Algoritma & Struktur Data
~ Ujian Tengah Semester ~
| Kontak | : \(\downarrow\) |
| mugemisausan05@gmail.com | |
| https://www.instagram.com/saram.05/ | |
| RPubs | https://rpubs.com/sausanramadhani/ |
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\\Public\\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
Data$Kelas<-ifelse(Data$Price > 12000,
"High",
ifelse(Data$Price >=10000 & Data$ Price <=12000,
"Medium",
ifelse(Data$Price <10000,
"Low",0)))
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
x<-Data$Price
Data$Booking_fee<-ifelse(x < 8000,
x*5/100,
ifelse(x >=8000 & x <9000,
x*6/100,
ifelse(x >=9000 & x <10000,
x*7/100,
ifelse(x >=10000 & x <11000,
x*8/100,
ifelse(x >=11000 & x <13000,
x*9/100,
ifelse(x >=13000 & x <=15000,
x*10/100, 0))))))
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
sales ="Sherly"
z = for(i in sales){
print(subset(Data,
subset=(Marketing_Name == i)))
}## Id Marketing_Name Work_Exp City Cluster Price Date_Sales
## 2 2 Sherly 2.4 Tengerang Permata 14740 2020-06-06
## 52 52 Sherly 2.4 Tengerang Sweethome 9730 2019-09-28
## 102 102 Sherly 2.4 Bogor Victoria 7667 2018-12-27
## 152 152 Sherly 2.4 Bogor Winona 8210 2019-03-13
## 202 202 Sherly 2.4 Bogor Adara 12719 2019-03-05
## 252 252 Sherly 2.4 Depok Alamanda 14040 2018-09-09
## 302 302 Sherly 2.4 Bekasi Adara 12516 2019-08-20
## 352 352 Sherly 2.4 Depok Lavesh 8801 2018-08-12
## 402 402 Sherly 2.4 Jakarta Mutiara 9327 2019-06-19
## 452 452 Sherly 2.4 Bogor Tiara 14741 2019-11-30
## 502 502 Sherly 2.4 Bogor Narada 12531 2019-08-26
## 552 552 Sherly 2.4 Bekasi Narada 10383 2019-01-10
## 602 602 Sherly 2.4 Depok Palmyra 10082 2019-06-27
## 652 652 Sherly 2.4 Bogor Palmyra 14415 2019-10-07
## 702 702 Sherly 2.4 Bogor Asera 9099 2020-06-16
## 752 752 Sherly 2.4 Tengerang Teradamai 12682 2019-12-22
## 802 802 Sherly 2.4 Tengerang Asoka 12405 2019-03-30
## 852 852 Sherly 2.4 Bekasi Arana 11227 2020-04-07
## 902 902 Sherly 2.4 Bogor Adara 7844 2018-07-22
## 952 952 Sherly 2.4 Bogor Asoka 9411 2019-09-10
## 1002 1002 Sherly 2.4 Jakarta Teradamai 14086 2019-03-21
## 1052 1052 Sherly 2.4 Depok Mutiara 8313 2020-09-07
## 1102 1102 Sherly 2.4 Tengerang Alindra 8175 2020-04-30
## 1152 1152 Sherly 2.4 Bogor Alamanda 12830 2018-08-03
## 1202 1202 Sherly 2.4 Bekasi Tiara 9274 2020-01-27
## 1252 1252 Sherly 2.4 Jakarta Teradamai 10224 2019-02-12
## 1302 1302 Sherly 2.4 Depok Narada 9242 2019-08-04
## 1352 1352 Sherly 2.4 Bogor Mutiara 11509 2019-01-27
## 1402 1402 Sherly 2.4 Tengerang Tiara 12003 2018-08-26
## 1452 1452 Sherly 2.4 Bogor