Clustering
Arya Bintang Fauzildan
2026-04-05
Sumber Dataset -> https://archive.ics.uci.edu/dataset/1186/paddy+dataset
Agrikultur mencakup satu per tiga dataran bumi dan vital untuk produksi pangan. Beras contohnya, tumbuh dari benih padi yang mengisi perut hampir setengah dari populasi global. Untuk memenuhi kenaikan permintaan pangan, studi ini berfokus pada peningkatan produksi beras menggunakan Machine Learning (ML) untuk memprediksi faktor yang memengaruhi pertumbuhan padi.
Deskripsi Variabel: Hectares = Luas lahan pertanian Agriblock = Area pertanian Variety = Varietas atau jenis padi Soil.Types = Jenis Tanah Seedrate.in.Kg. = Jumlah benih dalam kg
LP_Mainfield.in.Tonnes. = Produksi padi di lahan utama Nursery = Persemaian padi Nursery.area..Cents. = Luas area persemaian LP_nurseryare.in.Tonnes. = Produksi dari area persemaian
DAP_20days = Pupuk DAP yang diberikan pada 20 hari Weed28D_thiobencarb = Herbisida pada 28 hari Urea_40Days = Pupuk urea pada 40 hari Potassh_50Days = Potassium / pupuk kalium pada 50 hari Micronutrients_70Days = Pupuk mikro nutrien pada 70 hari Pest_60Day.in.ml. = Pestisida pada 60 hari
Rain : hujan, AI : Artificial Irrigation X30DRain..in.mm. = Curah hujan hari 1-30 X30DAI.in.mm. = Irigasi hari 1-30 etc
Min.temp_D1_D30 = Suhu minimum hari 1-30 Max.temp_D1_D30 = suhu maks hari 1-30 etc
Inst.Wind.Speed_D1_D30.in.Knots. = Kecepatan angin hari 1-30 Wind.Direction_D1_D30 = Arah Angin hari 1-30
Relative.Humidity_D1_30 = Kelembaban udara hari 1-30
Trash.in.bundles. = Jumlah limbah Paddy.yield.in.Kg. = Hasil panen didapat (Kg)
library(psych)library(corrplot)## corrplot 0.95 loadedlibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(factoextra)## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBadata <- read.csv("C:/Users/WIN 10/Downloads/paddy/paddydataset.csv")
head(data)## Hectares Agriblock Variety Soil.Types Seedrate.in.Kg.
