Algoritma & Struktur Data
~ Ujian Tengah Semester ~
| Kontak | : \(\downarrow\) |
| ali.19arifin@gmail.com | |
| https://www.instagram.com/arifin.alicia/ | |
| RPubs | https://rpubs.com/aliciaarifin/ |
Biodata
Nama : Alicia Arifin
Prodi : Statistika Bisnis
NIM : 20214920001
Kasus 1
Asumsikan Anda telah mengumpulkan beberapa kumpulan data dari perusahaan ABC Property seperti yang dapat kita lihat pada tabel berikut:
Id <- (1:10000)
Marketing_Name <- rep(c("Angel","Sherly","Vanessa","Irene","Julian",
"Jeffry","Nikita","Kefas","Siana","Lala",
"Fallen","Ardifo","Kevin","Juen","Jerrel",
"Imelda","Widi","Theodora","Elvani","Jonathan",
"Sofia","Abraham","Siti","Niko","Sefli",
"Bene", "Diana", "Pupe", "Andi", "Tatha",
"Endri", "Monika", "Hans", "Debora","Hanifa",
"James", "Jihan", "Friska","Ardiwan", "Bakti",
"Anthon","Amry", "Wiwik", "Bastian", "Budi",
"Leo","Simon","Matius","Arry", "Eliando"), 200)
Work_Exp <- rep(c(1.3,2.4,2.5,3.6,3.7,4.7,5.7,6.7,7.7,7.3,
5.3,5.3,10,9.3,3.3,3.3,3.4,3.4,3.5,5.6,
3.5,4.6,4.6,5.7,6.2,4.4,6.4,6.4,3.5,7.5,
4.6,3.7,4.7,4.3,5.2,6.3,7.4,2.4,3.4,8.2,
6.4,7.2,1.5,7.5,10,4.5,6.5,7.2,7.1,7.6),200)
City <- sample(c("Jakarta","Bogor","Depok","Tengerang","Bekasi"),10000, replace = T)
Cluster <- sample(c("Victoria","Palmyra","Winona","Tiara", "Narada",
"Peronia","Lavesh","Alindra","Sweethome", "Asera",
"Teradamai","Albasia", "Adara","Neon","Arana",
"Asoka", "Primadona", "Mutiara","Permata","Alamanda" ), 10000, replace=T)
Price <- sample(c(7000:15000),10000, replace = T)
Date_Sales <- sample(seq(as.Date("2018/01/01"), by = "day", length.out = 1000),10000, replace = T)
Advertisement <- sample(c(1:20), 10000, replace = T)
Data <- data.frame(Id,
Marketing_Name,
Work_Exp,
City,
Cluster,
Price,
Date_Sales,
Advertisement)
library(DT)
datatable(Data)write.csv(Data,"C:\\Users\\arifi\\Documents\\Kuliah\\UTS.csv", row.names = FALSE)Soal 1
Kategorikan variabel Harga pada dataset di atas menjadi tiga kelompok sebagai berikut:
- \(\text{High} > 12000\)
- \(10000 \le \text{Medium} \le 12000\)
- \(\text{Low} < 10000\)
Tetapkan ke dalam variabel baru yang disebut Kelas dengan menggunakan fungsi kontrol If, else if, dan else.
R
x = Data$Price
# i = for(x in Data$Price){
# if(x > 12000){
# print("High")}
# else if ( x >= 10000 & x <= 12000 ){
# print("Medium")}
# else{print("Low")} }
# fungsi diatas tidak bisa digunakan karena if hanya bisa menggunakan satu input.
a = ifelse((x>12000),print('High'),
ifelse((x >= 10000 & x <= 12000), print("Medium"),print('Low')
))## [1] "High"
## [1] "Medium"
## [1] "Low"Data$Class = a
DataSoal 2
Kategorikan variabel Harga pada dataset di atas menjadi enam kelompok sebagai berikut:
- Booking_fee nya 5 % jika \(\text{Price} < 8000\)
- Booking_fee nya 6 % jika \(8000 \le \text{Price} < 9000\)
- Booking_fee nya 7 % jika \(9000 \le \text{Price} < 10000\)
- Booking_fee nya 8 % jika \(10000 \le \text{Price} < 11000\)
- Booking_fee nya 9 % jika \(11000 \le \text{Price} < 13000\)
- Booking_fee nya 10 % jika \(13000 \le \text{Price} \le 15000\)
Tetapkan ke dalam variabel baru yang disebut Booking_fee dengan menggunakan fungsi kontrol If, else if, dan else.
R
y = Data$Price
b = ifelse((y<8000),5/100,
ifelse((y >= 8000 & y<9000),6/100,
ifelse((y >= 9000 & y< 10000),7/100,
ifelse((y >= 10000 & y< 11000),8/100,
ifelse((y >= 11000 & y< 12000),9/100,10/100))))
)
Data$Booking_Fee = b
DataSoal 3
Menurut kumpulan data akhir yang telah Anda buat pada soal no 2, saya berasumsi bahwa Anda telah bekerja sebagai pemasaran di perusahaan ABC Property, bagaimana Anda dapat mengumpulkan semua informasi tentang penjualan Anda dengan menggunakan pernyataan for.
R
library(DT)
sales ="Sherly"
for(x in sales){
print(subset(Data, subset=(Marketing_Name == x)))
}## Id Marketing_Name Work_Exp City Cluster Price Date_Sales
## 2 2 Sherly 2.4 Jakarta Narada 7404 2020-05-27
## 52 52 Sherly 2.4 Depok Alindra 9462 2020-02-21
## 102 102 Sherly 2.4 Bekasi Primadona 12727 2019-04-12
## 152 152 Sherly 2.4 Jakarta Arana 8999 2018-10-02
## 202 202 Sherly 2.4 Bekasi Asera 10980 2018-10-04
## 252 252 Sherly 2.4 Bekasi Narada 14693 2020-09-17
## 302 302 Sherly 2.4 Depok Narada 8670 2018-03-17
## 352 352 Sherly 2.4 Bekasi Tiara 9498 2019-03-07
## 402 402 Sherly 2.4 Bogor Sweethome 13979 2019-01-17
## 452 452 Sherly 2.4 Jakarta Mutiara 10254 2019-03-10
## 502 502 Sherly 2.4 Bekasi Asera 7794 2019-05-31
## 552 552 Sherly 2.4 Bekasi Alindra 12593 2020-05-02
## 602 602 Sherly 2.4 Tengerang Albasia 13803 2020-06-02
## 652 652 Sherly 2.4 Jakarta Alindra 13548 2019-01-29
## 702 702 Sherly 2.4 Bekasi Tiara 8203 2018-05-12
## 752 752 Sherly 2.4 Jakarta Asoka 7132 2019-05-05
## 802 802 Sherly 2.4 Tengerang Palmyra 12783 2020-09-24
## 852 852 Sherly 2.4 Tengerang Palmyra 12035 2019-07-21
## 902 902 Sherly 2.4 Bogor Asera 10217 2020-04-21
## 952 952 Sherly 