# Membuat data mahasiswa
No <- 1:100
Nama <- c(
"Aatirah Zuyyin","Abidah Zubair","Afifah Yamini","Afnan Wasima","Alishba Wajizah",
"Arfathan Yassar","Arrazi Yaqdhan","Bahira Uzdah","Bari’ah Unaisah","Busyra Tsabitah",
"Cordelia Tahsina","Daiyan Usaim","Dikara Umair","Diyanah Sakhi","Dzafri Ulwan",
"Dzakwaan Ukasya","Emran Tau`iid","Emyr Tasadduq","Erzhan Tarannum","Faaruuq Taheem",
"Fadhal Taamir","Faraj Sarfraz","Fathansyah Saqer","Fathinah Qismina","Fildzhah Qamira",
"Gamya Saabiq","Ghaisan Rasya","Ghaniyyah Orzala","Ghazi Raidu","Ghaziyah Ojaala",
"Ghina Oamra","Haatim Raaghiib","Habrizi Qashash","Haldis Qadir","Hana Nafhah",
"Hanania Nadhirah","Hanunah Nabila","Izzati Lubna","Jaffan Owais","Jaiz Osama",
"Jajmi Omran","Kalifa Kayesa","Kanz Nabih","Kayesa Kalifa","Kazim mulia",
"Khansa Juhairah","Lahfah Jannah","Laila Jameela","Laiq Mabkhut","Latif Ma’arif",
"Laziz Luzman","Itizam Luthfan","Maazin Laith","Mahdyah Iklilah","Malihah Ibnatu",
"Manaf kemenangan","Mazaya Hazimah","Naayif Kaysan","Nabih Kanz","Naflah Halwa",
"Nahdan Kalil","Nailah Haida","Nuha Hafsha","Obaid Jawad","Ouarda Ghaliyatul",
"Qabil Hibrizi","Qafiya Finna","Qahhar Hafizhani","Qarirah Fayza","Qudamah Haaziq",
"Raeef Gulzar","Rahadatu Ezzah","Rakhan Ghauth","Ramizah Erina","Sadira Eijaz",
"Sakhi Diyanah","Setia Fahriza","Shakhar Fahd","Syahmina Dairah","Talita Chayra",
"Tamima Chayra","Tanvir Esad","Tasadduq Emyr","Thafana Chairunnisa","Udayl Efendy",
"Ulwan Dilawar","Uqbah Daniyal","Utsratun Bahiyya","Uwais Carim","Varisha Athifah",
"Varten Bilfaqih","Velid bijak","Wafiyya Aniq","Wafiyyati Aliza","Wail Bashra",
"Wasima Afnan","Xaquille Atthallah","Yafizhan Asraf","Yang Arsalan","Yassar Arfathan"
)
JenisKelamin <- c(
"Perempuan","Laki-laki","Perempuan","Perempuan","Perempuan",
"Laki-laki","Laki-laki","Perempuan","Perempuan","Perempuan",
"Perempuan","Laki-laki","Laki-laki","Perempuan","Laki-laki",
"Laki-laki","Laki-laki","Laki-laki","Laki-laki","Laki-laki",
"Laki-laki","Laki-laki","Laki-laki","Perempuan","Perempuan",
"Laki-laki","Laki-laki","Perempuan","Laki-laki","Perempuan",
"Perempuan","Laki-laki","Laki-laki","Laki-laki","Perempuan",
