Tugas Praktikum Clustering
Ulya
2025-11-12
Import Data dan Library
Sumber : https://www.kaggle.com/datasets/arjunbhasin2013/ccdata
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.2## Warning: package 'ggplot2' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.2## Warning: package 'readr' was built under R version 4.4.2## Warning: package 'purrr' was built under R version 4.4.2## Warning: package 'stringr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.2## Warning: package 'lubridate' was built under R version 4.4.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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library(caret)## Warning: package 'caret' was built under R version 4.4.3## Loading required package: lattice
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## Attaching package: 'caret'
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library(tidyr)
library(scales)##
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## discard
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## col_factorlibrary(RColorBrewer)dataship <- read.csv("C:/Users/Resea/Documents/Semester 5/TPG/Analisis Gerombol/Ship_Performance_Dataset.csv")
dataship <- dataship[1:500, 6:9]
dataship## Speed_Over_Ground_knots Engine_Power_kW Distance_Traveled_nm Draft_meters
## 1 12.59756 2062.9840 1030.94362 14.132284
## 2 10.38758 1796.0574 1060.48638 14.653083
## 3 20.74975 1648.5567 658.87414 7.199261
## 4 21.05510 915.2618 1126.82252 11.789063
## 5 13.74278 1089.7218 1445.28116 9.727833
## 6 18.61620 2171.6466 723.30421 14.916320
## 7 20.43312 2505.0435 270.11859 8.455264
## 8 23.49805 814.8065 1717.32841 9.283780
## 9 17.30936 1179.0188 429.56431 6.002358
## 10 23.22710 2685.4017 1588.79228 11.721261
## 11 11.63511 2522.6972 1224.84627 10.900540
## 12 18.25358 2129.5179 1311.97858 11.955648
## 13 10.22534 1599.1261 1499.72361 11.136576
## 14 12.50124 2780.4496 1474.07188 7.620740
## 15 11.71878 1994.0251 1320.45784 12.145870
## 16 15.08722 578.8929 1932.21313 12.998270
## 17 20.73784 2525.6729 1813.94557 10.943832
## 18 14.67204 1930.3790 884.79010 5.294925
## 19 23.76429 1771.7695 1061.93297 8.521647
## 20 22.63880 1504.1159 1146.91562 12.558873
## 21 23.80385 1120.6992 1169.47850 10.544799
## 22 22.76036 2335.2629 1461.91754 7.980762
## 23 18.75487 1305.4237 354.59967 8.052450
## 24 15.22835 1283.1425 713.20555 10.911586
## 25 18.06901 2922.2826 559.86135 10.417704
## 26 17.67156 1845.7647 1708.79535 10.508273
## 27 20.99496 2296.3298 275.18711 7.124895
## 28 14.06855 2658.6562 1799.79640 12.081070
## 29 18.00365 1643.9365 177.16643 9.288473
## 30 11.02444 2957.6141 801.86435 13.644204
## 31 15.96204 540.9402 1376.42028 14.675854
## 32 21.42788 2027.8854 1694.34412 11.765945
## 33 18.05569 2940.9806 1674.05609 14.255468
## 34 14.48895 1541.6160 1602.75540 10.249176
## 35 23.29097 520.0793 1532.27522 11.462349
## 36 10.19866 504.4811 378.28538 12.145465
## 37 23.03682 1228.1039 150.19837 7.434287
## 38 13.04007 2012.8877 1057.33372 13.378386
## 39 18.26541 1223.1646 1592.74283 6.831529
## 40 24.27464 2041.3466 1487.37172 5.571069
## 41 22.89761 2450.5793 1229.56610 6.420262
## 42 19.41907 709.2840 524.84958 7.788783
## 43 19.08942 1095.2398 1801.17494 14.086731
## 44 21.61731 2065.6516 1940.01072 9.754800
## 45 16.01010 2523.3902 491.25992 13.890456
## 46 22.36635 2814.3636 961.82041 5.728426
## 47 12.79452 655.5854 993.38838 5.985634
## 48 19.83684 1630.2344 343.49013 14.921601
## 49 22.63437 2212.7052 732.29402 6.111994
## 50 12.28505 1398.2084 317.98342 5.450862
## 51 22.80571 1238.7209 1635.06560 5.789772
## 52 24.14346 543.3802 1311.11802 5.315510
## 53 20.30487 1763.0069 1794.33912 7.291651
## 54 13.62202 1796.7442 948.24020 6.335936
## 55 14.86132 709.3117 1598.03697 6.734772
## 56 23.09077 907.9932 329.76634 5.830493
## 57 16.88110 2386.4725 281.73023 13.559434
## 58 22.97615 2607.6572 185.58222 14.424414
## 59 12.62777 2310.3487 714.52841 14.771734
## 60 16.20612 1694.9922 1126.39547 6.091262
## 61 21.72076 1087.7231 1504.26123 9.384915
## 62 22.23475 1410.1987 1163.57630 9.555535
## 63 14.23324 2871.3403 1864.20068 9.864785
## 64 13.03322 1880.7677 1782.56048 11.971983
## 65 13.62630 2171.9131 1884.78458 8.037293
## 66 24.11178 2085.0796 1534.45979 14.296864
## 67 18.79723 2522.0872 1396.16698 9.850449
## 68 10.33086 762.0545 738.34712 11.900300
## 69 15.53457 2489.8838 1553.63649 8.770965
## 70 15.19006 1765.8056 603.39694 5.120040
## 71 24.60838 1528.2264 248.13984 14.645511
## 72 15.37892 1144.1660 1648.53065 6.649504
## 73 10.86055 678.1802 1367.99208 9.040671
## 74 15.81596 1131.9954 1588.17928 14.723086
## 75 22.94994 2568.9986 582.16663 13.400490
## 76 