2.4 Jakarta Peronia 13561 2018-02-02
## 1002 1002 Sherly 2.4 Tengerang Peronia 14606 2020-04-02
## 1052 1052 Sherly 2.4 Jakarta Asoka 10422 2019-05-25
## 1102 1102 Sherly 2.4 Bogor Teradamai 8663 2018-12-03
## 1152 1152 Sherly 2.4 Jakarta Primadona 13926 2019-12-30
## 1202 1202 Sherly 2.4 Jakarta Victoria 9165 2018-03-03
## 1252 1252 Sherly 2.4 Bekasi Mutiara 14651 2019-11-21
## 1302 1302 Sherly 2.4 Bogor Adara 10257 2018-11-07
## 1352 1352 Sherly 2.4 Bekasi Tiara 9752 2019-04-18
## 1402 1402 Sherly 2.4 Bogor Sweethome 13277 2020-03-27
## 1452 1452 Sherly 2.4 Bogor Sweethome 12245 2019-03-01
## 1502 1502 Sherly 2.4 Tengerang Permata 13837 2019-04-28
## 1552 1552 Sherly 2.4 Depok Narada 13898 2019-11-24
## 1602 1602 Sherly 2.4 Depok Alamanda 14790 2020-07-02
## 1652 1652 Sherly 2.4 Depok Victoria 14855 2019-09-03
## 1702 1702 Sherly 2.4 Tengerang Mutiara 9999 2019-08-27
## 1752 1752 Sherly 2.4 Bogor Alamanda 10606 2018-06-10
## 1802 1802 Sherly 2.4 Tengerang Neon 9076 2019-08-03
## 1852 1852 Sherly 2.4 Depok Victoria 8266 2018-10-04
## 1902 1902 Sherly 2.4 Jakarta Neon 13676 2018-02-01
## 1952 1952 Sherly 2.4 Bekasi Alindra 8787 2018-04-02
## 2002 2002 Sherly 2.4 Bogor Asoka 8608 2020-09-19
## 2052 2052 Sherly 2.4 Depok Palmyra 10236 2020-03-19
## 2102 2102 Sherly 2.4 Bekasi Teradamai 13034 2018-10-17
## 2152 2152 Sherly 2.4 Depok Permata 8094 2019-02-24
## 2202 2202 Sherly 2.4 Bekasi Albasia 9470 2019-10-08
## 2252 2252 Sherly 2.4 Bekasi Mutiara 14181 2019-11-30
## 2302 2302 Sherly 2.4 Depok Peronia 9023 2019-09-25
## 2352 2352 Sherly 2.4 Bekasi Winona 11229 2019-06-02
## 2402 2402 Sherly 2.4 Depok Neon 10636 2020-09-01
## 2452 2452 Sherly 2.4 Depok Victoria 10436 2018-03-21
## 2502 2502 Sherly 2.4 Bekasi Primadona 7461 2019-09-16
## 2552 2552 Sherly 2.4 Bekasi Alindra 8110 2018-09-18
## 2602 2602 Sherly 2.4 Bogor Victoria 10361 2018-01-11
## 2652 2652 Sherly 2.4 Bekasi Albasia 12866 2018-10-05
## 2702 2702 Sherly 2.4 Bekasi Sweethome 12050 2019-01-08
## 2752 2752 Sherly 2.4 Depok Asoka 13200 2020-08-28
## 2802 2802 Sherly 2.4 Depok Mutiara 10261 2019-06-26
## 2852 2852 Sherly 2.4 Tengerang Asoka 13974 2018-12-14
## 2902 2902 Sherly 2.4 Bogor Narada 12223 2020-04-23
## 2952 2952 Sherly 2.4 Depok Mutiara 7257 2018-05-08
## 3002 3002 Sherly 2.4 Tengerang Palmyra 10157 2020-05-22
## 3052 3052 Sherly 2.4 Tengerang Primadona 9486 2019-11-06
## 3102 3102 Sherly 2.4 Jakarta Alamanda 11078 2020-01-29
## 3152 3152 Sherly 2.4 Bogor Asoka 10888 2020-08-08
## 3202 3202 Sherly 2.4 Depok Asoka 9036 2018-08-30
## 3252 3252 Sherly 2.4 Bekasi Tiara 14847 2019-05-23
## 3302 3302 Sherly 2.4 Tengerang Arana 11370 2018-06-17
## 3352 3352 Sherly 2.4 Depok Winona 11339 2020-09-11
## 3402 3402 Sherly 2.4 Depok Adara 9607 2019-01-30
## 3452 3452 Sherly 2.4 Bekasi Neon 8965 2018-06-17
## 3502 3502 Sherly 2.4 Jakarta Alamanda 13274 2018-05-23
## 3552 3552 Sherly 2.4 Bekasi Winona 11290 2019-06-30
## 3602 3602 Sherly 2.4 Jakarta Winona 11537 2018-03-22
## 3652 3652 Sherly 2.4 Tengerang Neon 14762 2018-05-27
## 3702 3702 Sherly 2.4 Tengerang Lavesh 8865 2018-11-15
## 3752 3752 Sherly 2.4 Bekasi Alindra 7166 2020-05-16
## 3802 3802 Sherly 2.4 Depok Mutiara 9881 2020-02-13
## 3852 3852 Sherly 2.4 Jakarta Lavesh 10113 2019-03-19
## 3902 3902 Sherly 2.4 Bogor Mutiara 13107 2020-07-27
## 3952 3952 Sherly 2.4 Bekasi Albasia 7634 2018-10-20
## 4002 4002 Sherly 2.4 Bekasi Adara 12023 2020-06-17
## 4052 4052 Sherly 2.4 Bekasi Primadona 10935 2018-02-23
## 4102 4102 Sherly 2.4 Bogor Asera 10377 2018-06-07
## 4152 4152 Sherly 2.4 Jakarta Adara 8519 2020-02-22
## 4202 4202 Sherly 2.4 Jakarta Victoria 10445 2018-04-05
## 4252 4252 Sherly 2.4 Bogor Arana 9115 2018-02-13
## 4302 4302 Sherly 2.4 Bekasi Asoka 8714 2018-11-09
## 4352 4352 Sherly 2.4 Bekasi Alamanda 7184 2018-04-30
## 4402 4402 Sherly 2.4 Depok Adara 8325 2018-02-17
## 4452 4452 Sherly 2.4 Depok Victoria 14307 2018-09-29
## 4502 4502 Sherly 2.4 Depok Lavesh 7177 2019-09-28
## 4552 4552 Sherly 2.4 Bekasi Neon 13070 2020-08-30
## 4602 4602 Sherly 2.4 Bogor Winona 8047 2018-03-08
## 4652 4652 Sherly 2.4 Depok Asoka 14456 2019-08-26
## 4702 4702 Sherly 2.4 Tengerang Permata 14931 2019-06-08
## 4752 4752 Sherly 2.4 Tengerang Palmyra 8955 2020-07-04
## 4802 4802 Sherly 2.4 Depok Alamanda 8630 2019-12-29
## 4852 4852 Sherly 2.4 Bogor Neon 9439 2018-07-07
## 4902 4902 Sherly 2.4 Tengerang Permata 10038 2019-01-30
## 4952 4952 Sherly 2.4 Tengerang Peronia 13110 2018-10-02
## 5002 5002 Sherly 2.4 Depok Permata 14607 2020-07-14
## 5052 5052 Sherly 2.4 Bogor Neon 8225 2018-10-17
## 5102 5102 Sherly 2.4 Bekasi Palmyra 14282 2019-09-19
## 5152 5152 Sherly 2.4 Jakarta Sweethome 13152 2019-02-16
## 5202 5202 Sherly 2.4 Depok Neon 8131 2019-12-26
## 5252 5252 Sherly 2.4 Tengerang Narada 11935 2020-06-19
## 5302 5302 