## 1 6 Cuddalore CO_43 alluvial 150
## 2 6 Kurinjipadi ponmani clay 150
## 3 6 Panruti delux ponni alluvial 150
## 4 6 Kallakurichi CO_43 clay 150
## 5 6 Sankarapuram ponmani alluvial 150
## 6 6 Chinnasalem delux ponni alluvial 150
## LP_Mainfield.in.Tonnes. Nursery Nursery.area..Cents.
## 1 75 dry 120
## 2 75 wet 120
## 3 75 dry 120
## 4 75 wet 120
## 5 75 dry 120
## 6 75 wet 120
## LP_nurseryarea.in.Tonnes. DAP_20days Weed28D_thiobencarb Urea_40Days
## 1 6 240 12 162.78
## 2 6 240 12 162.78
## 3 6 240 12 162.78
## 4 6 240 12 162.78
## 5 6 240 12 162.78
## 6 6 240 12 162.78
## Potassh_50Days Micronutrients_70Days Pest_60Day.in.ml. X30DRain..in.mm.
## 1 62.28 90 3600 19.6
## 2 62.28 90 3600 19.6
## 3 62.28 90 3600 18.5
## 4 62.28 90 3600 18.5
## 5 62.28 90 3600 18.1
## 6 62.28 90 3600 18.1
## X30DAI.in.mm. X30_50DRain..in.mm. X30_50DAI.in.mm. X51_70DRain.in.mm.
## 1 20.4 187.2 270.8 167.0
## 2 20.4 187.2 270.8 167.0
## 3 21.5 185.2 272.8 165.3
## 4 21.5 185.2 272.8 165.3
## 5 21.9 185.6 272.4 166.1
## 6 21.9 185.6 272.4 166.1
## X51_70AI.in.mm. X71_105DRain.in.mm. X71_105DAI.in.mm. Min.temp_D1_D30
## 1 250.0 61.0 64.0 18.5
## 2 250.0 61.0 64.0 19.5
## 3 251.7 60.0 65.0 20.0
## 4 251.7 60.0 65.0 19.0
## 5 250.9 60.2 64.8 20.5
## 6 250.9 60.2 64.8 18.0
## Max.temp_D1_D30 Min.temp_D31_D60 Max.temp_D31_D60 Min.temp_D61_D90
## 1 34 16.0 30 15.5
## 2 34 18.5 35 17.0
## 3 35 18.0 30 17.5
## 4 33 17.0 32 16.5
## 5 32 17.5 28 18.0
## 6 31 15.5 34 15.0
## Max.temp_D61_D90 Min.temp_D91_D120 Max.temp_D91_D120
## 1 31.0 16.0 33.0
## 2 32.5 16.0 30.5
## 3 33.5 18.0 33.0
## 4 31.5 15.5 32.5
## 5 34.0 16.5 35.0
## 6 33.0 15.0 31.5
## Inst.Wind.Speed_D1_D30.in.Knots. Inst.Wind.Speed_D31_D60.in.Knots.
## 1 4 10
## 2 10 4
## 3 4 12
## 4 8 6
## 5 10 12
## 6 6 6
## Inst.Wind.Speed_D61_D90.in.Knots. Inst.Wind.Speed_D91_D120.in.Knots.
## 1 8 10
## 2 10 6
## 3 4 12
## 4 8 6
## 5 10 12
## 6 8 10
## Wind.Direction_D1_D30 Wind.Direction_D31_D60 Wind.Direction_D61_D90
## 1 SW W NNW
## 2 NW S SE
## 3 ENE NE NNE
## 4 W WNW SE
## 5 SSE W SW
## 6 E ENE NE
## Wind.Direction_D91_D120 Relative.Humidity_D1_D30 Relative.Humidity_D31_D60
## 1 WSW 72.0 78
## 2 SSE 64.6 85
## 3 W 85.0 96
## 4 S 88.5 95
## 5 NW 72.7 91
## 6 NNW 78.6 80
## Relative.Humidity_D61_D90 Relative.Humidity_D91_D120 Trash.in.bundles.
## 1 88 85 540
## 2 84 87 600
## 3 84 79 600
## 4 81 84 540
## 5 83 81 600
## 6 92 88 480
## Paddy.yield.in.Kg.
## 1 35028
## 2 35412
## 3 36300
## 4 35016
## 5 34044
## 6 36732colnames(data)## [1] "Hectares" "Agriblock"
## [3] "Variety" "Soil.Types"
## [5] "Seedrate.in.Kg." "LP_Mainfield.in.Tonnes."
## [7] "Nursery" "Nursery.area..Cents."
## [9] "LP_nurseryarea.in.Tonnes." "DAP_20days"
## [11] "Weed28D_thiobencarb" "Urea_40Days"
## [13] "Potassh_50Days" "Micronutrients_70Days"
## [15] "Pest_60Day.in.ml." "X30DRain..in.mm."
## [17] "X30DAI.in.mm." "X30_50DRain..in.mm."
## [19] "X30_50DAI.in.mm." "X51_70DRain.in.mm."
## [21] "X51_70AI.in.mm." "X71_105DRain.in.mm."
## [23] "X71_105DAI.in.mm." "Min.temp_D1_D30"
## [25] "Max.temp_D1_D30" "Min.temp_D31_D60"
## [27] "Max.temp_D31_D60" "Min.temp_D61_D90"
## [29] "Max.temp_D61_D90" "Min.temp_D91_D120"
## [31] "Max.temp_D91_D120" "Inst.Wind.Speed_D1_D30.in.Knots."
## [33] "Inst.Wind.Speed_D31_D60.in.Knots." "Inst.Wind.Speed_D61_D90.in.Knots."
## [35] "Inst.Wind.Speed_D91_D120.in.Knots." "Wind.Direction_D1_D30"
## [37] "Wind.Direction_D31_D60" "Wind.Direction_D61_D90"
## [39] "Wind.Direction_D91_D120" "Relative.Humidity_D1_D30"
## [41] "Relative.Humidity_D31_D60" "Relative.Humidity_D61_D90"
## [43] "Relative.Humidity_D91_D120" "Trash.in.bundles."
## [45] "Paddy.yield.in.Kg."str(data)## 'data.frame': 2789 obs. of 45 variables:
## $ Hectares : int 6 6 6 6 6 6 6 6 6 6 ...
## $ Agriblock : chr "Cuddalore" "Kurinjipadi" "Panruti" "Kallakurichi" ...
## $ Variety : chr "CO_43" "ponmani" "delux ponni" "CO_43" ...
## $ Soil.Types : chr "alluvial" "clay" "alluvial" "clay" ...
## $ Seedrate.in.Kg. : int 150 150 150 150 150 150 150 150 150 150 ...
## $ LP_Mainfield.in.Tonnes. : num 75 75 75 75 75 75 75 75 75 75 ...
## $ Nursery : chr "dry" "wet" "dry" "wet" ...
## $ Nursery.area..Cents. : int 120 120 120 120 120 120 120 120 120 120 ...
## $ LP_nurseryarea.in.Tonnes. : int 6 6 6 6 6 6 6 6 6 6 ...
## $ DAP_20days : int 240 240 240 240 240 240 240 240 240 240 ...
## $ Weed28D_thiobencarb : int 12 12 12 12 12 12 12 12 12 12 ...
## $ Urea_40Days : num 163 163 163 163 163 ...
## $ Potassh_50Days : num 62.3 62.3 62.3 62.3 62.3 ...
## $ Micronutrients_70Days : int 90 90 90 90 90 90 90 90 90 90 ...
## $ Pest_60Day.in.ml. : int 3600 3600 3600 3600 3600 3600 3600 3600 3600 3600 ...
## $ X30DRain..in.mm. : num 19.6 19.6 18.5 18.5 18.1 18.1 19.6 19.6 18.5 18.5 ...
## $ X30DAI.in.mm. : num 20.4 20.4 21.5 21.5 21.9 21.9 20.4 20.4 21.5 21.5 ...
## $ X30_50DRain..in.mm. : num 187 187 185 185 186 ...
## $ X30_50DAI.in.mm. : num 271 271 273 273 272 ...
## $ X51_70DRain.in.mm. : num 167 167 165 165 166 ...
## $ X51_70AI.in.mm. : num 250 250 252 252 251 ...
## $ X71_105DRain.in.mm. : num 61 61 60 60 60.2 60.2 61 61 60 60 ...
## $ X71_105DAI.in.mm. : num 64 64 65 65 64.8 64.8 64 64 65 65 ...
## $ Min.temp_D1_D30 : num 18.5 19.5 20 19 20.5 18 18.5 19.5 20 19 ...
## $ Max.temp_D1_D30 : int 34 34 35 33 32 31 34 34 35 33 ...
## $ Min.temp_D31_D60 : num 16 18.5 18 17 17.5 15.5 16 18.5 18 17 ...
## $ Max.temp_D31_D60 : int 30 35 30 32 28 34 30 35 30 32 ...
## $ Min.temp_D61_D90 : num 15.5 17 17.5 16.5 18 15 15.5 17 17.5 16.5 ...
## $ Max.temp_D61_D90 : num 31 32.5 33.5 31.5 34 33 31 32.5 33.5 31.5 ...
## $ Min.temp_D91_D120 : num 16 16 18 15.5 16.5 15 16 16 18 15.5 ...
## $ Max.temp_D91_D120 : num 33 30.5 33 32.5 35 31.5 33 30.5 33 32.5 ...
## $ Inst.Wind.Speed_D1_D30.in.Knots. : int 4 10 4 8 10 6 4 10 4 8 ...
## $ Inst.Wind.Speed_D31_D60.in.Knots. : int 10 4 12 6 12 6 10 4 12 6 ...
## $ Inst.Wind.Speed_D61_D90.in.Knots. : int 8 10 4 8 10 8 8 10 4 8 ...
## $ Inst.Wind.Speed_D91_D120.in.Knots.: int 10 6 12 6 12 10 10 6 12 6 ...
## $ Wind.Direction_D1_D30 : chr "SW" "NW" "ENE" "W" ...
## $ Wind.Direction_D31_D60 : chr "W" "S" "NE" "WNW" ...
## $ Wind.Direction_D61_D90 : chr "NNW" "SE" "NNE" "SE" ...
## $ Wind.Direction_D91_D120 : chr "WSW" "SSE" "W" "S" ...
## $ Relative.Humidity_D1_D30 : num 72 64.6 85 88.5 72.7 78.6 72 64.6 85 88.5 ...
## $ Relative.Humidity_D31_D60 : int 78 85 96 95 91 80 78 85 96 95 ...
## $ Relative.Humidity_D61_D90 : int 88 84 84 81 83 92 88 84 84 81 ...
## $ Relative.Humidity_D91_D120 : int 85 87 79 84 81 88 85 87 79 84 ...
## $ Trash.in.bundles. : int 540 600 600 540 600 480 540 480 600 540 ...
## $ Paddy.yield.in.Kg. : int 35028 35412 36300 35016 34044 36732 33162 36690 35310 32460 ...data_num <- data[, sapply(data, is.numeric)]colnames(data_num)## [1] "Hectares" "Seedrate.in.Kg."