24.07484 2397.8916 941.27195 11.679049
## 77 21.94581 759.2487 763.15677 6.387618
## 78 21.45526 2290.7194 489.57086 8.974810
## 79 18.42257 1904.8953 554.36956 9.785255
## 80 20.56654 2351.5551 113.85873 13.064045
## 81 22.52226 1285.5119 1617.06039 6.496371
## 82 23.66038 2539.6503 1989.84575 7.177801
## 83 24.75791 2798.1599 1677.16931 13.943580
## 84 21.16316 2204.8809 1283.73559 11.913643
## 85 22.35513 2126.2191 1496.56761 5.932365
## 86 10.92496 2073.6947 803.89325 6.499953
## 87 12.06805 1625.0007 203.50451 9.533687
## 88 12.83738 2212.3856 1045.77787 13.258846
## 89 21.54155 1097.8258 507.09246 5.356473
## 90 22.39074 1360.1954 1889.55668 6.713454
## 91 18.75395 534.7910 1809.76993 5.063586
## 92 22.53122 994.0679 1324.91849 14.294956
## 93 23.23114 990.9944 872.29015 13.170737
## 94 23.16255 1031.0331 87.19641 9.420665
## 95 19.37909 2371.6461 790.34010 14.350742
## 96 22.75165 884.8436 203.55300 11.187078
## 97 22.73431 2261.8987 1096.49090 7.143937
## 98 17.94855 1681.5346 350.46830 11.815473
## 99 24.38103 1309.6090 842.60113 10.637096
## 100 22.05902 2029.9786 1088.04479 11.561657
## 101 19.01606 1085.7584 294.63756 14.614545
## 102 18.08165 585.1951 72.61348 10.732201
## 103 20.70788 2647.5406 190.36586 6.367843
## 104 11.28591 847.7019 1795.99796 6.776043
## 105 22.72033 869.2678 210.74791 9.462868
## 106 13.85571 1290.3930 1531.71903 8.563355
## 107 22.02165 1682.8123 658.64762 5.125582
## 108 15.08537 2184.9140 1867.47283 11.506457
## 109 22.66313 1657.7807 1029.72957 12.129401
## 110 20.11810 2650.9363 1376.30574 12.329766
## 111 11.21137 1892.0776 173.85468 11.720547
## 112 21.61077 1184.2806 372.27738 14.179822
## 113 11.36411 2499.9373 1133.16428 10.178758
## 114 17.23082 829.6164 824.76913 8.375318
## 115 17.56600 2813.3228 1923.78017 12.323734
## 116 23.60755 1537.1550 307.06781 7.038043
## 117 21.03046 1934.0625 1033.81492 6.774869
## 118 14.43417 1432.6218 143.18551 14.906479
## 119 19.25728 940.2119 1288.51529 10.212289
## 120 23.85716 1779.5723 1744.34571 13.910377
## 121 13.76247 2693.6342 76.81854 13.154255
## 122 19.29693 818.2716 1781.64835 12.723021
## 123 19.87479 892.0090 1364.92789 12.090510
## 124 17.45314 2521.8167 1955.86233 10.367868
## 125 17.30228 1471.8151 143.36083 14.456739
## 126 22.85310 893.4271 1822.02011 5.157223
## 127 10.13615 1413.9877 492.33489 13.164127
## 128 18.06087 2894.6493 819.54242 5.073916
## 129 22.70870 1512.1199 771.34059 9.854360
## 130 12.35433 2281.8721 701.39206 14.454351
## 131 15.93637 1622.4763 1471.94399 11.548265
## 132 24.11754 866.9226 599.29538 7.032850
## 133 16.89182 609.4533 1653.69701 10.591492
## 134 23.02676 2624.9315 1607.74856 11.709188
## 135 14.64564 2605.7648 1714.86801 11.434511
## 136 14.65417 1313.7168 1000.23274 10.707528
## 137 22.55050 638.2856 444.90771 11.292760
## 138 11.52589 1895.9180 1391.93661 10.306034
## 139 10.56409 2328.2279 68.08607 7.556609
## 140 21.27536 2990.7950 201.24938 14.910589
## 141 11.28004 1554.2889 170.93011 12.574490
## 142 11.50912 712.0045 1417.08103 6.450089
## 143 16.06011 580.8206 817.44203 7.715641
## 144 11.82553 1131.3126 601.84733 13.561475
## 145 24.87095 666.6610 268.37372 5.627579
## 146 22.16899 2338.6079 1024.43027 12.516954
## 147 10.05167 2403.9187 779.07341 10.117509
## 148 18.01376 2072.9331 965.63810 12.592137
## 149 17.87312 782.8303 431.47821 9.636206
## 150 11.75901 1400.6178 630.72542 7.729929
## 151 18.73786 1139.8074 409.55646 8.628002
## 152 24.31780 1895.1698 1466.59770 11.046365
## 153 18.19935 1777.7972 634.69903 14.966545
## 154 22.03319 1775.9926 427.88466 7.820235
## 155 21.63615 2645.4427 747.01552 10.962876
## 156 12.41344 1629.2877 162.84889 13.226933
## 157 14.51956 727.4354 1531.94300 11.859090
## 158 20.51474 1240.0738 1463.46202 5.698510
## 159 11.14641 663.0705 1674.72228 6.291202
## 160 19.63677 1726.3137 597.46952 11.388067
## 161 22.39553 2850.9903 1327.00600 10.302152
## 162 20.66860 768.7005 1970.77069 13.499041
## 163 18.78012 815.6819 599.01096 5.237161
## 164 13.06222 998.2116 977.77829 14.684212
## 165 24.66249 2330.0596 1453.18759 11.690578
## 166 16.37537 2638.2389 710.58144 8.245658
## 167 18.34635 1220.9464 570.24032 7.900719
## 168 17.48104 2158.4206 474.81489 7.167293
## 169 18.88184 911.1700 406.89787 9.256225
## 170 15.07896 1751.1251 1566.28928 10.094847
## 171 20.26043 1736.8883 1905.15339 12.370077
## 172 13.56689 842.4208 1478.09756 12.600473
## 173 12.07762 2120.1754 694.43932 9.976304
## 174 22.24648 680.8540 696.91420 8.827087
## 175 22.62739 2422.0680 1135.26821 11.400924
## 176 20.58443 860.2095 1577.80435 7.782659
## 177 14.38716 2809.6190 461.23370 10.191207
## 178 20.49400 2876.7584 358.93452 12.937578