Sherly 2.4 Jakarta Winona 9776 2020-08-17
## 5352 5352 Sherly 2.4 Tengerang Lavesh 14881 2019-12-04
## 5402 5402 Sherly 2.4 Tengerang Permata 11742 2018-06-10
## 5452 5452 Sherly 2.4 Jakarta Permata 9734 2018-11-19
## 5502 5502 Sherly 2.4 Jakarta Alindra 8222 2019-01-30
## 5552 5552 Sherly 2.4 Depok Tiara 14468 2020-05-25
## 5602 5602 Sherly 2.4 Jakarta Arana 12825 2020-04-07
## 5652 5652 Sherly 2.4 Depok Lavesh 14156 2018-01-30
## 5702 5702 Sherly 2.4 Jakarta Teradamai 7416 2019-06-05
## 5752 5752 Sherly 2.4 Jakarta Mutiara 9015 2018-09-01
## 5802 5802 Sherly 2.4 Bogor Victoria 12259 2020-07-16
## 5852 5852 Sherly 2.4 Tengerang Tiara 13425 2019-09-29
## 5902 5902 Sherly 2.4 Tengerang Teradamai 14886 2018-06-05
## 5952 5952 Sherly 2.4 Tengerang Asoka 9113 2020-04-22
## 6002 6002 Sherly 2.4 Jakarta Neon 10960 2018-01-16
## 6052 6052 Sherly 2.4 Tengerang Arana 11046 2018-06-14
## 6102 6102 Sherly 2.4 Tengerang Asoka 10920 2019-12-08
## 6152 6152 Sherly 2.4 Jakarta Winona 9740 2019-05-18
## 6202 6202 Sherly 2.4 Tengerang Peronia 9677 2018-05-04
## 6252 6252 Sherly 2.4 Depok Peronia 12489 2019-01-25
## 6302 6302 Sherly 2.4 Depok Sweethome 9720 2018-03-22
## 6352 6352 Sherly 2.4 Depok Asera 9067 2019-09-01
## 6402 6402 Sherly 2.4 Jakarta Mutiara 10851 2019-08-31
## 6452 6452 Sherly 2.4 Tengerang Neon 13077 2018-05-25
## 6502 6502 Sherly 2.4 Depok Alamanda 11882 2018-02-08
## 6552 6552 Sherly 2.4 Bogor Palmyra 12948 2018-01-24
## 6602 6602 Sherly 2.4 Depok Arana 8640 2018-04-04
## 6652 6652 Sherly 2.4 Jakarta Arana 8485 2018-06-02
## 6702 6702 Sherly 2.4 Tengerang Asera 8503 2018-03-31
## 6752 6752 Sherly 2.4 Bogor Winona 12223 2019-11-27
## 6802 6802 Sherly 2.4 Bogor Asera 8954 2020-07-06
## 6852 6852 Sherly 2.4 Tengerang Sweethome 13501 2019-02-15
## 6902 6902 Sherly 2.4 Jakarta Sweethome 7204 2019-05-15
## 6952 6952 Sherly 2.4 Tengerang Tiara 12993 2018-07-20
## 7002 7002 Sherly 2.4 Depok Neon 13863 2019-05-25
## 7052 7052 Sherly 2.4 Bekasi Alamanda 13049 2018-06-13
## 7102 7102 Sherly 2.4 Depok Winona 13573 2019-11-11
## 7152 7152 Sherly 2.4 Depok Primadona 12623 2018-04-12
## 7202 7202 Sherly 2.4 Depok Adara 13962 2019-10-03
## 7252 7252 Sherly 2.4 Bekasi Mutiara 8440 2019-10-01
## 7302 7302 Sherly 2.4 Tengerang Narada 8361 2018-02-22
## 7352 7352 Sherly 2.4 Bogor Arana 8001 2020-06-14
## 7402 7402 Sherly 2.4 Bogor Asera 12941 2019-10-09
## 7452 7452 Sherly 2.4 Bogor Asoka 7538 2018-02-23
## 7502 7502 Sherly 2.4 Bekasi Lavesh 8312 2020-07-13
## 7552 7552 Sherly 2.4 Bekasi Winona 10620 2019-12-28
## 7602 7602 Sherly 2.4 Bogor Permata 8740 2019-01-19
## 7652 7652 Sherly 2.4 Bogor Palmyra 11326 2020-09-18
## 7702 7702 Sherly 2.4 Bogor Alamanda 10596 2019-11-04
## 7752 7752 Sherly 2.4 Depok Sweethome 9785 2020-03-14
## 7802 7802 Sherly 2.4 Depok Lavesh 10547 2018-10-21
## 7852 7852 Sherly 2.4 Bekasi Mutiara 10024 2019-04-30
## 7902 7902 Sherly 2.4 Bogor Primadona 13721 2020-02-23
## 7952 7952 Sherly 2.4 Tengerang Sweethome 9398 2020-09-15
## 8002 8002 Sherly 2.4 Jakarta Tiara 12816 2020-01-31
## 8052 8052 Sherly 2.4 Bogor Asoka 14028 2019-12-18
## 8102 8102 Sherly 2.4 Jakarta Arana 7608 2018-05-31
## 8152 8152 Sherly 2.4 Bogor Asoka 8277 2018-12-06
## 8202 8202 Sherly 2.4 Bekasi Palmyra 11673 2018-09-18
## 8252 8252 Sherly 2.4 Depok Peronia 10278 2019-09-14
## 8302 8302 Sherly 2.4 Bekasi Arana 11524 2019-04-25
## 8352 8352 Sherly 2.4 Bekasi Albasia 7669 2018-03-11
## 8402 8402 Sherly 2.4 Bekasi Adara 14748 2018-04-12
## 8452 8452 Sherly 2.4 Depok Arana 13181 2020-02-18
## 8502 8502 Sherly 2.4 Bekasi Teradamai 11919 2020-03-04
## 8552 8552 Sherly 2.4 Jakarta Teradamai 12941 2019-12-08
## 8602 8602 Sherly 2.4 Bekasi Adara 7951 2018-03-18
## 8652 8652 Sherly 2.4 Bekasi Alindra 12869 2020-05-06
## 8702 8702 Sherly 2.4 Bekasi Peronia 13376 2019-12-30
## 8752 8752 Sherly 2.4 Depok Albasia 7254 2018-06-15
## 8802 8802 Sherly 2.4 Depok Adara 11374 2019-01-26
## 8852 8852 Sherly 2.4 Bogor Tiara 13902 2018-08-10
## 8902 8902 Sherly 2.4 Tengerang Primadona 14460 2020-08-25
## 8952 8952 Sherly 2.4 Tengerang Peronia 12839 2019-12-12
## 9002 9002 Sherly 2.4 Jakarta Asera 14985 2018-11-23
## 9052 9052 Sherly 2.4 Bogor Lavesh 8637 2018-02-17
## 9102 9102 Sherly 2.4 Depok Asera 14933 2020-03-03
## 9152 9152 Sherly 2.4 Tengerang Neon 11656 2018-02-18
## 9202 9202 Sherly 2.4 Tengerang Neon 13249 2019-12-01
## 9252 9252 Sherly 2.4 Bekasi Arana 8826 2020-06-26
## 9302 9302 Sherly 2.4 Jakarta Alindra 13626 2019-11-15
## 9352 9352 Sherly 2.4 Tengerang Mutiara 7509 2019-03-17
## 9402 9402 Sherly 2.4 Depok Primadona 11615 2019-06-05
## 9452 9452 Sherly 2.4 Jakarta Permata 10323 2019-07-23
## 9502 9502 Sherly 2.4 Depok Asoka 13068 2019-11-07
## 9552 9552 Sherly 2.4 Jakarta Teradamai 9825 2018-10-05
## 9602 9602 Sherly 2.4 Bogor Arana 13028 2018-10-07