## [3] "LP_Mainfield.in.Tonnes." "Nursery.area..Cents."
## [5] "LP_nurseryarea.in.Tonnes." "DAP_20days"
## [7] "Weed28D_thiobencarb" "Urea_40Days"
## [9] "Potassh_50Days" "Micronutrients_70Days"
## [11] "Pest_60Day.in.ml." "X30DRain..in.mm."
## [13] "X30DAI.in.mm." "X30_50DRain..in.mm."
## [15] "X30_50DAI.in.mm." "X51_70DRain.in.mm."
## [17] "X51_70AI.in.mm." "X71_105DRain.in.mm."
## [19] "X71_105DAI.in.mm." "Min.temp_D1_D30"
## [21] "Max.temp_D1_D30" "Min.temp_D31_D60"
## [23] "Max.temp_D31_D60" "Min.temp_D61_D90"
## [25] "Max.temp_D61_D90" "Min.temp_D91_D120"
## [27] "Max.temp_D91_D120" "Inst.Wind.Speed_D1_D30.in.Knots."
## [29] "Inst.Wind.Speed_D31_D60.in.Knots." "Inst.Wind.Speed_D61_D90.in.Knots."
## [31] "Inst.Wind.Speed_D91_D120.in.Knots." "Relative.Humidity_D1_D30"
## [33] "Relative.Humidity_D31_D60" "Relative.Humidity_D61_D90"
## [35] "Relative.Humidity_D91_D120" "Trash.in.bundles."
## [37] "Paddy.yield.in.Kg."str(data_num)## 'data.frame': 2789 obs. of 37 variables:
## $ Hectares : int 6 6 6 6 6 6 6 6 6 6 ...
## $ Seedrate.in.Kg. : int 150 150 150 150 150 150 150 150 150 150 ...
## $ LP_Mainfield.in.Tonnes. : num 75 75 75 75 75 75 75 75 75 75 ...
## $ Nursery.area..Cents. : int 120 120 120 120 120 120 120 120 120 120 ...
## $ LP_nurseryarea.in.Tonnes. : int 6 6 6 6 6 6 6 6 6 6 ...
## $ DAP_20days : int 240 240 240 240 240 240 240 240 240 240 ...
## $ Weed28D_thiobencarb : int 12 12 12 12 12 12 12 12 12 12 ...
## $ Urea_40Days : num 163 163 163 163 163 ...
## $ Potassh_50Days : num 62.3 62.3 62.3 62.3 62.3 ...
## $ Micronutrients_70Days : int 90 90 90 90 90 90 90 90 90 90 ...
## $ Pest_60Day.in.ml. : int 3600 3600 3600 3600 3600 3600 3600 3600 3600 3600 ...
## $ X30DRain..in.mm. : num 19.6 19.6 18.5 18.5 18.1 18.1 19.6 19.6 18.5 18.5 ...
## $ X30DAI.in.mm. : num 20.4 20.4 21.5 21.5 21.9 21.9 20.4 20.4 21.5 21.5 ...
## $ X30_50DRain..in.mm. : num 187 187 185 185 186 ...
## $ X30_50DAI.in.mm. : num 271 271 273 273 272 ...
## $ X51_70DRain.in.mm. : num 167 167 165 165 166 ...
## $ X51_70AI.in.mm. : num 250 250 252 252 251 ...
## $ X71_105DRain.in.mm. : num 61 61 60 60 60.2 60.2 61 61 60 60 ...
## $ X71_105DAI.in.mm. : num 64 64 65 65 64.8 64.8 64 64 65 65 ...
## $ Min.temp_D1_D30 : num 18.5 19.5 20 19 20.5 18 18.5 19.5 20 19 ...
## $ Max.temp_D1_D30 : int 34 34 35 33 32 31 34 34 35 33 ...
## $ Min.temp_D31_D60 : num 16 18.5 18 17 17.5 15.5 16 18.5 18 17 ...
## $ Max.temp_D31_D60 : int 30 35 30 32 28 34 30 35 30 32 ...
## $ Min.temp_D61_D90 : num 15.5 17 17.5 16.5 18 15 15.5 17 17.5 16.5 ...
## $ Max.temp_D61_D90 : num 31 32.5 33.5 31.5 34 33 31 32.5 33.5 31.5 ...
## $ Min.temp_D91_D120 : num 16 16 18 15.5 16.5 15 16 16 18 15.5 ...
## $ Max.temp_D91_D120 : num 33 30.5 33 32.5 35 31.5 33 30.5 33 32.5 ...
## $ Inst.Wind.Speed_D1_D30.in.Knots. : int 4 10 4 8 10 6 4 10 4 8 ...
## $ Inst.Wind.Speed_D31_D60.in.Knots. : int 10 4 12 6 12 6 10 4 12 6 ...
## $ Inst.Wind.Speed_D61_D90.in.Knots. : int 8 10 4 8 10 8 8 10 4 8 ...
## $ Inst.Wind.Speed_D91_D120.in.Knots.: int 10 6 12 6 12 10 10 6 12 6 ...
## $ Relative.Humidity_D1_D30 : num 72 64.6 85 88.5 72.7 78.6 72 64.6 85 88.5 ...
## $ Relative.Humidity_D31_D60 : int 78 85 96 95 91 80 78 85 96 95 ...
## $ Relative.Humidity_D61_D90 : int 88 84 84 81 83 92 88 84 84 81 ...
## $ Relative.Humidity_D91_D120 : int 85 87 79 84 81 88 85 87 79 84 ...
## $ Trash.in.bundles. : int 540 600 600 540 600 480 540 480 600 540 ...
## $ Paddy.yield.in.Kg. : int 35028 35412 36300 35016 34044 36732 33162 36690 35310 32460 ...Cek missing value
sum(is.na(data_num))## [1] 0EDA sebelum preprocessing
describe(data_num)## vars n mean sd median trimmed