## 179 17.81392 2993.2265 351.97585 10.272933
## 180 20.91340 1045.6998 248.67442 9.101045
## 181 17.39708 1271.0948 579.97523 12.565405
## 182 14.76112 2962.4354 1475.65044 14.825901
## 183 20.55081 1467.0109 1054.77458 7.624080
## 184 17.84055 1441.3594 991.03464 11.298249
## 185 14.15602 2058.2113 1102.15834 14.020608
## 186 13.93965 2928.9597 1494.31538 8.277471
## 187 16.74015 2396.6385 891.74842 5.365741
## 188 11.96620 2371.0740 551.52412 6.750113
## 189 24.16161 1088.3051 688.47461 5.595817
## 190 10.68182 2489.6068 902.36614 12.700776
## 191 13.20963 1528.4347 123.11414 12.659648
## 192 21.19953 1464.1173 804.68353 11.875788
## 193 22.43233 2531.1060 561.69343 10.652707
## 194 10.40343 2081.8593 1711.64293 13.320022
## 195 23.82119 730.4000 1760.57059 13.889950
## 196 23.94911 2915.4408 758.11073 13.559499
## 197 11.12406 1493.9086 1279.96895 11.590024
## 198 10.69954 928.5280 1899.87729 5.281448
## 199 12.25630 568.2896 495.49770 11.859816
## 200 24.41803 1510.5443 968.54221 5.416479
## 201 14.74407 1846.0898 1586.11052 5.800639
## 202 10.18917 2585.8496 692.06312 7.925640
## 203 24.47517 2253.4898 568.50285 12.313777
## 204 18.69641 542.9951 89.98700 14.949668
## 205 11.47550 1889.1038 1085.56966 11.449172
## 206 15.43850 654.1636 1143.69280 7.699517
## 207 13.01345 2613.1451 1705.87070 7.659909
## 208 23.74410 1925.0861 124.81231 12.197450
## 209 24.99704 1644.3030 1576.45114 14.704515
## 210 15.48029 886.4264 272.29130 8.613753
## 211 13.95550 2143.3057 558.32856 6.309419
## 212 10.60690 1481.4108 1902.15448 9.201779
## 213 15.12508 2947.9475 555.28244 7.418694
## 214 19.83196 1300.4309 91.43988 13.398558
## 215 22.20826 1971.8521 654.26091 6.686104
## 216 19.69080 2592.1127 1794.53297 5.898323
## 217 18.69271 1143.1765 255.20026 9.279358
## 218 16.05152 1181.8845 1790.84440 5.537588
## 219 10.36508 1195.3456 247.05651 12.560964
## 220 23.39345 2356.6645 1788.57365 7.548321
## 221 13.98517 1814.5751 1830.54096 5.066375
## 222 22.37753 1498.2242 448.89575 7.576489
## 223 13.87824 1576.4300 1682.68380 7.067593
## 224 14.22455 2771.7336 1814.09031 7.231079
## 225 19.16115 2362.8886 1706.67667 8.354080
## 226 20.58447 2416.6637 484.69393 10.950854
## 227 20.36535 2390.4295 427.82286 6.172464
## 228 12.19867 1905.9235 398.45113 6.702690
## 229 17.05728 965.5039 799.43980 8.801820
## 230 17.56723 541.0039 861.29050 6.821483
## 231 19.70461 659.1855 1330.15778 11.224658
## 232 15.76097 1201.4830 856.54641 5.738156
## 233 15.23155 2852.7968 591.97238 12.770144
## 234 21.07038 993.2413 1282.97113 12.956110
## 235 14.81196 2456.0574 1216.57966 14.983936
## 236 16.81515 777.4765 538.30515 12.703134
## 237 17.07090 942.9747 913.11148 14.485655
## 238 16.79687 1579.7496 539.79202 13.779696
## 239 11.37471 1626.3646 691.12398 7.286178
## 240 17.45325 683.9206 1671.78654 9.082201
## 241 24.19857 2518.2603 1933.31219 5.723345
## 242 19.69463 1247.8495 1520.46665 7.256666
## 243 17.51046 1993.1633 947.43614 11.788187
## 244 14.78317 1514.2993 674.90607 11.365947
## 245 10.21984 1498.6574 885.94919 7.265604
## 246 24.72513 1846.7498 372.11934 7.078740
## 247 13.59747 1399.4045 1763.21662 14.264679
## 248 13.78183 596.6413 1917.90036 11.356678
## 249 13.67086 2945.4649 399.16296 7.928567
## 250 11.30168 2394.1770 1691.74741 11.718317
## 251 21.12691 2501.2889 480.86677 5.975342
## 252 12.96759 2150.5034 1891.63610 8.444983
## 253 20.15349 982.3062 1587.28870 12.951893
## 254 10.98623 914.8781 810.65382 5.907498
## 255 10.37825 2554.9075 1608.32446 9.140081
## 256 17.24386 1952.6281 1792.69093 5.942868
## 257 20.25066 1675.6762 1396.55904 8.661889
## 258 21.74340 1705.8739 1288.79092 14.107809
## 259 10.68415 1035.6881 1728.31782 6.971895
## 260 24.77926 2147.8650 1795.79808 5.281270
## 261 15.50346 2077.4356 1200.53708 14.370975
## 262 18.94946 2108.8000 1094.47814 12.864760
## 263 16.19108 2459.2758 262.57618 13.326276
## 264 21.02504 1257.2892 694.52843 11.609566
## 265 13.93863 2488.5673 422.63992 10.937113
## 266 12.07026 1634.6894 492.87142 5.382325
## 267 11.19883 1059.7294 675.54172 8.087615
## 268 13.74401 1073.4706 662.24832 7.789510
## 269 24.29276 2272.7000 1709.52240 8.527637
## 270 23.75283 1371.0885 292.98108 10.178696
## 271 10.47458 2191.3594 607.92481 7.267483
## 272 16.85060 1395.3627 1263.67390 14.554244
## 273 10.21642 938.1992 459.04181 12.756660
## 274 20.34045 2042.4747 459.10350 12.164557
## 275 22.48191 789.4537 664.83269 7.513533
## 276 18.69034 2073.8102 1366.96217 7.148470
## 277 11.53760 688.6105 1899.96187 10.488899
## 278 10.89135 1084.4696 506.31064 11.954054
## 279 20.92356 695.0024 307.19588 9.870327
## 280 19.28948 811.6861 649.80752 10.312133
## 281 