## 9652 9652 Sherly 2.4 Bekasi Albasia 9220 2018-10-17
## 9702 9702 Sherly 2.4 Tengerang Alamanda 8238 2020-06-16
## 9752 9752 Sherly 2.4 Bogor Winona 13356 2019-01-12
## 9802 9802 Sherly 2.4 Depok Asera 14278 2020-04-03
## 9852 9852 Sherly 2.4 Jakarta Primadona 11608 2019-02-05
## 9902 9902 Sherly 2.4 Depok Arana 9779 2019-07-31
## 9952 9952 Sherly 2.4 Tengerang Winona 14321 2018-02-07
## Advertisement Class Booking_Fee
## 2 11 Low 0.05
## 52 15 Low 0.07
## 102 20 High 0.10
## 152 19 Low 0.06
## 202 10 Medium 0.08
## 252 7 High 0.10
## 302 4 Low 0.06
## 352 6 Low 0.07
## 402 7 High 0.10
## 452 12 Medium 0.08
## 502 1 Low 0.05
## 552 16 High 0.10
## 602 10 High 0.10
## 652 8 High 0.10
## 702 16 Low 0.06
## 752 17 Low 0.05
## 802 9 High 0.10
## 852 12 High 0.10
## 902 15 Medium 0.08
## 952 6 High 0.10
## 1002 6 High 0.10
## 1052 2 Medium 0.08
## 1102 10 Low 0.06
## 1152 18 High 0.10
## 1202 10 Low 0.07
## 1252 6 High 0.10
## 1302 7 Medium 0.08
## 1352 14 Low 0.07
## 1402 8 High 0.10
## 1452 3 High 0.10
## 1502 20 High 0.10
## 1552 20 High 0.10
## 1602 9 High 0.10
## 1652 7 High 0.10
## 1702 2 Low 0.07
## 1752 14 Medium 0.08
## 1802 6 Low 0.07
## 1852 14 Low 0.06
## 1902 14 High 0.10
## 1952 4 Low 0.06
## 2002 12 Low 0.06
## 2052 16 Medium 0.08
## 2102 17 High 0.10
## 2152 4 Low 0.06
## 2202 16 Low 0.07
## 2252 17 High 0.10
## 2302 20 Low 0.07
## 2352 11 Medium 0.09
## 2402 4 Medium 0.08
## 2452 17 Medium 0.08
## 2502 18 Low 0.05
## 2552 20 Low 0.06
## 2602 13 Medium 0.08
## 2652 20 High 0.10
## 2702 19 High 0.10
## 2752 3 High 0.10
## 2802 13 Medium 0.08
## 2852 8 High 0.10
## 2902 12 High 0.10
## 2952 8 Low 0.05
## 3002 14 Medium 0.08
## 3052 20 Low 0.07
## 3102 2 Medium 0.09
## 3152 13 Medium 0.08
## 3202 10 Low 0.07
## 3252 5 High 0.10
## 3302 3 Medium 0.09
## 3352 16 Medium 0.09
## 3402 14 Low 0.07
## 3452 16 Low 0.06
## 3502 12 High 0.10
## 3552 3 Medium 0.09
## 3602 15 Medium 0.09
## 3652 5 High 0.10
## 3702 8 Low 0.06
## 3752 7 Low 0.05
## 3802 10 Low 0.07
## 3852 1 Medium 0.08
## 3902 7 High 0.10
## 3952 16 Low 0.05
## 4002 11 High 0.10
## 4052 19 Medium 0.08
## 4102 15 Medium 0.08
## 4152 7 Low 0.06
## 4202 11 Medium 0.08
## 4252 3 Low 0.07
## 4302 14 Low 0.06
## 4352 6 Low 0.05
## 4402 11 Low 0.06
## 4452 3 High 0.10
## 4502 12 Low 0.05
## 4552 9 High 0.10
## 4602 19 Low 0.06
## 4652 10 High 0.10
## 4702 9 High 0.10
## 4752 10 Low 0.06
## 4802 16 Low 0.06
## 4852 6 Low 0.07
## 4902 4 Medium 0.08
## 4952 4 High 0.10
## 5002 14 High 0.10
## 5052 13 Low 0.06
## 5102 3 High 0.10
## 5152 1 High 0.10
## 5202 10 Low 0.06
## 5252 6 Medium 0.09
## 5302 5 Low 0.07
## 5352 5 High 0.10
## 5402 10 Medium 0.09
## 5452 20 Low 0.07
## 5502 20 Low 0.06
## 5552 13 High 0.10
## 5602 10 High 0.10
## 5652 14 High 0.10
## 5702 10 Low 0.05
## 5752 19 Low 0.07
## 5802 5 High 0.10
## 5852 16 High 0.10
## 5902 8 High 0.10
## 5952 5 Low 0.07
## 6002 20 Medium 0.08
## 6052 7 Medium 0.09
## 6102 11 Medium 0.08
## 6152 18 Low 0.07
## 6202 6 Low 0.07
## 6252 6 High 0.10
## 6302 2 Low 0.07
## 6352 15 Low 0.07
## 6402 13 Medium 0.08
## 6452 2 High 0.10
## 6502 3 Medium 0.09
## 6552 20 High 0.10
## 6602 16 Low 0.06
## 6652 6 Low 0.06
## 6702 15 Low 0.06
## 6752 7 High 0.10
## 6802 7 Low 0.06
## 6852 9 High 0.10
## 6902 8 Low 0.05
## 6952 10 High 0.10
## 7002 4 High 0.10
## 7052 18 High 0.10
## 7102 13 High 0.10
## 7152 13 High 0.10
## 7202 16 High 0.10
## 7252 20 Low 0.06
## 7302 5 Low 0.06
## 7352 19 Low 0.06
## 7402 3 High 0.10
## 7452 17 Low 0.05
## 7502 11 Low 0.06
## 7552 11 Medium 0.08
## 7602 14 Low 0.06
## 7652 17 Medium 0.09
## 7702 16 Medium 0.08
## 7752 15 Low 0.07
## 7802 5 Medium 0.08
## 7852 20 Medium 0.08
## 7902 13 High 0.10
## 7952 13 Low 0.07
## 8002 13 High 0.10
## 8052 16 High 0.10
## 8102 10 Low 0.05
## 8152 17 Low 0.06
## 8202 16 Medium 0.09
## 8252 5 Medium 0.08
## 8302 14 Medium 0.09
## 8352 15 Low 0.05
## 8402 16 High 0.10
## 8452 6 High 0.10
## 8502 8 Medium 0.09
## 8552 16 High 0.10
## 8602 20 Low 0.05
## 8652 12 High 0.10
## 8702 13 High 0.10
## 8752 6 Low 0.05
## 8802 11 Medium 0.09
## 8852 9 High 0.10
## 8902 5 High 0.10
## 8952 12 High 0.10
## 9002 16 High 0.10
## 9052 9 Low 0.06
## 9102 4 High 0.10
## 9152 16 Medium 0.09
## 9202 10 High 0.10
## 9252 16 Low 0.06
## 9302 17 High 0.10
## 9352 10 Low 0.05
## 9402 16 Medium 0.09
## 9452 19 Medium 0.08
## 9502 5 High 0.10
## 9552 3 Low 0.07
## 9602 20 High 0.10
## 9652 16 Low 0.07
## 9702 12 Low 0.06
## 9752 18 High 0.10
## 9802 1 High 0.10
## 9852 15 Medium 0.09
## 9902 19 Low 0.07
## 9952 8 High 0.10Soal 4
Jika Anda akan mendapatkan bonus 2% dari Booking fee per unit sebagai pemasaran dan juga mendapatkan bonus tambahan 1% jika Anda telah bekerja di perusahaan ini selama lebih dari 3 tahun. Silakan hitung total bonus dengan menggunakan pernyataan if, for, dan break.
