library(ggplot2)
pelanggan <- read.csv("https://storage.googleapis.com/dqlab-dataset/customer_segments.txt", sep="\t")
head(pelanggan)
## Customer_ID Nama.Pelanggan Jenis.Kelamin Umur Profesi Tipe.Residen
## 1 CUST-001 Budi Anggara Pria 58 Wiraswasta Sector
## 2 CUST-002 Shirley Ratuwati Wanita 14 Pelajar Cluster
## 3 CUST-003 Agus Cahyono Pria 48 Professional Cluster
## 4 CUST-004 Antonius Winarta Pria 53 Professional Cluster
## 5 CUST-005 Ibu Sri Wahyuni, IR Wanita 41 Wiraswasta Cluster
## 6 CUST-006 Rosalina Kurnia Wanita 24 Professional Cluster
## NilaiBelanjaSetahun
## 1 9497927
## 2 2722700
## 3 5286429
## 4 5204498
## 5 10615206
## 6 5215541
pelanggan_matrix <- data.matrix(pelanggan[c("Jenis.Kelamin", "Profesi", "Tipe.Residen")])
pelanggan_matrix
## Jenis.Kelamin Profesi Tipe.Residen
## [1,] 1 5 2
## [2,] 2 3 1
## [3,] 1 4 1
## [4,] 1 4 1
## [5,] 2 5 1
## [6,] 2 4 1
## [7,] 1 5 2
## [8,] 1 4 1
## [9,] 2 4 2
## [10,] 1 4 1
## [11,] 2 4 2
## [12,] 2 4 2
## [13,] 2 5 1
## [14,] 1 5 1
## [15,] 2 5 1
## [16,] 1 4 1
## [17,] 2 1 1
## [18,] 2 1 1
## [19,] 2 5 1
## [20,] 2 3 2
## [21,] 2 5 1
## [22,] 2 4 1
## [23,] 1 4 1
## [24,] 2 5 1
## [25,] 2 5 2
## [26,] 2 4 1
## [27,] 2 5 1
## [28,] 2 1 1
## [29,] 2 4 1
## [30,] 2 1 2
## [31,] 2 2 1
## [32,] 2 5 2
## [33,] 2 2 1
## [34,] 2 5 2
## [35,] 2 4 2
## [36,] 2 5 1
## [37,] 2 4 2
## [38,] 2 5 2
## [39,] 2 4 1
## [40,] 2 3 2
## [41,] 2 1 1
## [42,] 2 5 1
## [43,] 2 4 1
## [44,] 2 5 1
## [45,] 2 4 1
## [46,] 2 5 2
## [47,] 2 1 1
## [48,] 2 5 2
## [49,] 2 1 2
## [50,] 2 5 2
pelanggan2 <- data.frame(pelanggan, pelanggan_matrix)
pelanggan2
## Customer_ID Nama.Pelanggan Jenis.Kelamin Umur Profesi
## 1 CUST-001 Budi Anggara Pria 58 Wiraswasta
## 2 CUST-002 Shirley Ratuwati Wanita 14 Pelajar
## 3 CUST-003 Agus Cahyono Pria 48 Professional
## 4 CUST-004 Antonius Winarta Pria 53 Professional
## 5 CUST-005 Ibu Sri Wahyuni, IR Wanita 41 Wiraswasta
## 6 CUST-006 Rosalina Kurnia Wanita 24 Professional
## 7 CUST-007 Cahyono, Agus Pria 64 Wiraswasta
## 8 CUST-008 Danang Santosa Pria 52 Professional
## 9 CUST-009 Elisabeth Suryadinata Wanita 29 Professional
## 10 CUST-010 Mario Setiawan Pria 33 Professional
## 11 CUST-011 Maria Suryawan Wanita 50 Professional
## 12 CUST-012 Erliana Widjaja Wanita 49 Professional
## 13 CUST-013 Cahaya Putri Wanita 64 Wiraswasta
## 14 CUST-014 Mario Setiawan Pria 60 Wiraswasta
## 15 CUST-015 Shirley Ratuwati Wanita 20 Wiraswasta
## 16 CUST-016 Bambang Rudi Pria 35 Professional
## 17 CUST-017 Yuni Sari Wanita 32 Ibu Rumah Tangga
## 18 CUST-018 Nelly Halim Wanita 63 Ibu Rumah Tangga
## 19 CUST-019 Mega Pranoto Wanita 32 Wiraswasta
## 20 CUST-020 Irene