Permata 11621 2020-07-31
## 1502 1502 Sherly 2.4 Bekasi Peronia 13599 2018-08-31
## 1552 1552 Sherly 2.4 Depok Peronia 8320 2019-01-30
## 1602 1602 Sherly 2.4 Depok Peronia 7543 2018-05-17
## 1652 1652 Sherly 2.4 Bekasi Adara 10639 2019-10-29
## 1702 1702 Sherly 2.4 Tengerang Tiara 7448 2018-06-20
## 1752 1752 Sherly 2.4 Jakarta Victoria 9209 2019-05-31
## 1802 1802 Sherly 2.4 Depok Narada 7180 2020-06-09
## 1852 1852 Sherly 2.4 Tengerang Arana 9578 2018-10-14
## 1902 1902 Sherly 2.4 Bogor Peronia 8576 2018-04-12
## 1952 1952 Sherly 2.4 Bekasi Victoria 8406 2018-06-19
## 2002 2002 Sherly 2.4 Bekasi Arana 7263 2018-07-27
## 2052 2052 Sherly 2.4 Depok Mutiara 9158 2019-10-30
## 2102 2102 Sherly 2.4 Jakarta Lavesh 14118 2018-03-21
## 2152 2152 Sherly 2.4 Depok Teradamai 9787 2019-03-09
## 2202 2202 Sherly 2.4 Tengerang Tiara 10665 2018-11-10
## 2252 2252 Sherly 2.4 Bogor Neon 13808 2018-04-07
## 2302 2302 Sherly 2.4 Bogor Asera 13220 2018-04-03
## 2352 2352 Sherly 2.4 Depok Tiara 10843 2019-06-07
## 2402 2402 Sherly 2.4 Tengerang Asoka 9770 2019-06-24
## 2452 2452 Sherly 2.4 Bogor Victoria 11612 2020-06-08
## 2502 2502 Sherly 2.4 Bogor Peronia 9410 2020-04-11
## 2552 2552 Sherly 2.4 Bogor Peronia 9477 2019-03-03
## 2602 2602 Sherly 2.4 Bekasi Palmyra 10081 2018-05-18
## 2652 2652 Sherly 2.4 Jakarta Albasia 11602 2019-05-30
## 2702 2702 Sherly 2.4 Bogor Mutiara 8050 2018-08-10
## 2752 2752 Sherly 2.4 Tengerang Primadona 9863 2020-07-03
## 2802 2802 Sherly 2.4 Tengerang Narada 7625 2020-06-10
## 2852 2852 Sherly 2.4 Depok Mutiara 8147 2019-07-07
## 2902 2902 Sherly 2.4 Bogor Asoka 11142 2018-03-05
## 2952 2952 Sherly 2.4 Bogor Permata 13553 2019-08-05
## 3002 3002 Sherly 2.4 Bogor Alindra 8964 2018-09-05
## 3052 3052 Sherly 2.4 Tengerang Adara 12180 2019-03-08
## 3102 3102 Sherly 2.4 Depok Asoka 11672 2019-11-01
## 3152 3152 Sherly 2.4 Bekasi Palmyra 13846 2018-06-07
## 3202 3202 Sherly 2.4 Tengerang Winona 13338 2020-08-28
## 3252 3252 Sherly 2.4 Depok Palmyra 13345 2020-06-10
## 3302 3302 Sherly 2.4 Jakarta Victoria 10522 2018-07-01
## 3352 3352 Sherly 2.4 Jakarta Mutiara 12974 2018-06-11
## 3402 3402 Sherly 2.4 Depok Palmyra 11755 2018-01-15
## 3452 3452 Sherly 2.4 Bogor Winona 8640 2019-01-14
## 3502 3502 Sherly 2.4 Bekasi Palmyra 12690 2019-10-31
## 3552 3552 Sherly 2.4 Tengerang Winona 10783 2020-01-23
## 3602 3602 Sherly 2.4 Tengerang Tiara 10667 2020-05-26
## 3652 3652 Sherly 2.4 Jakarta Alindra 14351 2019-12-28
## 3702 3702 Sherly 2.4 Tengerang Albasia 9333 2019-06-23
## 3752 3752 Sherly 2.4 Depok Tiara 12222 2020-03-02