R
#nama sales
sales = "Irene"
r <- subset(Data, subset=(Marketing_Name == sales))
p= ifelse(( r$Work_Exp <3),
(r$Price * r$Booking_Fee ) *(2/100),
(r$Price * r$Booking_Fee ) *(3/100) )
r$Bonus =p
rBonus = sum(r$Bonus)
Bonus## [1] 5659.72Soal 5
Pada bagian ini, Anda diharapkan dapat membuat fungsi yang dapat menjawab setiap penyataan dibawah ini dengan melibatkan setiap fungsi kontrol yang dipelajari pada pertemuan 7.
- Siapa nama marketing pemasaran terbaik?
- Kota dan Cluster mana yang paling menguntungkan?
- Hitung total biaya iklan Anda, jika Anda harus membayarnya $4 setiap kali iklan.
- Hitung rata-rata biaya iklan untuk setiap marketing di Perusahaan tersebut.
- Hitung Total Pendapatan (dalam Bulanan)
R
# marketing no 1
ssales = c("Angel","Sherly","Vanessa","Irene","Julian",
"Jeffry","Nikita","Kefas","Siana","Lala",
"Fallen","Ardifo","Kevin","Juen","Jerrel",
"Imelda","Widi","Theodora","Elvani","Jonathan",
"Sofia","Abraham","Siti","Niko","Sefli",
"Bene", "Diana", "Pupe", "Andi", "Tatha",
"Endri", "Monika", "Hans", "Debora","Hanifa",
"James", "Jihan", "Friska","Ardiwan", "Bakti",
"Anthon","Amry", "Wiwik", "Bastian", "Budi",
"Leo","Simon","Matius","Arry", "Eliando")
Jonathan = subset(Data, subset=(Marketing_Name == "Jonathan"))
Theodora = subset(Data, subset=(Marketing_Name == "Theodora"))
Angel = subset(Data, subset=(Marketing_Name == "Angel"))
Sherly=subset(Data, subset=(Marketing_Name == "Sherly"))
Vanessa=subset(Data, subset=(Marketing_Name == "Vanessa"))
Irene=subset(Data, subset=(Marketing_Name == "Irene"))
Julian=subset(Data, subset=(Marketing_Name == "Julian"))
Jeffry =subset(Data, subset=(Marketing_Name == "Jeffry"))
Nikita =subset(Data, subset=(Marketing_Name == "Nikita"))
Kefas =subset(Data, subset=(Marketing_Name == "Kefas"))
Siana =subset(Data, subset=(Marketing_Name == "Siana"))
Lala=subset(Data, subset=(Marketing_Name == "Lala"))
Fallen =subset(Data, subset=(Marketing_Name == "Fallen"))
Ardifo =subset(Data, subset=(Marketing_Name == "Ardifo"))
Kevin =subset(Data, subset=(Marketing_Name == "Kevin"))
Juen =subset(Data, subset=(Marketing_Name == "Juen"))
Jerrel =subset(Data, subset=(Marketing_Name == "Jerrel"))
Imelda =subset(Data, subset=(Marketing_Name == "Imelda"))
Widi =subset(Data, subset=(Marketing_Name == "Widi"))
Theodor = subset(Data, subset=(Marketing_Name == "Theodor"))
Elvani =subset(Data, subset=(Marketing_Name == "Elvani"))
Jonatha = subset(Data, subset=(Marketing_Name == "Jonatha"))
Sofia =subset(Data, subset=(Marketing_Name == "Sofia"))
Abraham = subset(Data, subset=(Marketing_Name == "Abraham"))
Siti =subset(Data, subset=(Marketing_Name == "Siti"))
Niko =subset(Data, subset=(Marketing_Name == "Niko"))
Sefli =subset(Data, subset=(Marketing_Name == "Sefli"))
Bene =subset(Data, subset=(Marketing_Name == "Bene"))
Diana =subset(Data, subset=(Marketing_Name == "Diana"))
Pupe =subset(Data, subset=(Marketing_Name == "Pupe"))
Andi =subset(Data, subset=(Marketing_Name == "Andi"))
Tatha =subset(Data, subset=(Marketing_Name == "Tatha"))
Endri=subset(Data, subset=(Marketing_Name == "Endri"))
Monika= subset(Data, subset=(Marketing_Name == "Monika"))
Hans =subset(Data, subset=(Marketing_Name == "Hans"))
Debora= subset(Data, subset=(Marketing_Name == "Debora"))
Hanifa= subset(Data, subset=(Marketing_Name == "Hanifa"))
James =subset(Data, subset=(Marketing_Name == "James"))
Jihan =subset(Data, subset=(Marketing_Name == "Jihan"))
Friska =subset(Data, subset=(Marketing_Name == "Friska"))
Ardiwan = subset(Data, subset=(Marketing_Name == "Ardiwan"))
Bakti =subset(Data, subset=(Marketing_Name == "Bakti"))
Anthon =subset(Data, subset=(Marketing_Name == "Anthon"))
Amry =subset(Data, subset=(Marketing_Name == "Amry"))
Wiwik =subset(Data, subset=(Marketing_Name == "Wiwik"))
Bastian = subset(Data, subset=(Marketing_Name == "Bastian"))
Budi = subset(Data, subset=(Marketing_Name == "Budi"))
Leo = subset(Data, subset=(Marketing_Name == "Leo"))
Simon = subset(Data, subset=(Marketing_Name == "Simon"))
Matius = subset(Data, subset=(Marketing_Name == "Matius"))
Arry = subset(Data, subset=(Marketing_Name == "Arry"))
Eliando = subset(Data, subset=(Marketing_Name == "Eliando"))
total =
c(sum(Angel$Price),sum(Sherly$Price),sum(Vanessa$Price),sum(Irene$Price),
sum(Julian$Price),sum(Jeffry$Price),sum(Nikita$Price),sum(Kefas$Price),
sum(Siana$Price),sum(Lala$Price),sum(Fallen$Price),sum(Ardifo$Price),
sum(Kevin$Price),sum(Juen$Price),sum(Jerrel$Price),sum(Imelda$Price),
sum(Widi$Price),sum(Theodora$Price),sum(Elvani$Price),sum(Jonathan$Price),
sum(Sofia$Price),sum(Abraham$Price),sum(Siti$Price),sum(Niko$Price),
sum(Sefli$Price),sum(Bene$Price),sum( Diana$Price),sum( Pupe$Price),
sum(Andi$Price),sum( Tatha$Price),sum(Endri$Price),sum( Monika$Price),
sum( Hans$Price),sum( Debora$Price),sum(Hanifa$Price),sum(James$Price),
sum( Jihan$Price),sum( Friska$Price),sum(Ardiwan$Price),sum(Bakti$Price),
sum(Anthon$Price),sum(Amry$Price),sum( Wiwik$Price),sum( Bastian$Price),
sum( Budi$Price),sum(Leo$Price),sum(Simon$Price),sum(Matius$Price),
sum(Arry$Price),sum( Eliando$Price))
marketing_ = data.frame(ssales,
total)
marketing_terbaik =which.max(marketing_$total)
yang_terbaik = marketing_[terbaik,]# nomor 2 kota dan cluster
city = c("Jakarta","Bogor", "Tangerang","Depok","Bekasi")
Jakarta = subset(Data, subset=(City == "Jakarta"))
Bogor = subset(Data, subset=(City == "Bogor"))
Tangerang = subset(Data, subset=(City == "Tengerang"))
Depok = subset(Data, subset=(City == "Depok"))
Bekasi = subset(Data, subset=(City == "Bekasi"))
# kota_wing = for (x in hi){
# kt = subset(r, subset=(City == x))
# harga = sum(kt$Price)
# panjang = length(kt$Id)
# print(c(cat(harga/panjang,(cat(x,"rata di " )))))
#
# }
rata_city = c(
sum(Jakarta$Price)/length(Jakarta$Id), sum(Bogor$Price)/length(Bogor$Id), sum(Tangerang$Price)/length(Tangerang$Id) ,sum(Depok$Price)/length(Depok$Id) , sum(Bekasi$Price)/length(Bekasi$Id)
)
kota= data.frame(city,
rata_city)
kotay = which.max(kota$rata_city)
kota_menguntungkan = kota[y,]
kota_menguntungkancluster = c("Victoria","Palmyra","Winona","Tiara", "Narada",
"Peronia","Lavesh","Alindra","Sweethome", "Asera",
"Teradamai","Albasia", "Adara","Neon","Arana",
"Asoka", "Primadona", "Mutiara","Permata","Alamanda")
Teradamai = subset(Data, subset=( Cluster == "Teradamai"))
Victoria = subset(Data, subset=( Cluster == "Victoria"))
Palmyra = subset(Data, subset=( Cluster == "Palmyra"))