Novianto Wanita 16 Pelajar
## 21 CUST-021 Lestari Fabianto Wanita 38 Wiraswasta
## 22 CUST-022 Novita Purba Wanita 52 Professional
## 23 CUST-023 Denny Amiruddin Pria 34 Professional
## 24 CUST-024 Putri Ginting Wanita 39 Wiraswasta
## 25 CUST-025 Julia Setiawan Wanita 29 Wiraswasta
## 26 CUST-026 Christine Winarto Wanita 55 Professional
## 27 CUST-027 Grace Mulyati Wanita 35 Wiraswasta
## 28 CUST-028 Adeline Huang Wanita 40 Ibu Rumah Tangga
## 29 CUST-029 Tia Hartanti Wanita 56 Professional
## 30 CUST-030 Rosita Saragih Wanita 46 Ibu Rumah Tangga
## 31 CUST-031 Eviana Handry Wanita 19 Mahasiswa
## 32 CUST-032 Chintya Winarni Wanita 47 Wiraswasta
## 33 CUST-033 Cecilia Kusnadi Wanita 19 Mahasiswa
## 34 CUST-034 Deasy Arisandi Wanita 21 Wiraswasta
## 35 CUST-035 Ida Ayu Wanita 39 Professional
## 36 CUST-036 Ni Made Suasti Wanita 30 Wiraswasta
## 37 CUST-037 Felicia Tandiono Wanita 25 Professional
## 38 CUST-038 Agatha Salim Wanita 46 Wiraswasta
## 39 CUST-039 Gina Hidayat Wanita 20 Professional
## 40 CUST-040 Irene Darmawan Wanita 14 Pelajar
## 41 CUST-041 Shinta Aritonang Wanita 24 Ibu Rumah Tangga
## 42 CUST-042 Yuliana Wati Wanita 26 Wiraswasta
## 43 CUST-043 Yenna Sumadi Wanita 31 Professional
## 44 CUST-044 Anna Wanita 18 Wiraswasta
## 45 CUST-045 Rismawati Juni Wanita 22 Professional
## 46 CUST-046 Elfira Surya Wanita 25 Wiraswasta
## 47 CUST-047 Mira Kurnia Wanita 55 Ibu Rumah Tangga
## 48 CUST-048 Maria Hutagalung Wanita 45 Wiraswasta
## 49 CUST-049 Josephine Wahab Wanita 33 Ibu Rumah Tangga
## 50 CUST-050 Lianna Nugraha Wanita 55 Wiraswasta
## Tipe.Residen NilaiBelanjaSetahun Jenis.Kelamin.1 Profesi.1 Tipe.Residen.1
## 1 Sector 9497927 1 5 2
## 2 Cluster 2722700 2 3 1
## 3 Cluster 5286429 1 4 1
## 4 Cluster 5204498 1 4 1
## 5 Cluster 10615206 2 5 1
## 6 Cluster 5215541 2 4 1
## 7 Sector 9837260 1 5 2
## 8 Cluster 5223569 1 4 1
## 9 Sector 5993218 2 4 2
## 10 Cluster 5257448 1 4 1
## 11 Sector 5987367 2 4 2
## 12 Sector 5941914 2 4 2
## 13 Cluster 9333168 2 5 1
## 14 Cluster 9471615 1 5 1
## 15 Cluster 10365668 2 5 1
## 16 Cluster 5262521 1 4 1
## 17 Cluster 5677762 2 1 1
## 18 Cluster 5340690 2 1 1
## 19 Cluster 10884508 2 5 1
## 20 Sector 2896845 2 3 2
## 21 Cluster 9222070 2 5 1
## 22 Cluster 5298157 2 4 1
## 23 Cluster 5239290 1 4 1
## 24 Cluster 10259572 2 5 1
## 25 Sector 10721998 2 5 2
## 26 Cluster 5269392 2 4 1
## 27 Cluster 9114159 2 5 1
## 28 Cluster 6631680 2 1 1
## 29 Cluster 5271845 2 4 1
## 30 Sector 5020976 2 1 2
## 31 Cluster 3042773 2 2 1
## 32 Sector 10663179 2 5 2
## 33 Cluster 3047926 2 2 1
## 34 Sector 9759822 2 5 2
## 35 Sector 5962575 2 4 2
## 36 Cluster 9678994 2 5 1
## 37 Sector 