## Hectares 1 2789 3.72 1.44 4.00 3.79
## Seedrate.in.Kg. 2 2789 92.94 35.94 100.00 94.85
## LP_Mainfield.in.Tonnes. 3 2789 46.47 17.97 50.00 47.42
## Nursery.area..Cents. 4 2789 74.35 28.76 80.00 75.88
## LP_nurseryarea.in.Tonnes. 5 2789 3.72 1.44 4.00 3.79
## DAP_20days 6 2789 148.70 57.51 160.00 151.76
## Weed28D_thiobencarb 7 2789 7.43 2.88 8.00 7.59
## Urea_40Days 8 2789 100.85 39.01 108.52 102.93
## Potassh_50Days 9 2789 38.59 14.92 41.52 39.38
## Micronutrients_70Days 10 2789 55.76 21.57 60.00 56.91
## Pest_60Day.in.ml. 11 2789 2230.48 862.67 2400.00 2276.40
## X30DRain..in.mm. 12 2789 18.72 0.64 18.50 18.69
## X30DAI.in.mm. 13 2789 21.28 0.64 21.50 21.31
## X30_50DRain..in.mm. 14 2789 186.02 0.86 185.60 185.97
## X30_50DAI.in.mm. 15 2789 271.98 0.86 272.40 272.03
## X51_70DRain.in.mm. 16 2789 166.16 0.68 166.10 166.16
## X51_70AI.in.mm. 17 2789 250.84 0.68 250.90 250.84
## X71_105DRain.in.mm. 18 2789 60.41 0.43 60.20 60.39
## X71_105DAI.in.mm. 19 2789 64.59 0.43 64.80 64.61
## Min.temp_D1_D30 20 2789 19.34 0.87 19.50 19.36
## Max.temp_D1_D30 21 2789 33.13 1.32 33.00 33.16
## Min.temp_D31_D60 22 2789 17.14 1.04 17.50 17.18
## Max.temp_D31_D60 23 2789 31.32 2.53 30.00 31.28
## Min.temp_D61_D90 24 2789 16.68 1.07 17.00 16.73
## Max.temp_D61_D90 25 2789 32.66 1.08 33.00 32.70
## Min.temp_D91_D120 26 2789 16.19 0.90 16.00 16.12
## Max.temp_D91_D120 27 2789 32.70 1.50 33.00 32.69
## Inst.Wind.Speed_D1_D30.in.Knots. 28 2789 7.23 2.57 8.00 7.29
## Inst.Wind.Speed_D31_D60.in.Knots. 29 2789 8.51 3.20 10.00 8.64
## Inst.Wind.Speed_D61_D90.in.Knots. 30 2789 8.17 1.99 8.00 8.46
## Inst.Wind.Speed_D91_D120.in.Knots. 31 2789 9.45 2.52 10.00 9.56
## Relative.Humidity_D1_D30 32 2789 76.26 8.00 72.70 76.18
## Relative.Humidity_D31_D60 33 2789 87.59 6.78 91.00 87.74
## Relative.Humidity_D61_D90 34 2789 85.16 3.49 84.00 84.83
## Relative.Humidity_D91_D120 35 2789 83.86 3.13 84.00 83.95
## Trash.in.bundles. 36 2789 335.51 134.31 360.00 340.24
## Paddy.yield.in.Kg. 37 2789 22517.73 9199.66 24636.00 22974.44
## mad min max range skew
## Hectares 1.48 1.00 6.00 5.00 -0.37
## Seedrate.in.Kg. 37.06 25.00 150.00 125.00 -0.37
## LP_Mainfield.in.Tonnes. 18.53 12.50 75.00 62.50 -0.37
## Nursery.area..Cents. 29.65 20.00 120.00 100.00 -0.37
## LP_nurseryarea.in.Tonnes. 1.48 1.00 6.00 5.00 -0.37
## DAP_20days 59.30 40.00 240.00 200.00 -0.37
## Weed28D_thiobencarb 2.97 2.00 12.00 10.00 -0.37
## Urea_40Days 40.22 27.13 162.78 135.65 -0.37
## Potassh_50Days 15.39 10.38 62.28 51.90 -0.37
## Micronutrients_70Days 22.24 15.00 90.00 75.00 -0.37
## Pest_60Day.in.ml. 889.56 600.00 3600.00 3000.00 -0.37
## X30DRain..in.mm. 0.59 18.10 19.60 1.50 0.50
## X30DAI.in.mm. 0.59 20.40 21.90 1.50 -0.50
## X30_50DRain..in.mm. 0.59 185.20 187.20 2.00 0.58
## X30_50DAI.in.mm. 0.59 270.80 272.80 2.00 -0.58
## X51_70DRain.in.mm. 1.19 165.30 167.00 1.70 0.02
## X51_70AI.in.mm. 1.19 250.00 251.70 1.70 -0.02
## X71_105DRain.in.mm. 0.30 60.00 61.00 1.00 0.58
## X71_105DAI.in.mm. 0.30 64.00 65.00 1.00 -0.58
## Min.temp_D1_D30 1.48 18.00 20.50 2.50 -0.10
## Max.temp_D1_D30 1.48 31.00 35.00 4.00 -0.22
## Min.temp_D31_D60 0.74 15.50 18.50 3.00 -0.31
## Max.temp_D31_D60 2.97 28.00 35.00 7.00 0.18
## Min.temp_D61_D90 1.48 15.00 18.00 3.00 -0.31
## Max.temp_D61_D90 1.48 31.00 34.00 3.00 -0.28
## Min.temp_D91_D120 0.74 15.00 18.00 3.00 0.83
## Max.temp_D91_D120 2.22 30.50 35.00 4.50 0.15
## Inst.Wind.Speed_D1_D30.in.Knots. 2.97 4.00 10.00 6.00 -0.15
## Inst.Wind.Speed_D31_D60.in.Knots. 2.97 4.00 12.00 8.00 -0.14
## Inst.Wind.Speed_D61_D90.in.Knots. 2.97 4.00 10.00 6.00 -1.10
## Inst.Wind.Speed_D91_D120.in.Knots. 2.97 6.00 12.00 6.00 -0.44
## Relative.Humidity_D1_D30 8.75 64.60 88.50 23.90 0.15
## Relative.Humidity_D31_D60 7.41 78.00 96.00 18.00 -0.16
## Relative.Humidity_D61_D90 1.48 81.00 92.00 11.00 0.88
## Relative.Humidity_D91_D120 4.45 79.00 88.00 9.00 -0.21
## Trash.in.bundles. 