Winona = subset(Data, subset=( Cluster == "Winona"))
Tiara = subset(Data, subset=( Cluster == "Tiara"))
Narada = subset(Data, subset=( Cluster == "Narada"))
Peronia = subset(Data, subset=( Cluster == "Peronia"))
Lavesh = subset(Data, subset=( Cluster == "Lavesh"))
Alindra = subset(Data, subset=( Cluster == "Alindra"))
Sweethome = subset(Data, subset=( Cluster == "Sweethome"))
Asera = subset(Data, subset=( Cluster == "Asera"))
Teradamai = subset(Data, subset=( Cluster == "Teradamai"))
Albasia = subset(Data, subset=( Cluster == "Albasia"))
Adara = subset(Data, subset=( Cluster == "Adara"))
Neon = subset(Data, subset=( Cluster == "Neon"))
Arana = subset(Data, subset=( Cluster == "Arana"))
Asoka = subset(Data, subset=( Cluster == "Asoka"))
Primadona = subset(Data, subset=( Cluster == "Primadona"))
Mutiara = subset(Data, subset=( Cluster == "Mutiara"))
Permata = subset(Data, subset=( Cluster == "Permata"))
Alamanda = subset(Data, subset=( Cluster == "Alamanda"))
rata = c(sum(Victoria$Price)/length(Victoria$Id),sum(Palmyra$Price)/length(Palmyra$Id),
sum(Winona$Price)/length(Winona$Id),sum(Tiara$Price)/length(Tiara$Id),
sum(Narada$Price)/length(Narada$Id),sum(Peronia$Price)/length(Peronia$Id),
sum(Lavesh$Price)/length( Lavesh$Id), sum(Alindra$Price)/length( Alindra$Id),
sum(Sweethome$Price)/length( Sweethome$Id), sum(Asera$Price)/length( Asera$Id),
sum(Teradamai$Price)/length( Teradamai$Id), sum(Albasia$Price)/length( Albasia$Id),
sum(Adara$Price)/length( Adara$Id), sum(Neon$Price)/length( Neon$Id),
sum(Arana$Price)/length( Arana$Id), sum(Asoka$Price)/length( Asoka$Id),
sum(Primadona$Price)/length( Primadona$Id), sum(Mutiara$Price)/length( Mutiara$Id),
sum(Permata$Price)/length( Permata$Id),sum(Alamanda$Price)/length( Alamanda$Id))
ter_cluster = data.frame(cluster,
rata)
terbaik_cluster = ter_cluster[which.max(ter_cluster$rata),]# biaya iklan sales
sales = "Irene"
table_sales = subset(Data, subset=(Marketing_Name == sales))
Ads = ( table_sales$Advertisement * 4)
Total_Ads = print(sum(Ads))## [1] 8704iklan_sales =c(sales, unlist(Total_Ads))
# rata-rata biaya iklan perbulan setiap marketing
ssales = c("Angel","Sherly","Vanessa","Irene","Julian",
"Jeffry","Nikita","Kefas","Siana","Lala",
"Fallen","Ardifo","Kevin","Juen","Jerrel",
"Imelda","Widi","Theodora","Elvani","Jonathan",
"Sofia","Abraham","Siti","Niko","Sefli",
"Bene", "Diana", "Pupe", "Andi", "Tatha",
"Endri", "Monika", "Hans", "Debora","Hanifa",
"James", "Jihan", "Friska","Ardiwan", "Bakti",
"Anthon","Amry", "Wiwik", "Bastian", "Budi",
"Leo","Simon","Matius","Arry", "Eliando")
# bagian 5
pendapatan_total = sum(Data$Price)-(sum(Data$Advertisement)*4)
nomor_lima = function(x){
print(iklan_sales)
print(c(cat("sales yang terbaik adalah", unlist(yang_terbaik),"\n",
"cluster yang menguntungkan", unlist(terbaik_cluster),"\n",
"kota yang menguntungkan ",unlist(kota_menguntungkan),"\n",
"total pendapatan ABC property adalah", pendapatan_total)))
Ads_Karyawan = for (x in ssales){
ts = subset(Data, subset=(Marketing_Name == x))
Ads = sum(ts$Advertisement * 4)
print(cat(sum(Ads)/length(ts$Id),(cat(x," rata-rata adsnya " ))))}
}
nomor_lima(x)## [1] "Irene" "8704"
## sales yang terbaik adalah Ardifo 2274375
## cluster yang menguntungkan Palmyra 11191.1976744186
## kota yang menguntungkan Bekasi 11079.1914572864
## total pendapatan ABC property adalah 110062868NULL
## Angel rata-rata adsnya 41.1NULL
## Sherly rata-rata adsnya 44.8NULL
## Vanessa rata-rata adsnya 44.06NULL
## Irene rata-rata adsnya 43.52NULL
## Julian rata-rata adsnya 42.42NULL
## Jeffry rata-rata adsnya 41.8NULL
## Nikita rata-rata adsnya 41.1NULL
## Kefas rata-rata adsnya 38.8NULL
## Siana rata-rata adsnya 42.46NULL
## Lala rata-rata adsnya 43.18NULL
## Fallen rata-rata adsnya 40.74NULL
## Ardifo rata-rata adsnya 42.14NULL
## Kevin rata-rata adsnya 42.16NULL
## Juen rata-rata adsnya 41.1NULL
## Jerrel rata-rata adsnya 41.26NULL
## Imelda rata-rata adsnya 40.6NULL
## Widi rata-rata adsnya 38.22NULL
## Theodora rata-rata adsnya 42.74NULL
## Elvani rata-rata adsnya 41.66NULL
## Jonathan rata-rata adsnya 43.54NULL
## Sofia rata-rata adsnya 42.3NULL
## Abraham rata-rata adsnya 43.46NULL
## Siti rata-rata adsnya 38.92NULL
## Niko rata-rata adsnya 41.14NULL
## Sefli rata-rata adsnya 40.06NULL
## Bene rata-rata adsnya 43.58NULL
## Diana rata-rata adsnya 43.5NULL
## Pupe rata-rata adsnya 40.18NULL
## Andi rata-rata adsnya 40.54NULL
## Tatha rata-rata adsnya 42.22NULL
## Endri rata-rata adsnya 41.64NULL
## Monika rata-rata adsnya 41.36NULL
## Hans rata-rata adsnya 43.36NULL
## Debora rata-rata adsnya 41.12NULL
## Hanifa rata-rata adsnya 41.28NULL
## James rata-rata adsnya 44.94NULL
## Jihan rata-rata adsnya 42.62NULL
## Friska rata-rata adsnya 40.02NULL
## Ardiwan rata-rata adsnya 43.36NULL
## Bakti rata-rata adsnya 43.18NULL
## Anthon rata-rata adsnya 42.34NULL
## Amry rata-rata adsnya 43NULL
## Wiwik rata-rata adsnya 41.16NULL
## Bastian rata-rata adsnya 43.02NULL
## Budi rata-rata adsnya 41.5NULL
## Leo rata-rata adsnya 42.28NULL
## Simon rata-rata adsnya 44.8NULL
## Matius rata-rata adsnya 40.72NULL
## Arry rata-rata adsnya 41.8NULL
## Eliando rata-rata adsnya 45.62NULLKasus 2
Misalkan Anda memiliki proyek riset pasar untuk mempertahankan beberapa pelanggan potensial di perusahaan Anda. Mari kita asumsikan Anda bekerja di perusahaan asuransi ABC. Untuk melakukannya, Anda ingin mengumpulkan kumpulan data berikut:
- Marital_Status : menetapkan status perkawinan acak (“Ya”, “Tidak”)
- Address : berikan alamat acak (JABODETABEK)
- Work_Location : menetapkan lokasi kerja secara acak (JABODETABEK)
- Age : menetapkan urutan angka acak (dari 19 hingga 60)
- Academic : menetapkan tingkat akademik acak (“J.School”, “H.School”, “Sarjana”, “Magister”, “Phd”)
- Job : 10 pekerjaan acak untuk setiap tingkat akademik
- Grade : 5 nilai acak untuk setiap Pekerjaan
- Income : tetapkan pendapatan yang mungkin untuk setiap Pekerjaan
- Spending : tetapkan kemungkinan pengeluaran untuk setiap Pekerjaan
- Number_of_children: menetapkan nomor acak di antara 0 dan 10 (sesuai dengan status perkawinan)
- Private_vehicle : menetapkan kemungkinan kendaraan pribadi untuk setiap orang (“Mobil”, “sepeda motor”, “Umum”)
- Home : “Sewa”, “Milik”, “Kredit”
Soal 1
Tolong berikan saya kumpulan data tentang informasi 50000 pelanggan yang mengacu pada setiap variabel di atas!