5972787 2 4 2
## 38 Sector 10477127 2 5 2
## 39 Cluster 5257775 2 4 1
## 40 Sector 2861855 2 3 2
## 41 Cluster 6820976 2 1 1
## 42 Cluster 9880607 2 5 1
## 43 Cluster 5268410 2 4 1
## 44 Cluster 9339737 2 5 1
## 45 Cluster 5211041 2 4 1
## 46 Sector 10099807 2 5 2
## 47 Cluster 6130724 2 1 1
## 48 Sector 10390732 2 5 2
## 49 Sector 4992585 2 1 2
## 50 Sector 10569316 2 5 2
pelanggan2$NilaiBelanjaSetahun <- pelanggan$NilaiBelanjaSetahun / 10000
pelanggan2
## Customer_ID Nama.Pelanggan Jenis.Kelamin Umur Profesi
## 1 CUST-001 Budi Anggara Pria 58 Wiraswasta
## 2 CUST-002 Shirley Ratuwati Wanita 14 Pelajar
## 3 CUST-003 Agus Cahyono Pria 48 Professional
## 4 CUST-004 Antonius Winarta Pria 53 Professional
## 5 CUST-005 Ibu Sri Wahyuni, IR Wanita 41 Wiraswasta
## 6 CUST-006 Rosalina Kurnia Wanita 24 Professional
## 7 CUST-007 Cahyono, Agus Pria 64 Wiraswasta
## 8 CUST-008 Danang Santosa Pria 52 Professional
## 9 CUST-009 Elisabeth Suryadinata Wanita 29 Professional
## 10 CUST-010 Mario Setiawan Pria 33 Professional
## 11 CUST-011 Maria Suryawan Wanita 50 Professional
## 12 CUST-012 Erliana Widjaja Wanita 49 Professional
## 13 CUST-013 Cahaya Putri Wanita 64 Wiraswasta
## 14 CUST-014 Mario Setiawan Pria 60 Wiraswasta
## 15 CUST-015 Shirley Ratuwati Wanita 20 Wiraswasta
## 16 CUST-016 Bambang Rudi Pria 35 Professional
## 17 CUST-017 Yuni Sari Wanita 32 Ibu Rumah Tangga
## 18 CUST-018 Nelly Halim Wanita 63 Ibu Rumah Tangga
## 19 CUST-019 Mega Pranoto Wanita 32 Wiraswasta
## 20 CUST-020 Irene Novianto Wanita 16 Pelajar
## 21 CUST-021 Lestari Fabianto Wanita 38 Wiraswasta
## 22 CUST-022 Novita Purba Wanita 52 Professional
## 23 CUST-023 Denny Amiruddin Pria 34 Professional
## 24 CUST-024 Putri Ginting Wanita 39 Wiraswasta
## 25 CUST-025 Julia Setiawan Wanita 29 Wiraswasta
## 26 CUST-026 Christine Winarto Wanita 55 Professional
## 27 CUST-027 Grace Mulyati Wanita 35 Wiraswasta
## 28 CUST-028 Adeline Huang Wanita 40 Ibu Rumah Tangga
## 29 CUST-029 Tia Hartanti Wanita 56 Professional
## 30 CUST-030 Rosita Saragih Wanita 46 Ibu Rumah Tangga
## 31 CUST-031 Eviana Handry Wanita 19 Mahasiswa
## 32 CUST-032 Chintya Winarni Wanita 47 Wiraswasta
## 33 CUST-033 Cecilia Kusnadi Wanita 19 Mahasiswa
## 34 CUST-034 Deasy Arisandi Wanita 21 Wiraswasta
## 35 CUST-035 Ida Ayu Wanita 39 Professional
## 36 CUST-036 Ni Made Suasti Wanita 30 Wiraswasta
## 37 CUST-037 Felicia Tandiono Wanita 25 Professional
## 38 CUST-038 Agatha Salim Wanita 46 Wiraswasta
## 39 CUST-039 Gina Hidayat Wanita 20 Professional
## 40 CUST-040 Irene Darmawan Wanita 14 Pelajar
## 41 CUST-041 Shinta Aritonang Wanita 24 Ibu Rumah Tangga
## 42 CUST-042 Yuliana Wati Wanita 26 