.445681 61.76648 0 14 0
## 910 3.462864 70.23150 0 20 0
## 911 3.580489 52.84628 0 12 0
## 912 1.526111 79.46250 0 21 0
## 913 3.201429 82.27157 0 19 0
## 914 2.226316 82.83164 0 21 0
## 915 3.423814 71.40831 1 15 0
## 916 2.560457 73.48958 1 19 0
## 917 1.783076 76.22776 0 14 0
## 918 1.681845 78.85864 0 23 1
## 919 3.727133 91.79829 0 20 0
## 920 1.566106 71.21002 0 19 0
## 921 3.358356 92.66495 0 16 0
## 922 3.722556 52.83731 0 15 0
## 923 3.343549 60.44842 0 23 0
## 924 1.859315 91.43886 0 12 0
## 925 3.261535 88.32022 0 14 0
## 926 2.655496 79.68912 0 15 0
## 927 3.702595 51.37454 1 18 0
## 928 3.645471 91.45910 1 20 0
## 929 1.645145 65.92851 1 16 1
## 930 2.853066 95.81641 0 12 0
## 931 2.706934 68.27582 0 24 0
## 932 1.522530 91.77812 0 23 0
## 933 2.886188 68.08608 0 18 0
## 934 3.091629 57.33398 0 17 0
## 935 3.756851 81.99713 0 17 0
## 936 1.833797 79.45484 0 13 1
## 937 3.919746 51.84077 0 19 0
## 938 2.485977 66.52769 0 22 0
## 939 2.197172 52.68958 1 17 0
## 940 2.926778 87.24195 1 23 0
## 941 3.451075 91.89629 0 18 0
## 942 1.992862 52.21409 0 24 0
## 943 2.543668 64.23469 0 13 0
## 944 2.242507 66.46331 0 12 0
## 945 2.440681 57.14895 0 22 0
## 946 2.301203 50.80950 0 21 1
## 947 2.709145 69.61147 0 12 0
## 948 2.253883 78.85755 1 12 0
## 949 2.536730 84.78098 0 22 0
## 950 2.347201 92.21088 1 12 0
## 951 2.712999 79.30131 1 24 0
## 952 2.737769 53.35157 1 22 0
## 953 2.689375 79.85440 1 21 0
## 954 1.547995 53.37658 1 24 1
## 955 2.077033 93.85315 0 24 0
## 956 2.857371 64.49185 0 14 0
## 957 2.234825 60.02012 1 14 1
## 958 2.189794 86.40705 1 24 0
## 959 3.362373 51.14945 1 16 0
## 960 2.850161 79.90417 0 23 0
## 961 3.050493 54.11650 0 14 0
## 962 3.962566 83.18263 0 19 0
## 963 2.185095 79.45725 1 17 1
## 964 3.745420 95.39409 0 23 0
## 965 2.109205 68.31259 0 17 0
## 966 2.980445 64.65432 0 15 0
## 967 3.302286 52.09553 0 19 0
## 968 3.216793 76.22379 1 22 0
## 969 1.966118 54.46797 0 15 1
## 970 1.573906 85.25287 0 23 1
## 971 3.090159 81.28234 1 15 0
## 972 3.976373 80.85073 0 16 0
## 973 2.520224 75.72655 0 18 0
## 974 3.588405 93.05308 0 18 0
## 975 2.982247 51.22412 0 22 0
## 976 3.200263 69.64384 0 16 0
## 977 2.278940 59.48217 0 22 0
## 978 2.306773 71.16318 0 20 0
## 979 3.602409 68.64083 1 15 0
## 980 3.885669 82.72960 0 21 0
## 981 2.729842 58.68840 0 22 0
## 982 3.640377 67.93554 1 22 0
## 983 2.617034 89.48593 1 13 0
## 984 3.170660 86.53184 0 12 0
## 985 3.125910 68.54102 0 21 0
## 986 2.532255 70.13590 0 14 0
## 987 2.547017 55.80213 0 17 0
## 988 1.655449 87.70877 0 13 0
## 989 1.993143 85.91366 0 22 0
## 990 3.346894 62.18120 0 17 0
## 991 2.893768 92.66290 0 23 0
## 992 3.278305 59.57020 0 20 0
## 993 2.188608 51.16279 0 13 1
## 994 3.116069 69.68015 0 23 0
## 995 3.570298 66.36159 0 19 0
## 996 2.815826 72.34836 0 13 0
## 997 1.660408 53.88585 1 22 1
## 998 3.274822 60.10570 1 14 0
## 999 3.276149 88.97774 0 19 0
## 1000 3.140389 87.86248 0 12 0
# Visualisasi Karakteristik Data
par(mfrow=c(1,2))
hist(ipk, breaks=20, col="skyblue", main="Distribusi IPK Sintetis", xlab="IPK")