R
No <- (1:50000)
Merital_Status <- sample(c("Yes","No"), 50000, replace = T)
Address <- sample(c("Jakarta","Bogor","Depok","Tangerang","Bekasi"),50000, replace = T)
Work_Location <- sample(c("Jakarta","Bogor","Depok","Tangerang","Bekasi"), 50000, replace=T)
Age <- sample(c(19:60),50000, replace = T)
Grade = sample(c("A","B","C","D","E"), 50000, replace=T)
Private_vehicle = sample(c("Car","Motorcycle","Public"), 50000, replace =T)
Home = sample( c("Rent", "Own", "Credit"), 50000, replace = T)
Case <- data.frame(No,
Merital_Status,
Address,
Work_Location,
Age,
Grade,
Private_vehicle,
Home)
Caseadult =c("Engineer","Doctor","Dentist","Programmer","Athlete","Judge","Lawyer","Teacher","Chef")Academic = ifelse(Case$Age < 20, "J.School",
ifelse(Case$Age < 25, "H.School",
sample(c( "Sarjana", "Magister", "Phd"),replace = T)))
# Job = ifelse(Case$Academic == "J.School","Student",
# ifelse(Case$Academic == "H.School","Student",
# sample(adult, replace =T)))
Job = ifelse(Academic == "J.School","Student",
ifelse(Academic == "H.School","Student",
sample(adult, replace =T)))
Case <- data.frame(No,
Merital_Status,
Address,
Work_Location,
Age,
Grade,
Private_vehicle,
Home,
Academic,
Job)
Income = ifelse(Case$Job =="Engineer", 10000,
ifelse(Case$Job =="Doctor", 35000,
ifelse(Case$Job =="Dentist", 40000,
ifelse(Case$Job =="Programmer", 27000,
ifelse(Case$Job =="Athlete", 32000,
ifelse(Case$Job =="Judge", 25000,
ifelse(Case$Job =="Lawyer", 13000,
ifelse(Case$Job=="Teacher", 7000,
ifelse(Case$Job =="Chef", 9000,1000
)))))))))
Spending =ifelse(Case$Job =="Engineer", 75000,
ifelse(Case$Job =="Doctor", 27890,
ifelse(Case$Job =="Dentist", 3200,
ifelse(Case$Job =="Programmer", 14750,
ifelse(Case$Job =="Athlete", 20000,
ifelse(Case$Job =="Judge", 3200,
ifelse(Case$Job =="Lawyer", 4000,
ifelse(Case$Job =="Teacher", 2500,
ifelse(Case$Job =="Chef", 2300,500
)))))))))
Case$Income =Income
Case$Spending =Spending
Number_of_children = ifelse(Case$Merital_Status == "Yes", sample( c(0:10),25000,replace =T ), 0)
Case$Number_of_Children = Number_of_children
CaseSoal 2
Ringkasan Statistik penting seperti apa yang bisa Anda dapatkan dari kumpulan data Anda?
R
Case_Number = function (x)
{
min = min(x)
max = max(x)
median = x[median.default(x)]
modus =x[which.max(x)]
rata = round(sum(x)/length(x), digits=2)
varians = sum((x-rata)^2) / (length(x)-1)
sd = sqrt(sum((x-rata)^2) / (length(x)-1))
return(cat(c("minimum =", min,"\n",
"maksimum =", max,"\n",
"median =",median,"\n",
"modus =", modus,"\n",
"rata-rata =", rata,"\n",
"varians = ", varians,"\n",
"standar deviasi =", sd,"\n"
)))}
# Case_Summary(pilih tabel yang mana yang ingin di summary)
# Case_Number(Case$Age)# summary
Case_Number(Case$Age)## minimum = 19
## maksimum = 60
## median = 53
## modus = 60
## rata-rata = 39.4
## varians = 146.182307646153
## standar deviasi = 12.0905875641407Case_Number(Case$Income)## minimum = 1000
## maksimum = 40000
## median = 25000
## modus = 40000
## rata-rata = 16879.3
## varians = 155977851.067021
## standar deviasi = 12489.1092983856Case_Number(Case$Spending)## minimum = 500
## maksimum = 14750
## median = 2500
## modus = 14750
## rata-rata = 5821.88
## varians = 32425119.4430289
## standar deviasi = 5694.30587894862Case_Number(Case$Number_of_Children)## minimum = 0
## maksimum = 10
## median =
## modus = 10
## rata-rata = 2.51
## varians = 11.2491837836757
## standar deviasi = 3.35398028969695# Data terdiri dari
# data yang mana saja gapapa karena pada dataframe semua baris sama
length(Case$Merital_Status)## [1] 50000#tipe data dibawah ini adalah
typeof(Case$Merital_Status)## [1] "character"typeof(Case$Address)## [1] "character"typeof(Case$Work_Location)## [1] "character"typeof(Case$Grade)## [1] "character"typeof(Case$Private_vehicle)## [1] "character"typeof(Case$Home)## [1] "character"typeof(Case$Academic)## [1] "character"typeof(Case$Job)## [1] "character"Soal 3
Menurut perhitungan dan analisis Anda, pelanggan mana yang potensial untuk Anda pertahankan?