Wiraswasta
## 43 CUST-043 Yenna Sumadi Wanita 31 Professional
## 44 CUST-044 Anna Wanita 18 Wiraswasta
## 45 CUST-045 Rismawati Juni Wanita 22 Professional
## 46 CUST-046 Elfira Surya Wanita 25 Wiraswasta
## 47 CUST-047 Mira Kurnia Wanita 55 Ibu Rumah Tangga
## 48 CUST-048 Maria Hutagalung Wanita 45 Wiraswasta
## 49 CUST-049 Josephine Wahab Wanita 33 Ibu Rumah Tangga
## 50 CUST-050 Lianna Nugraha Wanita 55 Wiraswasta
## Tipe.Residen NilaiBelanjaSetahun Jenis.Kelamin.1 Profesi.1 Tipe.Residen.1
## 1 Sector 949.7927 1 5 2
## 2 Cluster 272.2700 2 3 1
## 3 Cluster 528.6429 1 4 1
## 4 Cluster 520.4498 1 4 1
## 5 Cluster 1061.5206 2 5 1
## 6 Cluster 521.5541 2 4 1
## 7 Sector 983.7260 1 5 2
## 8 Cluster 522.3569 1 4 1
## 9 Sector 599.3218 2 4 2
## 10 Cluster 525.7448 1 4 1
## 11 Sector 598.7367 2 4 2
## 12 Sector 594.1914 2 4 2
## 13 Cluster 933.3168 2 5 1
## 14 Cluster 947.1615 1 5 1
## 15 Cluster 1036.5668 2 5 1
## 16 Cluster 526.2521 1 4 1
## 17 Cluster 567.7762 2 1 1
## 18 Cluster 534.0690 2 1 1
## 19 Cluster 1088.4508 2 5 1
## 20 Sector 289.6845 2 3 2
## 21 Cluster 922.2070 2 5 1
## 22 Cluster 529.8157 2 4 1
## 23 Cluster 523.9290 1 4 1
## 24 Cluster 1025.9572 2 5 1
## 25 Sector 1072.1998 2 5 2
## 26 Cluster 526.9392 2 4 1
## 27 Cluster 911.4159 2 5 1
## 28 Cluster 663.1680 2 1 1
## 29 Cluster 527.1845 2 4 1
## 30 Sector 502.0976 2 1 2
## 31 Cluster 304.2773 2 2 1
## 32 Sector 1066.3179 2 5 2
## 33 Cluster 304.7926 2 2 1
## 34 Sector 975.9822 2 5 2
## 35 Sector 596.2575 2 4 2
## 36 Cluster 967.8994 2 5 1
## 37 Sector 597.2787 2 4 2
## 38 Sector 1047.7127 2 5 2
## 39 Cluster 525.7775 2 4 1
## 40 Sector 286.1855 2 3 2
## 41 Cluster 682.0976 2 1 1
## 42 Cluster 988.0607 2 5 1
## 43 Cluster 526.8410 2 4 1
## 44 Cluster 933.9737 2 5 1
## 45 Cluster 521.1041 2 4 1
## 46 Sector 1009.9807 2 5 2
## 47 Cluster 613.0724 2 1 1
## 48 Sector 1039.0732 2 5 2
## 49 Sector 499.2585 2 1 2
## 50 Sector 1056.9316 2 5 2
Profesi <- unique(pelanggan2[c("Profesi","Profesi.1")])
Jenis.Kelamin <- unique(pelanggan2[c("Jenis.Kelamin","Jenis.Kelamin.1")])
Tipe.Residen <- unique(pelanggan2[c("Tipe.Residen","Tipe.Residen.1")])
Profesi
## Profesi Profesi.1
## 1 Wiraswasta 5
## 2 Pelajar 3
## 3 Professional 4
## 17 Ibu Rumah Tangga 1
## 31 Mahasiswa 2
Jenis.Kelamin
## Jenis.Kelamin Jenis.Kelamin.1
## 1 Pria 1
## 2 Wanita 2
Tipe.Residen
## Tipe.Residen Tipe.Residen.1
## 1 Sector 2
## 2 Cluster 1
set.seed(100)
field_yang_digunakan = c("Jenis.Kelamin.1", "Umur", "Profesi.1", "Tipe.Residen.1", "NilaiBelanjaSetahun")
segmentasi <- kmeans(x=pelanggan2[field_yang_digunakan], centers = 5, nstart = 25)
pelanggan2$cluster <- segmentasi$cluster
str(pelanggan)