"Perempuan","Perempuan","Perempuan","Laki-laki","Laki-laki",
"Laki-laki","Perempuan","Laki-laki","Perempuan","Laki-laki",
"Perempuan","Perempuan","Perempuan","Laki-laki","Laki-laki",
"Laki-laki","Laki-laki","Laki-laki","Perempuan","Perempuan",
"Laki-laki","Perempuan","Laki-laki","Laki-laki","Perempuan",
"Laki-laki","Perempuan","Perempuan","Laki-laki","Perempuan",
"Laki-laki","Perempuan","Laki-laki","Perempuan","Laki-laki",
"Laki-laki","Perempuan","Laki-laki","Perempuan","Perempuan",
"Perempuan","Laki-laki","Laki-laki","Perempuan","Perempuan",
"Perempuan","Laki-laki","Laki-laki","Perempuan","Laki-laki",
"Laki-laki","Laki-laki","Perempuan","Laki-laki","Perempuan",
"Laki-laki","Laki-laki","Perempuan","Perempuan","Laki-laki",
"Perempuan","Laki-laki","Laki-laki","Laki-laki","Laki-laki"
)
Usia <- c(
29,25,32,32,28,25,32,26,30,28,
30,27,33,30,24,26,27,26,32,28,
25,26,33,30,30,27,31,26,24,33,
31,30,24,31,24,25,28,28,26,24,
24,27,32,33,27,28,33,28,31,33,
24,29,28,29,27,31,33,31,31,32,
32,32,28,26,24,31,33,25,27,24,
27,29,28,28,25,33,33,24,33,28,
27,24,26,24,29,31,25,25,29,30,
33,26,33,27,30,29,27,31,24,32
)
data_mahasiswa <- data.frame(No,Nama,JenisKelamin,Usia)set.seed(123)
sample_simple <- data_mahasiswa[sample(1:100, 20), ]
sample_simple## No Nama JenisKelamin Usia
## 31 31 Ghina Oamra Perempuan 31
## 79 79 Syahmina Dairah Perempuan 33
## 51 51 Laziz Luzman Laki-laki 24
## 14 14 Diyanah Sakhi Perempuan 30
## 67 67 Qafiya Finna Perempuan 33
## 42 42 Kalifa Kayesa Perempuan 27
## 50 50 Latif Ma’arif Laki-laki 33
## 43 43 Kanz Nabih Laki-laki 32
## 97 97 Xaquille Atthallah Laki-laki 27
## 25 25 Fildzhah Qamira Perempuan 30
## 90 90 Varisha Athifah Perempuan 30
## 69 69 Qarirah Fayza Perempuan 27
## 57 57 Mazaya Hazimah Perempuan 33
## 9 9 Bari’ah Unaisah Perempuan 30
## 72 72 Rahadatu Ezzah Perempuan 29
## 26 26 Gamya Saabiq Laki-laki 27
## 7 7 Arrazi Yaqdhan Laki-laki 32
## 95 95 Wail Bashra Laki-laki 30
## 87 87 Uqbah Daniyal Laki-laki 25
## 36 36 Hanania Nadhirah Perempuan 25Artinya 20 mahasiswa dipilih secara acak dari 100 mahasiswa.
k <- 5
start <- sample(1:k,1)
sample_systematic <- data_mahasiswa[seq(start,100,by=k),]