133.43 80.00 600.00 520.00 -0.21
## Paddy.yield.in.Kg. 10403.40 5410.00 38814.00 33404.00 -0.32
## kurtosis se
## Hectares -0.91 0.03
## Seedrate.in.Kg. -0.91 0.68
## LP_Mainfield.in.Tonnes. -0.91 0.34
## Nursery.area..Cents. -0.91 0.54
## LP_nurseryarea.in.Tonnes. -0.91 0.03
## DAP_20days -0.91 1.09
## Weed28D_thiobencarb -0.91 0.05
## Urea_40Days -0.91 0.74
## Potassh_50Days -0.91 0.28
## Micronutrients_70Days -0.91 0.41
## Pest_60Day.in.ml. -0.91 16.33
## X30DRain..in.mm. -1.52 0.01
## X30DAI.in.mm. -1.52 0.01
## X30_50DRain..in.mm. -1.51 0.02
## X30_50DAI.in.mm. -1.51 0.02
## X51_70DRain.in.mm. -1.44 0.01
## X51_70AI.in.mm. -1.44 0.01
## X71_105DRain.in.mm. -1.51 0.01
## X71_105DAI.in.mm. -1.51 0.01
## Min.temp_D1_D30 -1.30 0.02
## Max.temp_D1_D30 -1.19 0.02
## Min.temp_D31_D60 -1.24 0.02
## Max.temp_D31_D60 -1.38 0.05
## Min.temp_D61_D90 -1.30 0.02
## Max.temp_D61_D90 -1.32 0.02
## Min.temp_D91_D120 -0.02 0.02
## Max.temp_D91_D120 -0.91 0.03
## Inst.Wind.Speed_D1_D30.in.Knots. -1.67 0.05
## Inst.Wind.Speed_D31_D60.in.Knots. -1.68 0.06
## Inst.Wind.Speed_D61_D90.in.Knots. 0.20 0.04
## Inst.Wind.Speed_D91_D120.in.Knots. -1.47 0.05
## Relative.Humidity_D1_D30 -1.16 0.15
## Relative.Humidity_D31_D60 -1.53 0.13
## Relative.Humidity_D61_D90 -0.43 0.07
## Relative.Humidity_D91_D120 -1.34 0.06
## Trash.in.bundles. -0.83 2.54
## Paddy.yield.in.Kg. -1.07 174.20par(mar = c(10, 4, 4, 2))
boxplot(data_num, las = 2, cex.axis = 0.6)
par(mar = c(10, 4, 4, 2))
boxplot(data_num[, !names(data_num) %in% c("Paddy.yield.in.Kg.", "Pest_60Day.in.ml.", "Trash.in.bundles.")],las = 2, cex.axis = 0.6)
lahan_produksi <- data_num[, c(
"Hectares",
"Seedrate.in.Kg.",
"LP_Mainfield.in.Tonnes.",
"Nursery.area..Cents.",
"LP_nurseryarea.in.Tonnes."
)]
boxplot(lahan_produksi, las = 1, cex.axis = 0.5)
hasil_lahan <- data_num[, c(
"Trash.in.bundles.",
"Paddy.yield.in.Kg."
)]
boxplot(hasil_lahan, las = 1, cex.axis = 0.9)
pupuk <- data_num[, c(
"DAP_20days",
"Weed28D_thiobencarb",
"Urea_40Days",
"Potassh_50Days",
"Micronutrients_70Days",
"Pest_60Day.in.ml."
)]
boxplot(pupuk, las = 1, cex.axis = 0.5)
hujan_irigasi <- data_num[, c(
"X30DRain..in.mm.",
"X30DAI.in.mm.",
"X30_50DRain..in.mm.",
"X30_50DAI.in.mm.",
"X51_70DRain.in.mm.",
"X51_70AI.in.mm.",
"X71_105DRain.in.mm.",
"X71_105DAI.in.mm."
)]
boxplot(hujan_irigasi, las = 2, cex.axis = 0.5)
suhu <- data_num[, c(
"Min.temp_D1_D30",
"Max.temp_D1_D30",
"Min.temp_D31_D60",
"Max.temp_D31_D60",
"Min.temp_D61_D90",
"Max.temp_D61_D90",
"Min.temp_D91_D120",
"Max.temp_D91_D120"
)]
boxplot(suhu, las = 2, cex.axis = 0.5)
angin <- data_num[, c(
"Inst.Wind.Speed_D1_D30.in.Knots.",
"Inst.Wind.Speed_D31_D60.in.Knots.",
"Inst.Wind.Speed_D61_D90.in.Knots.",
"Inst.Wind.Speed_D91_D120.in.Knots."
)]
boxplot(angin, las = 1, cex.axis = 0.5)
kelembaban <- data_num[, c(
"Relative.Humidity_D1_D30",
"Relative.Humidity_D31_D60",
"Relative.Humidity_D61_D90",
"Relative.Humidity_D91_D120"
)]
boxplot(kelembaban, las = 1, cex.axis = 0.5)
layout(matrix(1:4, 2, 2))
for(i in colnames(lahan_produksi)) {
hist(lahan_produksi[[i]], main = i)
}
layout(matrix(1:4, 2, 2))
for(i in colnames(hasil_lahan)) {
hist(hasil_lahan[[i]], main = i)
}
layout(matrix(1:4, 2, 2))
for(i in colnames(pupuk)) {
hist(pupuk[[i]], main = i)
}
layout(matrix(1:4, 2, 2))
for(i in colnames(hujan_irigasi)) {
hist(hujan_irigasi[[i]], main = i)
}
layout(matrix(1:4, 2, 2))
for(i in colnames(suhu)) {
hist(suhu[[i]], main = i)
}
layout(matrix(1:4, 2, 2))
for(i in colnames(angin)) {
hist(angin[[i]], main = i)
}
layout(matrix(1:4, 2, 2))
for(i in colnames(kelembaban)) {
hist(kelembaban[[i]], main = i)
}
cor_matrix <- cor(data_num)
corrplot(cor_matrix,
method = "color",
tl.cex = 0.5,
tl.srt = 45)
detect_outlier <- function(x) {
Q1 <- quantile(x, 0.25, na.rm = TRUE)
Q3 <- quantile(x, 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
outliers <- x[x < lower_bound | x > upper_bound]
return(length(outliers))
}outlier_count <- sapply(data_num, detect_outlier)outlier_summary <- data.frame(
Variabel = names(outlier_count),
Jumlah_Outlier = outlier_count
)