16.78573 2952.9545 1279.18256 6.731708
## 282 21.62287 1504.3347 1446.94296 11.407849
## 283 17.96829 2345.1970 1308.66229 10.452187
## 284 12.49472 2503.9141 1879.33911 11.451643
## 285 13.45747 2525.3298 496.67393 14.024181
## 286 12.09319 2122.3334 1415.60517 14.209162
## 287 20.20112 732.9764 592.19361 13.677871
## 288 24.48669 924.0985 1723.93937 11.721611
## 289 13.55199 982.9855 703.77913 13.619149
## 290 16.69825 2303.2087 129.21452 9.382540
## 291 15.48651 875.1495 817.64217 13.051408
## 292 13.19546 2694.8446 870.35354 9.177140
## 293 15.29386 900.7127 269.88963 11.638994
## 294 12.95549 2335.4603 1739.20673 7.486446
## 295 15.02204 2183.8012 1429.61218 7.854958
## 296 10.15075 2044.5469 1606.80184 14.335679
## 297 17.07069 1304.6907 1528.37832 13.739565
## 298 21.10019 2606.7176 1106.70310 5.154981
## 299 24.26534 1343.8706 969.82082 5.226115
## 300 15.54197 2687.4207 1979.17849 9.997487
## 301 18.62798 1282.7182 1084.00394 5.478139
## 302 24.91018 2357.8178 164.76103 10.133511
## 303 11.27010 2188.0041 498.78583 8.603338
## 304 10.11937 1214.1633 1646.33331 12.301405
## 305 20.07304 1662.1247 438.96550 6.223896
## 306 24.86511 1239.9107 1545.68080 10.157419
## 307 22.50830 1856.7310 491.85561 7.372930
## 308 11.81257 2278.6126 713.90581 8.043870
## 309 14.80961 566.8926 1733.72893 13.144292
## 310 23.60748 2039.0007 559.63574 8.344844
## 311 23.92748 2392.2610 1504.69077 13.012022
## 312 12.15287 2764.8954 878.80993 5.179758
## 313 17.40898 2825.2216 1041.73414 13.799273
## 314 12.72063 2410.4822 1790.77937 8.233912
## 315 16.30208 2268.0562 1879.14528 5.952393
## 316 23.78714 1252.3859 79.95411 13.014097
## 317 16.36894 699.8535 837.79209 7.192182
## 318 18.29807 2334.3637 449.36677 8.030344
## 319 14.33096 990.7571 300.15452 10.389873
## 320 17.60270 1682.3814 679.71560 5.017875
## 321 14.37938 1197.6769 326.07014 14.636441
## 322 20.53148 2318.9712 1354.25034 13.200091
## 323 16.38558 2427.3243 748.23893 10.810900
## 324 14.32620 674.1713 1772.04209 6.786579
## 325 18.89742 1848.2761 1226.13715 6.596924
## 326 24.98046 2983.2368 1805.67168 8.686142
## 327 21.85748 1946.8077 1090.76237 10.659062
## 328 14.29104 1863.7275 1980.63497 14.253202
## 329 10.86065 1421.5042 1975.64753 10.975940
## 330 19.66445 1751.8946 1760.47418 14.002834
## 331 15.39904 866.0199 1560.20542 8.335197
## 332 18.29273 1122.7665 1817.78287 12.550190
## 333 22.30826 2371.5328 1487.76349 5.269242
## 334 19.83611 1805.1895 1289.79146 10.488185
## 335 24.05516 1746.6291 1695.86810 5.314281
## 336 21.75192 1505.5523 1060.56120 7.319652
## 337 16.10623 800.3541 1535.81801 8.418789
## 338 14.77774 1133.8053 907.37894 10.074222
## 339 17.58910 1475.0232 301.85946 7.562543
## 340 11.09147 836.9753 443.20421 10.359196
## 341 12.48359 2451.0503 628.20700 9.841371
## 342 21.62989 878.3880 1447.95499 7.649765
## 343 24.47958 864.8487 1874.12366 7.690806
## 344 22.04455 599.6047 540.70829 13.405805
## 345 18.18857 1376.1421 989.60150 10.625667
## 346 19.10073 2035.6427 785.18650 9.187279
## 347 13.62567 1170.2513 1189.11654 12.000004
## 348 22.29813 1644.2857 1880.66152 6.670571
## 349 23.43620 2516.1729 68.82644 7.771379
## 350 22.83736 1940.3339 313.26390 9.393485
## 351 15.83526 867.5134 1622.08750 7.193346
## 352 15.16854 2777.1350 1340.46743 5.648314
## 353 17.21046 725.1323 1786.32106 6.760727
## 354 24.60594 1117.7911 641.19311 12.945723
## 355 17.22714 1068.7075 87.76439 8.985557
## 356 18.02975 599.3705 586.68752 9.813566
## 357 16.90270 1659.9764 396.25800 14.466860
## 358 15.94673 2803.7347 1710.66751 8.554325
## 359 16.07347 2483.3363 363.50183 10.349216
## 360 20.92901 1569.6016 740.43434 9.592651
## 361 10.77142 1372.1658 214.68942 13.066990
## 362 10.88392 773.3206 348.67994 7.148942
## 363 18.56602 1517.0135 1852.62895 6.763938
## 364 14.38931 2394.6880 1714.85451 9.332139
## 365 14.44364 1065.8398 101.92520 9.002369
## 366 19.43890 1528.3875 840.40157 11.057214
## 367 24.59505 1001.2240 550.92821 14.423150
## 368 24.33228 1151.0910 874.82904 5.771854
## 369 17.39244 1329.7975 961.12312 5.210404
## 370 14.26124 2143.2141 1335.98326 12.500726
## 371 13.27338 711.8135 371.35494 8.038047
## 372 13.14713 1631.7841 1363.31288 12.208808
## 373 10.63952 2290.9487 1127.54607 7.798359
## 374 10.51714 1297.5769 510.80050 12.882525
## 375 22.85721 1992.5590 465.01218 13.565908
## 376 11.33976 2382.2897 698.01430 9.397910
## 377 11.72158 1584.5406 1181.81888 8.355455
## 378 17.82181 1287.3812 1040.99248 6.670863
## 379 15.88049 1597.5024 1664.25004 6.034248
## 380 14.91295 2150.5805 1282.37717 8.467009
## 381 15.36562 1269.5762 1271.10150 10.397680
## 382 19.29671 2807.7068 94.66449 9.453793
## 383 