## 'data.frame': 50 obs. of 7 variables:
## $ Customer_ID : chr "CUST-001" "CUST-002" "CUST-003" "CUST-004" ...
## $ Nama.Pelanggan : chr "Budi Anggara" "Shirley Ratuwati" "Agus Cahyono" "Antonius Winarta" ...
## $ Jenis.Kelamin : chr "Pria" "Wanita" "Pria" "Pria" ...
## $ Umur : int 58 14 48 53 41 24 64 52 29 33 ...
## $ Profesi : chr "Wiraswasta" "Pelajar" "Professional" "Professional" ...
## $ Tipe.Residen : chr "Sector" "Cluster" "Cluster" "Cluster" ...
## $ NilaiBelanjaSetahun: int 9497927 2722700 5286429 5204498 10615206 5215541 9837260 5223569 5993218 5257448 ...
which(pelanggan2$cluster == 1)
## [1] 5 15 19 24 25 32 38 46 48 50
pelanggan[which(pelanggan2$cluster == 1),]
## Customer_ID Nama.Pelanggan Jenis.Kelamin Umur Profesi Tipe.Residen
## 5 CUST-005 Ibu Sri Wahyuni, IR Wanita 41 Wiraswasta Cluster
## 15 CUST-015 Shirley Ratuwati Wanita 20 Wiraswasta Cluster
## 19 CUST-019 Mega Pranoto Wanita 32 Wiraswasta Cluster
## 24 CUST-024 Putri Ginting Wanita 39 Wiraswasta Cluster
## 25 CUST-025 Julia Setiawan Wanita 29 Wiraswasta Sector
## 32 CUST-032 Chintya Winarni Wanita 47 Wiraswasta Sector
## 38 CUST-038 Agatha Salim Wanita 46 Wiraswasta Sector
## 46 CUST-046 Elfira Surya Wanita 25 Wiraswasta Sector
## 48 CUST-048 Maria Hutagalung Wanita 45 Wiraswasta Sector
## 50 CUST-050 Lianna Nugraha Wanita 55 Wiraswasta Sector
## NilaiBelanjaSetahun
## 5 10615206
## 15 10365668
## 19 10884508
## 24 10259572
## 25 10721998
## 32 10663179
## 38 10477127
## 46 10099807
## 48 10390732
## 50 10569316
pelanggan[which(pelanggan2$cluster == 2),]
## Customer_ID Nama.Pelanggan Jenis.Kelamin Umur Profesi Tipe.Residen
## 1 CUST-001 Budi Anggara Pria 58 Wiraswasta Sector
## 7 CUST-007 Cahyono, Agus Pria 64 Wiraswasta Sector
## 13 CUST-013 Cahaya Putri Wanita 64 Wiraswasta Cluster
## 14 CUST-014 Mario Setiawan Pria 60 Wiraswasta Cluster
## 21 CUST-021 Lestari Fabianto Wanita 38 Wiraswasta Cluster
## 27 CUST-027 Grace Mulyati Wanita 35 Wiraswasta Cluster
## 34 CUST-034 Deasy Arisandi Wanita 21 Wiraswasta Sector
## 36 CUST-036 Ni Made Suasti Wanita 30 Wiraswasta Cluster
## 42 CUST-042 Yuliana Wati Wanita 26 Wiraswasta Cluster
## 44 CUST-044 Anna Wanita 18 Wiraswasta Cluster
## NilaiBelanjaSetahun
## 1 9497927
## 7 9837260
## 13 9333168
## 14 9471615
## 21 9222070
## 27 9114159
## 34 9759822
## 36 9678994
## 42 9880607
## 44 9339737
pelanggan[which(pelanggan2$cluster == 3),]