sample_systematic## No Nama JenisKelamin Usia
## 1 1 Aatirah Zuyyin Perempuan 29
## 6 6 Arfathan Yassar Laki-laki 25
## 11 11 Cordelia Tahsina Perempuan 30
## 16 16 Dzakwaan Ukasya Laki-laki 26
## 21 21 Fadhal Taamir Laki-laki 25
## 26 26 Gamya Saabiq Laki-laki 27
## 31 31 Ghina Oamra Perempuan 31
## 36 36 Hanania Nadhirah Perempuan 25
## 41 41 Jajmi Omran Laki-laki 24
## 46 46 Khansa Juhairah Perempuan 28
## 51 51 Laziz Luzman Laki-laki 24
## 56 56 Manaf kemenangan Laki-laki 31
## 61 61 Nahdan Kalil Laki-laki 32
## 66 66 Qabil Hibrizi Laki-laki 31
## 71 71 Raeef Gulzar Laki-laki 27
## 76 76 Sakhi Diyanah Perempuan 33
## 81 81 Tamima Chayra Perempuan 27
## 86 86 Ulwan Dilawar Laki-laki 31
## 91 91 Varten Bilfaqih Laki-laki 33
## 96 96 Wasima Afnan Perempuan 29Artinya setiap mahasiswa ke-5 dipilih sebagai sampel.
(Strata berdasarkan Jenis Kelamin)
library(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, unionsample_stratified <- data_mahasiswa %>%
group_by(JenisKelamin) %>%
sample_n(10)
sample_stratified## # A tibble: 20 × 4
## # Groups: JenisKelamin [2]
## No Nama JenisKelamin Usia
## <int> <chr> <chr> <dbl>
## 1 82 Tanvir Esad Laki-laki 24
## 2 71 Raeef Gulzar Laki-laki 27
## 3 98 Yafizhan Asraf Laki-laki 31
## 4 21 Fadhal Taamir Laki-laki 25
## 5 26 Gamya Saabiq Laki-laki 27
## 6 58 Naayif Kaysan Laki-laki 31
## 7 78 Shakhar Fahd Laki-laki 24
## 8 85 Udayl Efendy Laki-laki 29
## 9 16 Dzakwaan Ukasya Laki-laki 26
## 10 18 Emyr Tasadduq Laki-laki 26
## 11 88 Utsratun Bahiyya Perempuan 25
## 12 24 Fathinah Qismina Perempuan 30
## 13 48 Laila Jameela Perempuan 28
## 14 60 Naflah Halwa Perempuan 32
## 15 10 Busyra Tsabitah Perempuan 28
## 16 90 Varisha Athifah Perempuan 30
## 17 69 Qarirah Fayza Perempuan 27
## 18 80 Talita Chayra Perempuan 28
## 19 55 Malihah Ibnatu Perempuan 27
## 20 74 Ramizah Erina Perempuan 28Artinya masing-masing gender diambil sampel 10 mahasiswa.
data_mahasiswa$cluster <- ceiling(data_mahasiswa$No/20)
unique(data_mahasiswa$cluster)## [1] 1 2 3 4 5cluster_terpilih <- sample(unique(data_mahasiswa$cluster),1)
sample_cluster <- data_mahasiswa[data_mahasiswa$cluster == cluster_terpilih,]
sample_cluster## No Nama JenisKelamin Usia cluster
## 81 81 Tamima Chayra Perempuan 27 5
## 82 82 Tanvir Esad Laki-laki 24 5
## 83 83 Tasadduq Emyr Laki-laki 26 5
## 84 84 Thafana Chairunnisa Perempuan 24 5
## 85 85 Udayl Efendy Laki-laki 29 5
## 86 86 Ulwan Dilawar Laki-laki 31 5
## 87 87 Uqbah Daniyal Laki-laki 25 5
## 88 88 Utsratun Bahiyya Perempuan 25 5
## 89 89 Uwais Carim Laki-laki 29 5
## 90 90 Varisha Athifah Perempuan 30 5
## 91 91 Varten Bilfaqih Laki-laki 33 5
## 92 92 Velid bijak Laki-laki 26 5
## 93 93 Wafiyya Aniq Perempuan 33 5
## 94 94 Wafiyyati Aliza Perempuan 27 5
## 95 95 Wail Bashra Laki-laki 30 5
## 96 96 Wasima Afnan Perempuan 29 5
## 97 97 Xaquille Atthallah Laki-laki 27 5
## 98 98 Yafizhan Asraf Laki-laki 31 5
## 99 99 Yang Arsalan Laki-laki 24 5
## 100 100 Yassar Arfathan Laki-laki 32 5Artinya 1 kelompok mahasiswa dipilih sebagai sampel.
mean(sample_simple$Usia)## [1] 29.4mean(sample_systematic$Usia)## [1] 28.4mean(sample_stratified$Usia)## [1] 27.65mean(sample_cluster$Usia)## [1] 28.1