16.63115 520.8292 1786.85678 8.150682
## 384 24.91462 2489.7662 530.14095 7.512101
## 385 12.57357 1795.8166 1726.01713 12.459288
## 386 24.23783 2359.7612 1249.67613 13.015400
## 387 18.11934 1429.0771 477.20374 6.921955
## 388 23.45058 1267.7026 120.03689 6.918317
## 389 13.82486 1071.6219 102.27719 12.056533
## 390 14.93556 905.1408 1416.34188 10.938678
## 391 13.54432 2295.7322 1686.54383 14.701341
## 392 21.90359 1329.5868 600.16795 5.272282
## 393 15.41297 1096.0759 1839.71562 8.389246
## 394 10.53841 721.1233 103.51220 5.110126
## 395 19.43631 2971.8961 1458.65627 14.333634
## 396 21.54807 1719.9553 340.01025 5.046245
## 397 12.62286 1034.8714 1388.66520 10.959822
## 398 14.76304 2706.1120 1833.23429 8.453122
## 399 21.33223 717.1855 697.49255 12.962534
## 400 19.91154 2726.2979 1626.31454 13.481559
## 401 13.91393 2722.3980 1631.42853 9.669865
## 402 24.36955 2559.5429 577.93958 9.412266
## 403 22.48708 1537.1612 1825.73825 6.495941
## 404 15.19615 2714.7809 1319.96275 7.565454
## 405 23.34410 2102.7271 1382.77146 8.821511
## 406 20.75947 1973.6418 329.88882 11.053048
## 407 19.23999 1099.2550 1315.05619 8.870302
## 408 11.37433 1678.8396 188.23395 13.282741
## 409 23.08676 1446.4882 88.07393 14.410304
## 410 10.68141 810.6404 1057.29341 13.568590
## 411 12.60039 1387.7423 461.36883 11.639389
## 412 20.11692 2325.6121 1090.50249 14.606360
## 413 23.95394 1415.6751 52.28661 5.837265
## 414 17.85581 554.8260 520.49980 8.434534
## 415 20.14696 2122.1020 1082.78977 13.216944
## 416 18.74248 2754.7981 169.77995 6.952519
## 417 21.67848 2948.7279 538.89519 10.072658
## 418 16.44646 2463.5463 279.65153 8.677564
## 419 22.17694 965.4655 1194.62023 8.806841
## 420 12.80061 2995.5496 1285.78290 8.638543
## 421 13.59054 1317.5852 1906.34670 7.439784
## 422 19.45618 817.8916 919.88829 5.722567
## 423 14.37013 2086.3589 612.20586 5.480212
## 424 13.38918 1591.8048 204.94327 6.517172
## 425 13.57737 2047.9701 968.15564 8.697701
## 426 14.24837 2247.8866 234.23564 7.583368
## 427 17.57109 1798.8604 425.67139 12.563189
## 428 22.15218 2892.1814 1153.91073 5.707939
## 429 14.46030 834.1108 986.11220 14.924104
## 430 19.18988 2109.9656 482.18406 10.079461
## 431 24.50526 2494.4207 1882.56799 13.516766
## 432 14.38292 2125.4026 950.64260 10.203466
## 433 21.91714 1826.4702 1210.98948 12.384915
## 434 21.47639 568.2403 873.42653 14.624869
## 435 20.41151 1058.3499 803.50800 7.710338
## 436 11.50406 2586.3702 1975.73976 10.222109
## 437 11.11041 1020.9152 528.04692 13.599187
## 438 20.66718 2031.2802 610.58887 11.358770
## 439 17.13324 1894.6660 1597.68657 10.697587
## 440 22.45786 2874.9285 742.45079 6.859306
## 441 11.31514 1895.2596 109.52524 13.269937
## 442 17.02642 1831.0654 579.44131 13.764015
## 443 14.99106 2809.9075 1524.71581 10.567446
## 444 16.91619 2334.5899 987.29314 13.833227
## 445 13.76866 761.5061 1298.75507 12.461074
## 446 18.14361 2220.4322 621.47668 7.262309
## 447 17.03789 1482.4727 1389.89198 5.207722
## 448 21.43569 2153.1074 1104.41842 7.639041
## 449 12.68031 2930.7323 738.83932 8.307892
## 450 10.13271 1216.1019 1304.73306 10.053166
## 451 24.70540 937.5713 834.35013 14.724162
## 452 20.73297 1818.6281 242.03162 8.280086
## 453 22.87678 1836.2669 485.22847 10.968748
## 454 23.28563 1052.8380 145.95880 7.106194
## 455 11.14073 1528.4311 732.98569 7.242290
## 456 19.53223 1380.7025 1849.79899 10.830388
## 457 15.92743 1447.8669 1081.28486 6.992985
## 458 11.56165 506.0392 1895.73003 9.749492
## 459 21.24564 2924.2025 1608.55121 7.846189
## 460 22.61881 2061.0894 1321.95698 5.674680
## 461 12.50316 2805.2887 1034.09722 14.700947
## 462 15.88589 2365.4998 613.34524 10.787510
## 463 22.91557 1459.1685 1618.82794 13.155722
## 464 20.27755 901.7697 516.16624 7.338551
## 465 12.23214 1079.9900 737.25230 8.757592
## 466 16.58961 2465.2526 1940.10765 10.214133
## 467 17.86189 1879.0813 876.02426 11.927641
## 468 24.28534 1049.6293 891.99782 13.854832
## 469 21.32383 2745.8281 1957.45476 7.550977
## 470 14.75333 2268.9520 1178.65679 8.050030
## 471 17.41775 890.9653 1170.81313 14.666833
## 472 24.65124 1332.9501 308.33198 5.003111
## 473 17.92653 2244.8414 1846.53549 14.762431
## 474 19.94915 802.4332 493.81791 8.258445
## 475 24.83649 752.4284 1642.88044 9.022207
## 476 11.34886 2210.4414 251.39817 7.357909
## 477 14.26952 2920.9250 987.61811 6.824384
## 478 19.16296 1973.8371 505.81029 11.841614
## 479 15.34255 1862.9811 1554.73444 14.359155
## 480 20.44931 2696.3118 761.69071 12.104412
## 481 15.30153 2858.5454 402.29789 8.678886
## 482 12.97372 1372.2404 179.35919 14.482199
## 483 14.74902 2450.3907 1600.75528 13.902542
## 484 23.44672 2327.9927 1221.08270 14.404531
## 485 