## Customer_ID Nama.Pelanggan Jenis.Kelamin Umur Profesi Tipe.Residen
## 2 CUST-002 Shirley Ratuwati Wanita 14 Pelajar Cluster
## 20 CUST-020 Irene Novianto Wanita 16 Pelajar Sector
## 31 CUST-031 Eviana Handry Wanita 19 Mahasiswa Cluster
## 33 CUST-033 Cecilia Kusnadi Wanita 19 Mahasiswa Cluster
## 40 CUST-040 Irene Darmawan Wanita 14 Pelajar Sector
## NilaiBelanjaSetahun
## 2 2722700
## 20 2896845
## 31 3042773
## 33 3047926
## 40 2861855
pelanggan[which(pelanggan2$cluster == 4),]
## Customer_ID Nama.Pelanggan Jenis.Kelamin Umur Profesi
## 3 CUST-003 Agus Cahyono Pria 48 Professional
## 4 CUST-004 Antonius Winarta Pria 53 Professional
## 6 CUST-006 Rosalina Kurnia Wanita 24 Professional
## 8 CUST-008 Danang Santosa Pria 52 Professional
## 10 CUST-010 Mario Setiawan Pria 33 Professional
## 16 CUST-016 Bambang Rudi Pria 35 Professional
## 17 CUST-017 Yuni Sari Wanita 32 Ibu Rumah Tangga
## 18 CUST-018 Nelly Halim Wanita 63 Ibu Rumah Tangga
## 22 CUST-022 Novita Purba Wanita 52 Professional
## 23 CUST-023 Denny Amiruddin Pria 34 Professional
## 26 CUST-026 Christine Winarto Wanita 55 Professional
## 29 CUST-029 Tia Hartanti Wanita 56 Professional
## 30 CUST-030 Rosita Saragih Wanita 46 Ibu Rumah Tangga
## 39 CUST-039 Gina Hidayat Wanita 20 Professional
## 43 CUST-043 Yenna Sumadi Wanita 31 Professional
## 45 CUST-045 Rismawati Juni Wanita 22 Professional
## 49 CUST-049 Josephine Wahab Wanita 33 Ibu Rumah Tangga
## Tipe.Residen NilaiBelanjaSetahun
## 3 Cluster 5286429
## 4 Cluster 5204498
## 6 Cluster 5215541
## 8 Cluster 5223569
## 10 Cluster 5257448
## 16 Cluster 5262521
## 17 Cluster 5677762
## 18 Cluster 5340690
## 22 Cluster 5298157
## 23 Cluster 5239290
## 26 Cluster 5269392
## 29 Cluster 5271845
## 30 Sector 5020976
## 39 Cluster 5257775
## 43 Cluster 5268410
## 45 Cluster 5211041
## 49 Sector 4992585
pelanggan[which(pelanggan2$cluster == 5),]