outlier_summary## Variabel
## Hectares Hectares
## Seedrate.in.Kg. Seedrate.in.Kg.
## LP_Mainfield.in.Tonnes. LP_Mainfield.in.Tonnes.
## Nursery.area..Cents. Nursery.area..Cents.
## LP_nurseryarea.in.Tonnes. LP_nurseryarea.in.Tonnes.
## DAP_20days DAP_20days
## Weed28D_thiobencarb Weed28D_thiobencarb
## Urea_40Days Urea_40Days
## Potassh_50Days Potassh_50Days
## Micronutrients_70Days Micronutrients_70Days
## Pest_60Day.in.ml. Pest_60Day.in.ml.
## X30DRain..in.mm. X30DRain..in.mm.
## X30DAI.in.mm. X30DAI.in.mm.
## X30_50DRain..in.mm. X30_50DRain..in.mm.
## X30_50DAI.in.mm. X30_50DAI.in.mm.
## X51_70DRain.in.mm. X51_70DRain.in.mm.
## X51_70AI.in.mm. X51_70AI.in.mm.
## X71_105DRain.in.mm. X71_105DRain.in.mm.
## X71_105DAI.in.mm. X71_105DAI.in.mm.
## Min.temp_D1_D30 Min.temp_D1_D30
## Max.temp_D1_D30 Max.temp_D1_D30
## Min.temp_D31_D60 Min.temp_D31_D60
## Max.temp_D31_D60 Max.temp_D31_D60
## Min.temp_D61_D90 Min.temp_D61_D90
## Max.temp_D61_D90 Max.temp_D61_D90
## Min.temp_D91_D120 Min.temp_D91_D120
## Max.temp_D91_D120 Max.temp_D91_D120
## Inst.Wind.Speed_D1_D30.in.Knots. Inst.Wind.Speed_D1_D30.in.Knots.
## Inst.Wind.Speed_D31_D60.in.Knots. Inst.Wind.Speed_D31_D60.in.Knots.
## Inst.Wind.Speed_D61_D90.in.Knots. Inst.Wind.Speed_D61_D90.in.Knots.
## Inst.Wind.Speed_D91_D120.in.Knots. Inst.Wind.Speed_D91_D120.in.Knots.
## Relative.Humidity_D1_D30 Relative.Humidity_D1_D30
## Relative.Humidity_D31_D60 Relative.Humidity_D31_D60
## Relative.Humidity_D61_D90 Relative.Humidity_D61_D90
## Relative.Humidity_D91_D120 Relative.Humidity_D91_D120
## Trash.in.bundles. Trash.in.bundles.
## Paddy.yield.in.Kg. Paddy.yield.in.Kg.
## Jumlah_Outlier
## Hectares 0
## Seedrate.in.Kg. 0
## LP_Mainfield.in.Tonnes. 0
## Nursery.area..Cents. 0
## LP_nurseryarea.in.Tonnes. 0
## DAP_20days 0
## Weed28D_thiobencarb 0
## Urea_40Days 0
## Potassh_50Days 0
## Micronutrients_70Days 0
## Pest_60Day.in.ml. 0
## X30DRain..in.mm. 0
## X30DAI.in.mm. 0
## X30_50DRain..in.mm. 0
## X30_50DAI.in.mm. 0
## X51_70DRain.in.mm. 0
## X51_70AI.in.mm. 0
## X71_105DRain.in.mm. 0
## X71_105DAI.in.mm. 0
## Min.temp_D1_D30 0
## Max.temp_D1_D30 0
## Min.temp_D31_D60 0
## Max.temp_D31_D60 0
## Min.temp_D61_D90 0
## Max.temp_D61_D90 0
## Min.temp_D91_D120 0
## Max.temp_D91_D120 0
## Inst.Wind.Speed_D1_D30.in.Knots. 0
## Inst.Wind.Speed_D31_D60.in.Knots. 0
## Inst.Wind.Speed_D61_D90.in.Knots. 425
## Inst.Wind.Speed_D91_D120.in.Knots. 0
## Relative.Humidity_D1_D30 0
## Relative.Humidity_D31_D60 0
## Relative.Humidity_D61_D90 0
## Relative.Humidity_D91_D120 0
## Trash.in.bundles. 0
## Paddy.yield.in.Kg. 0data_scale <- scale(data_num)data_scale <- as.data.frame(data_scale)boxplot(data_scale, las = 2, cex.axis = 0.5)
pca <- prcomp(data_scale)
fviz_pca_ind(pca)
fviz_contrib(pca, choice = "var", axes = 1, top = 5)
fviz_contrib(pca, choice = "var", axes = 2, top = 5)
fviz_contrib(pca, choice = "var", axes = 3, top = 5)
fviz_contrib(pca, choice = "var", axes = 4, top = 5)
fviz_contrib(pca, choice = "var", axes = 5, top = 5)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 3.5963 3.3627 2.2549 1.81624 1.77159 1.08000 0.24715
## Proportion of Variance 0.3496 0.3056 0.1374 0.08916 0.08483 0.03152 0.00165
## Cumulative Proportion 0.3496 0.6552 0.7926 0.88175 0.96657 0.99810 0.99975
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.09665 5.312e-14 2.608e-14 1.583e-14 1.226e-14
## Proportion of Variance 0.00025 0.000e+00 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 1.00000 1.000e+00 1.000e+00 1.000e+00 1.000e+00
## PC13 PC14 PC15 PC16 PC17
## Standard deviation 9.287e-15 4.523e-15 1.908e-15 1.488e-15 1.306e-15
## Proportion of Variance 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
## PC18 PC19 PC20 PC21 PC22
## Standard deviation 1.026e-15 8.196e-16 6.652e-16 6.504e-16 3.451e-16
## Proportion of Variance 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
## PC23 PC24 PC25 PC26 PC27
## Standard deviation 3.451e-16 3.451e-16 3.451e-16 3.451e-16 3.451e-16
## Proportion of Variance 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
## PC28 PC29 PC30 PC31 PC32
## Standard deviation 3.451e-16 3.451e-16 3.451e-16 3.451e-16 3.451e-16
## Proportion of Variance 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
## PC33 PC34 PC35 PC36 PC37
## Standard deviation 3.451e-16 3.451e-16 3.242e-16 1.79e-16 8.544e-17
## Proportion of Variance 0.000e+00 0.000e+00 0.000e+00 0.00e+00 0.000e+00
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.00e+00 1.000e+00pca_data <- pca$x[, 1:5]dim(pca_data)## [1] 2789 5fviz_nbclust(pca_data, kmeans, method = "wss")
set.seed(123)
kmeans_result <- kmeans(pca_data, centers = 6, nstart = 25)
# Hasil cluster
kmeans_result$cluster## [1] 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4
## [38] 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4
## [75] 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5
## [112] 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6
## [149] 5 6 6 6 6 6 6 6 6 6 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5
## [186] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6
## [223] 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5
## [260] 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6
## [297] 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4
## [334] 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 6 6 6 6 6 6 6 6 5 5 5 5 5 4 4 4 4 4 4 4
## [371] 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 6 5 6 4
## [408] 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4
## [445] 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5
## [482] 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6
## [519] 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 6 6 6 6 6
## [556] 6 6 6 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5
## [593] 5 5 5 5 5 5 5 5 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5
## [630] 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6
## [667] 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4
## [704] 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4
## [741] 5 6 5 6 4 4 5 6 5 6 6 6 6 6 6 6 6 6 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [778] 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 6 5 6 4 4 5 6 5 6 4 4
## [815] 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5
## [852] 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6
## [889] 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5
## [926] 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 6 6 6 6 6 6 6 6 5 5 5 5
## [963] 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1000] 5 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6
## [1037] 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4
## [1074] 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4
## [1111] 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5
## [1148] 6 5 6 6 6 6 6 6 6 6 6 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5
## [1185] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5
## [1222] 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6
## [1259] 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5
## [1296] 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6
## [1333] 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 6 6 6 6 6 6 6 6 5 5 5 5 5 4 4 4 4 4 4
## [1370] 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 6 5 6
## [1407] 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4
## [1444] 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4
## [1481] 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5
## [1518] 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 6 6 6 6
## [1555] 6 6 6 6 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5
## [1592] 5 5 5 5 5 5 5 5 5 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6
## [1629] 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 5 6 5 6 4 4 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1
## [1666] 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2
## [1703] 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3
## [1740] 3 1 2 1 2 3 3 1 2 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1777] 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 1 2 1 2 3 3 1 2 1 2 3
## [1814] 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3
## [1851] 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1
## [1888] 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2
## [1925] 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 2 2 2 2 2 2 2 2 1 1 1
## [1962] 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1999] 1 1 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1
## [2036] 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2
## [2073] 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3
## [2110] 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3
## [2147] 1 2 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1
## [2184] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3
## [2221] 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1
## [2258] 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2
## [2295] 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1
## [2332] 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 3 3 3 3 3
## [2369] 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 1 2 1
## [2406] 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2
## [2443] 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3
## [2480] 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3
## [2517] 1 2 1 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2
## [2554] 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1
## [2591] 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2
## [2628] 3 3 1 2 1 2 3 3 1 2 1 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3
## [2665] 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1
## [2702] 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2
## [2739] 1 2 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 3 3 1 2 1 2 3 3 1 2 1 2 3 3 1 2 1 2
## [2776] 3 3 1 2 1 2 3 3 1 2 1 2 3 3fviz_cluster(kmeans_result, data = pca_data)
library(cluster)