14.37662 689.3851 652.82091 6.428849
## 486 19.15116 1801.3142 1120.76283 9.754092
## 487 11.22011 2667.4441 1389.72831 12.794907
## 488 10.94850 1398.1565 1959.06154 14.101428
## 489 17.94453 1529.1442 200.69291 9.278412
## 490 18.61612 892.5283 1377.55695 6.546640
## 491 20.85463 1269.0787 1633.81426 11.938767
## 492 14.42847 576.7580 688.80859 10.240993
## 493 21.31766 1968.3299 743.45203 11.383523
## 494 18.34760 1425.4686 78.84229 11.478714
## 495 11.61937 2761.3404 171.68158 8.438952
## 496 24.94023 904.0825 692.37443 5.807838
## 497 17.96329 1304.0208 1241.83237 14.002911
## 498 16.50674 1876.8143 496.68015 8.256703
## 499 16.48706 1459.6766 1526.45699 13.108488
## 500 15.43963 2074.8044 657.10033 8.140894summary(dataship)## Speed_Over_Ground_knots Engine_Power_kW Distance_Traveled_nm Draft_meters
## Min. :10.05 Min. : 504.5 Min. : 52.29 Min. : 5.003
## 1st Qu.:13.95 1st Qu.:1095.9 1st Qu.: 514.82 1st Qu.: 7.430
## Median :17.74 Median :1682.0 Median : 969.18 Median : 9.987
## Mean :17.63 Mean :1712.8 Mean :1003.56 Mean : 9.949
## 3rd Qu.:21.49 3rd Qu.:2328.1 3rd Qu.:1525.15 3rd Qu.:12.525
## Max. :25.00 Max. :2995.5 Max. :1989.85 Max. :14.984str(dataship)## 'data.frame': 500 obs. of 4 variables:
## $ Speed_Over_Ground_knots: num 12.6 10.4 20.7 21.1 13.7 ...
## $ Engine_Power_kW : num 2063 1796 1649 915 1090 ...
## $ Distance_Traveled_nm : num 1031 1060 659 1127 1445 ...
## $ Draft_meters : num 14.13 14.65 7.2 11.79 9.73 ...Standarisasi
dataship_scaled <- scale(dataship)
head(dataship_scaled)## Speed_Over_Ground_knots Engine_Power_kW Distance_Traveled_nm Draft_meters
## 1 -1.1533124 0.50054294 0.04853001 1.42965004
## 2 -1.6594890 0.11895535 0.10089070 1.60764622
## 3 0.7138775 -0.09190582 -0.61091453 -0.93988621
## 4 0.7838165 -1.14019497 0.21846284 0.62879469
## 5 -0.8910096 -0.89079389 0.78288918 -0.07568265
## 6 0.2252057 0.65588264 -0.49672065 1.69761404Menentukan Matriks Jarak dengan Euclidean
distance_shipmatrix <- dist(dataship_scaled, method = "euclidean")
head(distance_shipmatrix,10)## [1] 0.6604909 3.1443541 2.6673462 2.1931554 1.5144456 3.0336744 3.6936620
## [8] 3.4085622 2.8940633 1.3485489Menentukan Jumlah Klaster Optimal
ship_ward <- hclust(distance_shipmatrix, method = "ward.D2")
ship_ward##
## Call:
## hclust(d = distance_shipmatrix, method = "ward.D2")
##
## Cluster method : ward.D2
## Distance : euclidean
## Number of objects: 500Dendogram dengan K=3
fviz_dend(ship_ward, k = 3,
rect = TRUE,
rect_fill = TRUE,
main = "Dendrogram Metode Ward's")## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Hasil Klaster
clusters <- cutree(ship_ward, k = 3)
table(clusters)## clusters
## 1 2 3
## 113 176 211Komponen Hasil
names(ship_ward)## [1] "merge" "height" "order" "labels" "method"
## [6] "call" "dist.method"Pada fungsi hclust, beberapa komponen penting yang dihasilkan antara lain:
- merge adalah matriks yang menunjukkan bagaimana klaster digabungkan pada setiap langkah.
- height adalah vektor yang menunjukkan jarak pada setiap langkah penggabungan klaster.
- order adalah urutan objek dalam dendrogram.
- labels adalah label asli dari data yang digunakan.
- method & dist.method adalah metode penggabungan yang digunakan (dalam hal ini “ward.D2”).
- call merupakan panggilan fungsi yang digunakan untuk menghasilkan objek hclust.
head(ship_ward$merge,10)## [,1] [,2]
## [1,] -10 -134
## [2,] -59 -130
## [3,] -239 -455
## [4,] -29 -489
## [5,] -331 -337
## [6,] -348 -403
## [7,] -65 -252
## [8,] -57 -263
## [9,] -127 -374
## [10,] -37 -388head(ship_ward$height,20)## [1] 0.1035575 0.1337469 0.1679356 0.1698926 0.1942484 0.1958658 0.2079944
## [8] 0.2081150 0.2136431 0.2148049 0.2218037 0.2266441 0.2475158 0.2477706
## [15] 0.2494671 0.2501606 0.2511085 0.2522182 0.2530802 0.2593598cluster_hclust <- cutree(ship_ward, k = 3)
data_with_clusters <- dataship %>%
mutate(Cluster = as.factor(cluster_hclust))
cluster_profiles <- data_with_clusters %>%
group_by(Cluster) %>%
summarise(across(where(is.numeric), ~mean(.x, na.rm = TRUE)))
print(cluster_profiles)## # A tibble: 3 × 5
## Cluster Speed_Over_Ground_knots Engine_Power_kW Distance_Traveled_nm
## <fct> <dbl> <dbl> <dbl>
## 1 1 13.3 2198. 1048.
## 2 2 19.9 1485. 1045.
## 3 3 18.1 1643. 945.
## # ℹ 1 more variable: Draft_meters <dbl>Evaluasi Klaster
Terdapat beberapa metrik evaluasi klaster yang dapat digunakan untuk menilai kualitas hasil clustering: - Silhouette Score: Nilai berkisar dari -1 hingga 1, di mana nilai yang lebih tinggi menunjukkan klaster yang lebih baik. - Dunn Index: Nilai yang lebih tinggi menunjukkan klaster yang lebih baik. - Calinski-Harabasz Index: Nilai yang lebih tinggi menunjukkan klaster yang lebih baik.