plot(df$Dropout, col=c("green", "red"), main="Proporsi Status Mahasiswa", ylab="Jumlah")
par(mfrow=c(1,1))
# 4. PEMODELAN REGRESI LOGISTIK
model_logit <- glm(Dropout ~ IPK + Kehadiran + Status_Kerja + Beban_SKS,
data = df, family = "binomial")
cat("\n--- RINGKASAN ESTIMASI PARAMETER ---\n")
##
## --- RINGKASAN ESTIMASI PARAMETER ---
print(summary(model_logit))
##
## Call:
## glm(formula = Dropout ~ IPK + Kehadiran + Status_Kerja + Beban_SKS,
## family = "binomial", data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 12.38032 1.57648 7.853 4.06e-15 ***
## IPK -4.10214 0.45304 -9.055 < 2e-16 ***
## Kehadiran -0.06341 0.01112 -5.700 1.20e-08 ***
## Status_Kerja1 1.54764 0.31230 4.956 7.21e-07 ***
## Beban_SKS -0.10447 0.04094 -2.552 0.0107 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 576.86 on 999 degrees of freedom
## Residual deviance: 318.77 on 995 degrees of freedom
## AIC: 328.77
##
## Number of Fisher Scoring iterations: 8
# 5. SIMULASI BOOTSTRAP
n_boot <- 1000
boot_results <- numeric(n_boot)
for(i in 1:n_boot) {
# Resampling dengan pengembalian (Metode Week 6)
df_resample <- df[sample(nrow(df), replace = TRUE), ]
m_boot <- glm(Dropout ~ IPK + Kehadiran + Status_Kerja + Beban_SKS,
data = df_resample, family = "binomial")
boot_results[i] <- coef(m_boot)["IPK"]
}
# Visualisasi Hasil Bootstrap
hist(boot_results, breaks=30, col="orange", border="white",
main="Distribusi Sampling Bootstrap (Koefisien IPK)",
xlab="Nilai Estimasi Koefisien")
abline(v = mean(boot_results), col="red", lwd=2) # Mean Bootstrap
abline(v = coef(model_logit)["IPK"], col="blue", lty=2) # Estimasi Asli
# 6. EVALUASI
# Evaluasi Akurasi
df$pred_prob <- predict(model_logit, type = "response")
df$pred_class <- factor(ifelse(df$pred_prob > 0.5, 1, 0), levels = c(0, 1))
cat("\n--- METRIK EVALUASI KINERJA ---\n")
##
## --- METRIK EVALUASI KINERJA ---
conf_matrix <- confusionMatrix(df$pred_class, df$Dropout)
print(conf_matrix)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 901 48
## 1 15 36
##
## Accuracy : 0.937
## 95% CI : (0.9201, 0.9513)
## No Information Rate : 0.916
## P-Value [Acc > NIR] : 0.007821
##
## Kappa : 0.5017
##
## Mcnemar's Test P-Value : 5.539e-05
##
## Sensitivity : 0.9836
## Specificity : 0.4286
## Pos Pred Value : 0.9494
## Neg Pred Value : 0.7059
## Prevalence : 0.9160
## Detection Rate : 0.9010
## Detection Prevalence : 0.9490
## Balanced Accuracy : 0.7061
##
## 'Positive' Class : 0
##
# Grafik Kurva Sigmoid Final
ggplot(df, aes(x = IPK, y = as.numeric(as.character(Dropout)))) +
geom_point(aes(color = Dropout), alpha = 0.3, position = position_jitter(height = 0.02)) +
geom_smooth(method = "glm", method.args = list(family = "binomial"),
formula = y ~ x, se = TRUE, color = "blue") +
scale_color_manual(values = c("0" = "seagreen", "1" = "firebrick"),
labels = c("Aktif", "Dropout")) +
labs(title = "Simulasi Probabilitas Risiko Dropout",
subtitle = "Visualisasi Berdasarkan IPK",
x = "Indeks Prestasi Kumulatif (IPK)",
y = "Probabilitas Dropout (0 ke 1)") +
theme_minimal()