R
# tabel yang digunakan
yes = subset(Case, subset=( Merital_Status == "Yes"))
no = subset(Case, subset=( Merital_Status == "No"))
a = subset(Case, subset=( Grade == "A"))
b = subset(Case, subset=( Grade == "B"))
c = subset(Case, subset=( Grade == "C"))
d = subset(Case, subset=( Grade == "D"))
e = subset(Case, subset=( Grade == "E"))
aJa =subset(Case, subset=( Address == "Jakarta"))
aBo =subset(Case, subset=( Address == "Bogor"))
aDe =subset(Case, subset=( Address == "Depok"))
aTa =subset(Case, subset=( Address == "Tangerang"))
aBek =subset(Case, subset=( Address == "Bekasi"))
kJa =subset(Case, subset=( Work_Location == "Jakarta"))
kBo =subset(Case, subset=( Work_Location == "Bogor"))
kDe =subset(Case, subset=( Work_Location == "Depok"))
kTa =subset(Case, subset=( Work_Location == "Tangerang"))
kBek =subset(Case, subset=( Work_Location == "Bekasi"))
J = subset(Case, subset=( Academic == "J.School"))
H = subset(Case, subset=( Academic == "H.School"))
S = subset(Case, subset=( Academic == "Sarjana"))
M = subset(Case, subset=( Academic == "Magister"))
P = subset(Case, subset=(Academic== "Phd"))
mobil = subset(Case, subset=( Private_vehicle == "Car"))
motor = subset(Case, subset=( Private_vehicle == "Motorcycle"))
publik= subset(Case, subset=( Private_vehicle == "Public"))
sewa = subset(Case, subset=( Home == "Rent"))
milik = subset(Case, subset=( Home == "Own"))
kredit= subset(Case, subset=( Home == "Credit"))
siswa = subset(Case, subset=( Job == "Student"))
teknik = subset(Case, subset=( Job == "Engineer"))
dokter = subset(Case, subset=( Job == "Doctor"))
dentist = subset(Case, subset=( Job == "Dentist"))
pro = subset(Case, subset=( Job == "Programmer"))
atlet = subset(Case, subset=( Job == "Athlete"))
hakim = subset(Case, subset=( Job == "Judge"))
pengacara = subset(Case, subset=( Job == "Lawyer"))
guru = subset(Case, subset=( Job == "Teacher"))
chef = subset(Case, subset=( Job == "Chef"))Case_Word = function(x){
merital = function(x,y){
if(x>y){
print("Yes")}
else{print("No")} }
marrige = merital(length(yes$No), length(no$No))
alamat =
function(a,b,c,d,e){
if(a> max(c(b,c,d,e))){
print("Jakarta")}
else if(b > max(c(a,c,d,e))){
print("Bogor")}
else if(c > max(c(a,b,d,e))){
print("Depok")}
else if(d > max(c(a,b,c,e))){
print("Tangerang")}
else{print("Bekasi")}
}
address = alamat(length(aJa$No), length(aBo$No), length(aDe$No), length(aTa$No), length(aBek$No))
kerja =
function(a,b,c,d,e){
if(a> max(c(b,c,d,e))){
print("Jakarta")}
else if(b > max(c(a,c,d,e))){
print("Bogor")}
else if(c > max(c(a,b,d,e))){
print("Depok")}
else if(d > max(c(a,b,c,e))){
print("Tangerang")}
else{print("Bekasi")}}
work = kerja(length(kJa$No), length(kBo$No), length(kDe$No), length(kTa$No), length(kBek$No))
akademis =
function(a,b,c,d,e){
if(a> max(c(b,c,d,e))){
print("J.School")}
else if(b > max(c(a,c,d,e))){
print("H.School")}
else if(c > max(c(a,b,d,e))){
print("Sarjana")}
else if(d > max(c(a,b,c,e))){
print("Magister")}
else{print("Phd")}}
academic = akademis(length(J$No), length(H$No), length(S$No),
length(M$No), length(P$No))
nilai = function(a,b,c,d,e){
if(a> max(c(b,c,d,e))){
print("A")}
else if(b > max(c(a,c,d,e))){
print("B")}
else if(c > max(c(a,b,d,e))){
print("C")}
else if(d > max(c(a,b,c,e))){
print("D")}
else{print("E")}
}
ga = length(a$Address)
gb = length(b$No)
gc = length(c$No)
gd = length(d$No)
ge = length(e$No)
grade = nilai(ga,gb,gc,gd,ge)
vehicle =
function(a,b,c){
if(a> max(c(b,c))){
print("Mobil")}
else if(b > max(c(a,c))){
print("Motor")}
else {print("publik")} }
kendaraan = vehicle(length(mobil$No), length(motor$No), length(publik$No))
rumah = function(a,b,c){
if(a> max(c(b,c))){
print("sewa")}
else if(b > max(c(a,c))){
print("milik")}
else {print("kredit")} }
home = rumah(length(sewa$No), length(milik$No), length(kredit$No))
pekerjaan = function(a,b,c,d,e,f,g,h,i,j){
if(a> max(c(b,c,d,e,f,g,h,i,j))){
print("siswa")}
else if(b > max(c(a,c,d,e,f,g,h,i,j))){
print("teknik")}
else if(c > max(c(a,b,d,e,f,g,h,i,j))){
print("dokter")}
else if(d > max(c(a,b,c,e,f,g,h,i,j))){
print("dentist")}
else if(e > max(c(a,b,c,d,f,g,h,i,j))){
print("programmer")}
else if(f > max(c(a,b,c,d,e,g,h,i,j))){
print("atlet")}
else if(g > max(c(a,b,c,d,e,f,h,i,j))){
print("hakim")}
else if(h > max(c(a,b,c,d,e,f,g,i,j))){
print("pengacara")}
else if(i > max(c(a,b,c,d,e,f,g,h,j))){
print("guru")}
else{print("chef")}}
job = pekerjaan(length(siswa$No), length(teknik$No), length(dokter$No),
length(dentist$No),length(pro$No),length(atlet$No),
length(hakim$No), length(pengacara$No), length(guru$No),
length(chef$No))
return(cat(c("marrige", marrige,"\n",
"alamat", address,"\n",
"kerja",work,"\n",
"akademis",academic,"\n",
"grade", grade,"\n",
"kendaraan",kendaraan,"\n",
"rumah",home,"\n",
"pekerjaan",job
)))
}#pelanggan yang potensial yang akan saya pertahankan adalah dengan kategori seperti di bawah.
# "yang memiiki "
Case_Word(x)## [1] "Yes"
## [1] "Bekasi"
## [1] "Bogor"
## [1] "Sarjana"
## [1] "E"
## [1] "publik"
## [1] "kredit"
## [1] "guru"
## marrige Yes
## alamat Bekasi
## kerja Bogor
## akademis Sarjana
## grade E
## kendaraan publik
## rumah kredit
## pekerjaan guru#"memiliki umur rata-rata yang"
Case_Number(Case$Age)## minimum = 19
## maksimum = 60
## median = 53
## modus = 60
## rata-rata = 39.4
## varians = 146.182307646153
## standar deviasi = 12.0905875641407#"income rata-rata yang"
Case_Number(Case$Income)## minimum = 1000
## maksimum = 40000
## median = 25000
## modus = 40000
## rata-rata = 16879.3
## varians = 155977851.067021
## standar deviasi = 12489.1092983856#"yang rata-rata pengeluarannya"
Case_Number(Case$Spending)## minimum = 500
## maksimum = 14750
## median = 2500
## modus = 14750
## rata-rata = 5821.88
## varians = 32425119.4430289
## standar deviasi = 5694.30587894862#"yang memiliki rata-rata anak"
Case_Number(Case$Number_of_Children)## minimum = 0
## maksimum = 10
## median =
## modus = 10
## rata-rata = 2.51
## varians = 11.2491837836757
## standar deviasi = 3.35398028969695