Silhouette Score
library(cluster)
silhouette_values <- silhouette(cluster_hclust, distance_shipmatrix)
avg_silhouette <- mean(silhouette_values[, 3])
cat("Average Silhouette Score:", avg_silhouette, "\n")## Average Silhouette Score: 0.1459892Dunn Index
library(clusterCrit)## Warning: package 'clusterCrit' was built under R version 4.4.3dunn_index <- intCriteria(as.matrix(dataship_scaled),
as.integer(cluster_hclust),
c("Dunn"))$dunn
cat("Dunn Index:", dunn_index, "\n")## Dunn Index: 0.09443172Calinski-Harabasz Index
ch_index <- intCriteria(as.matrix(dataship_scaled),
as.integer(cluster_hclust),
c("Calinski_Harabasz"))$calinski_harabasz
cat("Calinski-Harabasz Index:", ch_index, "\n")## Calinski-Harabasz Index: 88.26635Karakteristik Klaster dengan Analisis Profil
dataship_clustered <- dataship %>%
mutate(Cluster = as.factor(cluster_hclust))
# Melihat profil rata-rata setiap klaster
cluster_profiles <- dataship_clustered %>%
group_by(Cluster) %>%
summarise(across(where(is.numeric), ~mean(.x, na.rm = TRUE)))
print(cluster_profiles)## # A tibble: 3 × 5
## Cluster Speed_Over_Ground_knots Engine_Power_kW Distance_Traveled_nm
## <fct> <dbl> <dbl> <dbl>
## 1 1 13.3 2198. 1048.
## 2 2 19.9 1485. 1045.
## 3 3 18.1 1643. 945.
## # ℹ 1 more variable: Draft_meters <dbl>Visualisasi Klaster
Visualisasi Klaster pada Dua Dimensi dengan PCA
pca_result <- prcomp(dataship_scaled)
pca_data <- data.frame(pca_result$x, Cluster = as.factor(cluster_hclust))
ggplot(pca_data, aes(x = PC1, y = PC2, color = Cluster)) +
geom_point(size = 2) +
labs(title = "Visualisasi Cluster menggunakan PCA",
x = "Principal Component 1",
y = "Principal Component 2") +
theme_minimal()
Berdasarkan visualisasi PCA, kita dapat mengamati bahwa klaster-klaster
yang terbentuk memiliki pemisahan yang cukup baik tetapi masih ada
beberapa tumpang tindih.
Visualisasi dengan Heat Map
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithslibrary(ggplot2)
dataship_num <- dataship_clustered[, sapply(dataship_clustered, is.numeric)]
dataship_num$Cluster <- dataship_clustered$Cluster
dataship_melted <- melt(dataship_num, id.vars = "Cluster")
ggplot(dataship_melted, aes(x = variable, y = as.factor(Cluster), fill = value)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "blue") +
labs(title = "Heatmap Karakteristik Cluster",
x = "Fitur",
y = "Cluster") +
theme_minimal()
Klaster 1 berwarna paling gelap di Speed_Over_Ground_kn dan
Engine_Power_kW menunjukkan bahwa kapal cepat dengan tenaga mesin besar.
Klaster 2 memiliki warna agak terang di semua fitur menunjukkan kapal
dengan kecepatan dan tenaga mesin sedang. Klaster 3 berwarna paling
terang (nilai rendah di semua fitur) menunjukkan Kapal lambat, mesin
kecil, mungkin kapal kecil atau kapal nelayan tradisional.
K-Means Clustering
Beberapa metode yang umum digunakan untuk menentukan k antara lain: - Elbow Method - Silhouette Method
Elbow Method
fviz_nbclust(dataship_scaled, kmeans, method = "wss") +
labs(title = "Elbow Method for Optimal k")
#### Silhouette Method
fviz_nbclust(dataship_scaled, kmeans, method = "silhouette") +
labs(title = "Silhouette Method for Optimal k")
Didapat bahwa jumlah klaster yang optimum adalah k=8.
Hasil K-Means
set.seed(123) # Untuk reproduktifitas
k <- 8 # Jumlah klaster optimal
kmeans_result <- kmeans(dataship_scaled, centers = k, nstart = 25)
table(kmeans_result$cluster)##
## 1 2 3 4 5 6 7 8
## 50 61 72 53 60 73 76 55names(kmeans_result)## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"kmeans_result$centers## Speed_Over_Ground_knots Engine_Power_kW Distance_Traveled_nm Draft_meters
## 1 0.9784240 0.6040468 0.9228097 -0.8848299
## 2 0.8245809 -0.7013530 0.5212000 0.8972485
## 3 0.6402568 0.8131487 -0.6393102 0.7392474
## 4 -0.7359530 -1.0129491 1.0232342 -0.5283593
## 5 -0.7700610 0.8291439 -0.6620850 -0.8288240
## 6 -0.8629419 0.8561343 0.9087239 0.5637013
## 7 0.7120312 -0.7854791 -0.7640210 -0.8492746
## 8 -0.9314467 -0.8150953 -0.9941984 0.6801942kmeans_result$tot.withinss## [1] 727.3713kmeans_result$size## [1] 50 61 72 53 60 73 76 55kmeans_result$iter## [1] 4kmeans_result$withinss## [1] 74.52186 92.17357 112.34410 76.00927 80.69313 115.71457 96.93215
## [8] 78.98265kmeans_result$betweenss## [1] 1268.629Evaluasi Klaster K-Means
distance_matrix <- dist(dataship_scaled)Silhouette Score
silhouette_values_kmeans <- silhouette(kmeans_result$cluster, distance_matrix)
avg_silhouette_kmeans <- mean(silhouette_values_kmeans[, 3])
cat("Average Silhouette Score (K-Means):", avg_silhouette_kmeans, "\n")## Average Silhouette Score (K-Means): 0.2509852Dunn Index
dunn_index_kmeans <- intCriteria(
as.matrix(dataship_scaled),
as.integer(kmeans_result$cluster),
c("Dunn")
)$dunn
cat("Dunn Index (K-Means):", dunn_index_kmeans, "\n")## Dunn Index (K-Means): 0.07520343Calinski-Harabaz Index
ch_index_kmeans <- intCriteria(
as.matrix(dataship_scaled),
as.integer(kmeans_result$cluster),
c("Calinski_Harabasz")
)$calinski_harabasz
cat("Calinski-Harabasz Index (K-Means):", ch_index_kmeans, "\n")## Calinski-Harabasz Index (K-Means): 122.5873Karakteristik Klaster K-Means
data_kmeans_clustered <- dataship %>%
mutate(Cluster = as.factor(kmeans_result$cluster))