kmedian_result <- pam(pca_data, k = 6)
# Hasil cluster
kmedian_result$cluster## [1] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1
## [38] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [75] 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [112] 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [149] 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5
## [186] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [223] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5
## [260] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [297] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1
## [334] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1 1 1
## [371] 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 3 4 5 6 1
## [408] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [445] 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [482] 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [519] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 6 6 6 6 4
## [556] 4 4 4 3 3 3 3 3 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5
## [593] 5 5 5 5 5 5 5 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5
## [630] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [667] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1
## [704] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [741] 3 4 5 6 1 2 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1 1 1 2 2 2 2 2 2 2
## [778] 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [815] 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [852] 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [889] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5
## [926] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3
## [963] 3 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1000] 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [1037] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1
## [1074] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [1111] 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [1148] 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5
## [1185] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [1222] 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [1259] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5
## [1296] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [1333] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1 1
## [1370] 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 3 4 5 6
## [1407] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1
## [1444] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [1481] 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [1518] 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 6 6 6 6
## [1555] 4 4 4 4 3 3 3 3 3 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5
## [1592] 5 5 5 5 5 5 5 5 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [1629] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5
## [1666] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [1703] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1
## [1740] 2 3 4 5 6 1 2 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1 1 1 2 2 2 2 2 2
## [1777] 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 3 4 5 6 1 2 3 4 5 6 1
## [1814] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [1851] 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [1888] 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [1925] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3
## [1962] 3 3 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1999] 5 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5
## [2036] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [2073] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1
## [2110] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [2147] 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5
## [2184] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [2221] 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [2258] 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [2295] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5
## [2332] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1
## [2369] 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 3 4 5
## [2406] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [2443] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1
## [2480] 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [2517] 3 4 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [2554] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5
## [2591] 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [2628] 1 2 3 4 5 6 1 2 3 4 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2
## [2665] 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3
## [2702] 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4
## [2739] 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [2776] 1 2 3 4 5 6 1 2 3 4 5 6 1 2# Visualisasi
fviz_cluster(kmedian_result, data = pca_data)
library(e1071)## Warning: package 'e1071' was built under R version 4.5.3##
## Attaching package: 'e1071'## The following object is masked from 'package:ggplot2':
##
## elementfcm_result <- cmeans(pca_data, centers = 6)
# Hasil cluster
fcm_result$cluster## [1] 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5
## [38] 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5
## [75] 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2
## [112] 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6
## [149] 4 6 6 6 6 6 6 6 6 6 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4
## [186] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2
## [223] 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4
## [260] 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6
## [297] 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5
## [334] 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 6 6 6 6 2 6 2 2 2 2 2 2 2 5 5 5 5 5 5 5
## [371] 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 2 2 4 6 5
## [408] 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5
## [445] 2 2 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2
## [482] 6 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 6
## [519] 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 6 4 6 6 6 6 6 6
## [556] 2 6 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4
## [593] 4 4 4 4 4 4 4 4 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 2 4
## [630] 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 6 4 6
## [667] 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5
## [704] 5 2 6 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5
## [741] 2 6 4 6 5 5 2 6 4 6 6 6 6 6 2 2 2 6 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [778] 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 2 2 4 6 5 5 2 2 4 6 5 5
## [815] 2 6 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2
## [852] 2 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 2
## [889] 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 2 4
## [926] 6 5 5 2 2 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 2 4 6 6 6 6 6 2 6 6 6 2 2 2 2
## [963] 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1000] 4 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 2 4 6 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 2 4 6
## [1037] 5 5 2 6 4 6 5 5 2 6 4 6 5 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1
## [1074] 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5
## [1111] 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2
## [1148] 2 4 6 6 6 6 6 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 4 4 4 4
## [1185] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2
## [1222] 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2
## [1259] 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4
## [1296] 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6
## [1333] 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 6 6 6 6 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1
## [1370] 1 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 5 2 2 4 6
## [1407] 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1
## [1444] 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5
## [1481] 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2
## [1518] 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 6 6 6 6
## [1555] 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4
## [1592] 4 4 4 4 4 4 4 4 4 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2
## [1629] 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 2 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4
## [1666] 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6
## [1703] 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1
## [1740] 5 2 3 4 6 1 5 2 3 4 6 6 6 6 6 3 3 3 3 2 2 2 2 2 1 1 1 1 1 1 1 5 5 5 5 5 5
## [1777] 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 5 2 3 4 6 1 5 2 3 4 6 1
## [1814] 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5
## [1851] 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2
## [1888] 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3
## [1925] 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 6 6 6 6 3 3 3 3 2 2 2
## [1962] 2 2 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1999] 4 4 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4
## [2036] 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6
## [2073] 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1
## [2110] 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 2 3 4 6 1 5 3 3 3 3 1 1
## [2147] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3
## [2184] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1
## [2221] 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3
## [2258] 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3
## [2295] 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3
## [2332] 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1
## [2369] 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3
## [2406] 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3
## [2443] 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1
## [2480] 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1
## [2517] 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3
## [2554] 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3
## [2591] 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3
## [2628] 1 1 3 3 3 3 1 1 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1
## [2665] 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3
## [2702] 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3
## [2739] 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3 1 1 3 3 3 3
## [2776] 1 1 3 3 3 3 1 1 3 3 3 3 1 1# Visualisasi
fviz_cluster(list(data = pca_data, cluster = fcm_result$cluster))