## Customer_ID Nama.Pelanggan Jenis.Kelamin Umur Profesi
## 9 CUST-009 Elisabeth Suryadinata Wanita 29 Professional
## 11 CUST-011 Maria Suryawan Wanita 50 Professional
## 12 CUST-012 Erliana Widjaja Wanita 49 Professional
## 28 CUST-028 Adeline Huang Wanita 40 Ibu Rumah Tangga
## 35 CUST-035 Ida Ayu Wanita 39 Professional
## 37 CUST-037 Felicia Tandiono Wanita 25 Professional
## 41 CUST-041 Shinta Aritonang Wanita 24 Ibu Rumah Tangga
## 47 CUST-047 Mira Kurnia Wanita 55 Ibu Rumah Tangga
## Tipe.Residen NilaiBelanjaSetahun
## 9 Sector 5993218
## 11 Sector 5987367
## 12 Sector 5941914
## 28 Cluster 6631680
## 35 Sector 5962575
## 37 Sector 5972787
## 41 Cluster 6820976
## 47 Cluster 6130724
segmentasi$centers
## Jenis.Kelamin.1 Umur Profesi.1 Tipe.Residen.1 NilaiBelanjaSetahun
## 1 2.000000 37.90000 5.000000 1.600000 1050.4711
## 2 1.700000 41.40000 5.000000 1.300000 951.3536
## 3 2.000000 16.40000 2.600000 1.400000 291.4420
## 4 1.647059 40.52941 3.294118 1.117647 525.2819
## 5 2.000000 38.87500 2.875000 1.625000 618.0155
segmentasi$size
## [1] 10 10 5 17 8
segmentasi$withinss
## [1] 6006.0988 9398.2563 768.8686 6084.2554 9379.4628
sse <- sapply(1:10, function(param_k) {kmeans(pelanggan2[field_yang_digunakan], param_k, nstart=25)$tot.withinss})
sse
## [1] 3299202.446 419275.412 127619.818 78436.234 31636.942 22501.561
## [7] 16033.699 13658.797 11198.336 9162.656
library(ggplot2)
jumlah_cluster_max <- 10
ssdata = data.frame(cluster=c(1:jumlah_cluster_max),sse)
ggplot(ssdata, aes(x=cluster,y=sse)) +
geom_line(color="red") + geom_point() +
ylab("Within Cluster Sum of Squares") + xlab("Jumlah Cluster") +
geom_text(aes(label=format(round(sse, 2), nsmall = 2)),hjust=-0.2, vjust=-0.5) +
scale_x_discrete(limits=c(1:jumlah_cluster_max))
## Warning in scale_x_discrete(limits = c(1:jumlah_cluster_max)): Continuous limits supplied to discrete scale.
## ℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?
segmentasi$centers
## Jenis.Kelamin.1 Umur Profesi.1 Tipe.Residen.1 NilaiBelanjaSetahun
## 1 2.000000 37.90000 5.000000 1.600000 1050.4711
## 2 1.700000 41.40000 5.000000 1.300000 951.3536
## 3 2.000000 16.40000 2.600000 1.400000 291.4420
## 4 1.647059 40.52941 3.294118 1.117647 525.2819
## 5 2.000000 38.87500 2.875000 1.625000 618.0155