# Melihat profil rata-rata setiap klaster
cluster_profiles_kmeans <- data_kmeans_clustered %>%
group_by(Cluster) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
print(cluster_profiles_kmeans)## # A tibble: 8 × 5
## Cluster Speed_Over_Ground_knots Engine_Power_kW Distance_Traveled_nm
## <fct> <dbl> <dbl> <dbl>
## 1 1 21.9 2135. 1524.
## 2 2 21.2 1222. 1298.
## 3 3 20.4 2282. 643.
## 4 4 14.4 1004. 1581.
## 5 5 14.3 2293. 630.
## 6 6 13.9 2312. 1516.
## 7 7 20.7 1163. 572.
## 8 8 13.6 1143. 443.
## # ℹ 1 more variable: Draft_meters <dbl>Visualisasi Klaster K-Means
Visualisasi Klaster pada Dua Dimensi dengan PCA
fviz_cluster(kmeans_result, data = dataship_scaled,
geom = "point",
ellipse.type = "convex",
palette = "jco",
ggtheme = theme_minimal(),
main = "Visualisasi Cluster K-Means")
Terlihat bahwa masing-masing klaster memiliki area yang relatif
terpisah, meskipun terdapat beberapa tumpang tindih di batas antar
klaster. Hal ini menunjukkan bahwa algoritma K-Means berhasil
mengelompokkan data berdasarkan kesamaan karakteristik utama, namun
beberapa klaster memiliki karakteristik yang cukup mirip satu sama
lain.
Visualisasi dengan Heat Map
library(reshape2)
library(ggplot2)
data_num_kmeans <- data_kmeans_clustered[, sapply(data_kmeans_clustered, is.numeric)]
data_num_kmeans$Cluster <- data_kmeans_clustered$Cluster
data_melted_kmeans <- melt(data_num_kmeans, id.vars = "Cluster")
ggplot(data_melted_kmeans, aes(x = variable, y = as.factor(Cluster), fill = value)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
labs(title = "Heatmap Karakteristik Klaster K-Means",
x = "Fitur",
y = "Klaster") +
theme_minimal()
Berdasarkan heatmap untuk hasil klasterisasi K-Means, kita dapat melihat
pola karakteristik yang berbeda antar klaster berdasarkan empat fitur
utama, yaitu Speed_Over_Ground_kn, Engine_Power_kW,
Distance_Traveled_nm, dan Draft_meters. Warna merah yang lebih gelap
menunjukkan nilai rata-rata yang lebih tinggi pada fitur tersebut dalam
klaster tertentu.
Klaster 6 dan Klaster 8 menunjukkan warna yang lebih gelap pada fitur Engine_Power_kW dan Speed_Over_Ground_kn, yang mengindikasikan bahwa kedua klaster ini memiliki kapal dengan tenaga mesin dan kecepatan yang relatif tinggi.
Klaster 2 dan Klaster 4 tampak memiliki warna yang lebih terang pada sebagian besar fitur, menandakan bahwa kapal dalam kelompok ini cenderung memiliki ukuran dan performa yang lebih rendah.
Klaster 3 dan Klaster 5 memperlihatkan intensitas sedang pada hampir semua fitur, yang berarti memiliki karakteristik menengah antara kelompok cepat dan lambat.
Perbandingan Hasil Metode Ward’ dan K-Means
comparison_table <- data.frame(
Method = c("Hierarchical (Ward's)", "K-Means"),
Silhouette_Score = c(avg_silhouette, avg_silhouette_kmeans),
Dunn_Index = c(dunn_index, dunn_index_kmeans),
Calinski_Harabasz_Index = c(ch_index, ch_index_kmeans)
)
print(comparison_table)## Method Silhouette_Score Dunn_Index Calinski_Harabasz_Index
## 1 Hierarchical (Ward's) 0.1459892 0.09443172 88.26635
## 2 K-Means 0.2509852 0.07520343 122.58729Berdasarkan tabel perbandingan di atas: - Silhouette Score, metode K-Means menunjukkan nilai yang lebih tinggi (0.2509852) dibandingkan metode Ward (0.1459892). Hal ini mengindikasikan bahwa hasil klasterisasi K-Means memiliki pemisahan antar-klaster yang lebih baik dan objek-objek dalam satu klaster lebih homogen.
Calinski-Harabasz Index, metode K-Means juga memiliki nilai yang lebih tinggi (122.58729) dibandingkan metode Ward (88.26635). Nilai yang lebih tinggi pada indeks ini menunjukkan bahwa K-Means menghasilkan klaster yang lebih kompak di dalam kelompok dan lebih terpisah antar kelompok.
Dunn Index, metode Hierarchical (Ward’s) sedikit lebih unggul (0.09443172) dibandingkan K-Means (0.07520343). Ini berarti bahwa dalam hal jarak minimum antar klaster relatif terhadap diameter klaster, metode Ward memiliki pemisahan klaster yang sedikit lebih baik.
Kesimpulan
Secara keseluruhan, dapat disimpulkan bahwa metode K-Means cenderung memberikan hasil klasterisasi yang lebih baik secara umum, terutama dalam hal kekompakan dan pemisahan antar klaster berdasarkan nilai Silhouette Score dan Calinski-Harabasz Index.