library(fpc)## Warning: package 'fpc' was built under R version 4.5.3dbscan_result <- dbscan(pca_data, eps = 0.5, MinPts = 5)
# Hasil cluster
dbscan_result$cluster## [1] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [25] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [49] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [73] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [97] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [121] 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
## [145] 1 2 3 4 5 6 6 6 6 6 4 4 4 4 3 3 3 3 3 1 1 1 1 1
## [169] 1 1 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5
## [193] 5 5 5 5 5 5 5 5 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [217] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [241] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [265] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [289] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [313] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [337] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 12 12 12 12 10 10 10 10 9 9
## [361] 9 9 9 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 11 11 11 11
## [385] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 7 8 9 10 11 12 7 8
## [409] 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8
## [433] 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8
## [457] 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8
## [481] 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8
## [505] 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8
## [529] 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 12 12
## [553] 12 12 10 10 10 10 9 9 9 9 9 7 7 7 7 7 7 7 8 8 8 8 8 8
## [577] 8 8 8 8 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [601] 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12
## [625] 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12
## [649] 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12
## [673] 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12
## [697] 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12
## [721] 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12
## [745] 7 8 9 10 11 12 12 12 12 12 10 10 10 10 9 9 9 9 9 7 7 7 7 7
## [769] 7 7 8 8 8 8 8 8 8 8 8 8 11 11 11 11 11 11 11 11 11 11 11 11
## [793] 11 11 11 11 11 11 11 11 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [817] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [841] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [865] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [889] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [913] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10
## [937] 11 12 7 8 9 10 11 12 7 8 9 10 11 12 12 12 12 12 10 10 10 10 9 9
## [961] 9 9 9 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 11 11 11 11
## [985] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 7 8 9 10 11 12 7 8
## [1009] 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8
## [1033] 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 7 8 13 14 15 16 17 18
## [1057] 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18
## [1081] 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18
## [1105] 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18
## [1129] 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 16 16
## [1153] 16 16 14 14 14 14 13 13 13 13 13 17 17 17 17 17 17 17 18 18 18 18 18 18
## [1177] 18 18 18 18 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [1201] 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16
## [1225] 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16
## [1249] 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16
## [1273] 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16
## [1297] 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16
## [1321] 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16
## [1345] 17 18 13 14 15 16 16 16 16 16 14 14 14 14 13 13 13 13 13 17 17 17 17 17
## [1369] 17 17 18 18 18 18 18 18 18 18 18 18 15 15 15 15 15 15 15 15 15 15 15 15
## [1393] 15 15 15 15 15 15 15 15 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14
## [1417] 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14
## [1441] 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14
## [1465] 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14
## [1489] 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14
## [1513] 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14
## [1537] 15 16 17 18 13 14 15 16 17 18 13 14 15 16 16 16 16 16 14 14 14 14 13 13
## [1561] 13 13 13 17 17 17 17 17 17 17 18 18 18 18 18 18 18 18 18 18 15 15 15 15
## [1585] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 17 18 13 14 15 16 17 18
## [1609] 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18
## [1633] 13 14 15 16 17 18 13 14 15 16 17 18 13 14 15 16 17 18 19 20 21 22 23 24
## [1657] 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24
## [1681] 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24
## [1705] 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24
## [1729] 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 22 22
## [1753] 22 22 20 20 20 20 19 19 19 19 19 23 23 23 23 23 23 23 24 24 24 24 24 24
## [1777] 24 24 24 24 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
## [1801] 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22
## [1825] 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22
## [1849] 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22
## [1873] 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22
## [1897] 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22
## [1921] 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22
## [1945] 23 24 19 20 21 22 22 22 22 22 20 20 20 20 19 19 19 19 19 23 23 23 23 23
## [1969] 23 23 24 24 24 24 24 24 24 24 24 24 21 21 21 21 21 21 21 21 21 21 21 21
## [1993] 21 21 21 21 21 21 21 21 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20
## [2017] 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20
## [2041] 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20
## [2065] 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20
## [2089] 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20
## [2113] 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20 21 22 23 24 19 20
## [2137] 21 22 23 24 25 26 27 28 29 30 25 26 27 28 28 28 28 28 26 26 26 26 25 25
## [2161] 25 25 25 29 29 29 29 29 29 29 30 30 30 30 30 30 30 30 30 30 27 27 27 27
## [2185] 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 29 30 25 26 27 28 29 30
## [2209] 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30
## [2233] 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30
## [2257] 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30
## [2281] 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30
## [2305] 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30
## [2329] 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 28 28
## [2353] 28 28 26 26 26 26 25 25 25 25 25 29 29 29 29 29 29 29 30 30 30 30 30 30
## [2377] 30 30 30 30 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
## [2401] 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28
## [2425] 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28
## [2449] 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28
## [2473] 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28
## [2497] 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 29
## [2521] 30 25 26 27 28 29 30 25 26 27 28 29 30 25 26 27 28 29 30 31 32 33 34 35
## [2545] 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35
## [2569] 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35
## [2593] 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35
## [2617] 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 35 36
## [2641] 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36
## [2665] 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36
## [2689] 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36
## [2713] 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36
## [2737] 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 35 36 31
## [2761] 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31 32 33 34 35 36 31
## [2785] 32 33 34 35 36# Visualisasi
plot(dbscan_result, pca_data)
library(meanShiftR)
ms <- meanShift(as.matrix(pca_data))
# Hasil cluster
ms$assignment## [,1]
## [1,] 1
## [2,] 2
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