## 3802 3802 Sherly 2.4 Depok Narada 8690 2020-05-19
## 3852 3852 Sherly 2.4 Tengerang Neon 11212 2018-06-24
## 3902 3902 Sherly 2.4 Bogor Alindra 11129 2019-09-11
## 3952 3952 Sherly 2.4 Tengerang Permata 8522 2018-10-30
## 4002 4002 Sherly 2.4 Bogor Asera 7799 2019-11-10
## 4052 4052 Sherly 2.4 Jakarta Primadona 14194 2019-03-23
## 4102 4102 Sherly 2.4 Bekasi Adara 8704 2020-01-04
## 4152 4152 Sherly 2.4 Bogor Neon 9550 2019-06-29
## 4202 4202 Sherly 2.4 Tengerang Teradamai 14887 2019-05-20
## 4252 4252 Sherly 2.4 Jakarta Mutiara 13643 2018-09-28
## 4302 4302 Sherly 2.4 Tengerang Sweethome 10622 2019-07-01
## 4352 4352 Sherly 2.4 Bogor Primadona 10250 2020-04-30
## 4402 4402 Sherly 2.4 Bogor Teradamai 8708 2020-05-16
## 4452 4452 Sherly 2.4 Bogor Peronia 14345 2020-04-19
## 4502 4502 Sherly 2.4 Bogor Narada 11448 2018-12-23
## 4552 4552 Sherly 2.4 Tengerang Sweethome 8565 2018-03-21
## 4602 4602 Sherly 2.4 Bekasi Mutiara 8050 2020-02-17
## 4652 4652 Sherly 2.4 Depok Narada 12577 2019-11-10
## 4702 4702 Sherly 2.4 Depok Albasia 10643 2019-01-05
## 4752 4752 Sherly 2.4 Tengerang Permata 8546 2018-10-15
## 4802 4802 Sherly 2.4 Depok Adara 12543 2020-04-04
## 4852 4852 Sherly 2.4 Tengerang Teradamai 7589 2019-06-06
## 4902 4902 Sherly 2.4 Tengerang Primadona 10275 2019-03-19
## 4952 4952 Sherly 2.4 Depok Alamanda 14495 2019-02-10
## 5002 5002 Sherly 2.4 Jakarta Permata 14878 2018-08-29
## 5052 5052 Sherly 2.4 Tengerang Permata 7904 2018-07-29
## 5102 5102 Sherly 2.4 Tengerang Neon 12867 2019-11-13
## 5152 5152 Sherly 2.4 Bogor Primadona 11737 2020-02-24
## 5202 5202 Sherly 2.4 Bekasi Tiara 8904 2018-11-20
## 5252 5252 Sherly 2.4 Jakarta Primadona 13547 2020-01-21
## 5302 5302 Sherly 2.4 Tengerang Primadona 9427 2018-05-14
## 5352 5352 Sherly 2.4 Tengerang Albasia 8160 2019-08-05
## 5402 5402 Sherly 2.4 Tengerang Narada 10947 2018-06-30
## 5452 5452 Sherly 2.4 Bogor Narada 11122 2019-11-01
## 5502 5502 Sherly 2.4 Jakarta Alindra 9601 2018-04-30
## 5552 5552 Sherly 2.4 Bogor Teradamai 9038 2020-07-17
## 5602 5602 Sherly 2.4 Jakarta Victoria 8778 2018-10-24
## 5652 5652 Sherly 2.4 Jakarta Alindra 10051 2019-12-22
## 5702 5702 Sherly 2.4 Bogor Primadona 11091 2019-09-09
## 5752 5752 Sherly 2.4 Jakarta Alamanda 9624 2020-01-08
## 5802 5802 Sherly 2.4 Jakarta Primadona 13524 2018-03-28
## 5852 5852 Sherly 2.4 Jakarta Permata 10219 2018-05-26
## 5902 5902 Sherly 2.4 Depok Lavesh 11237 2020-09-08
## 5952 5952 Sherly 2.4 Bekasi Permata 11752 2020-08-02
## 6002 6002 Sherly 2.4 Jakarta Primadona 13321 2020-08-26
## 6052 6052 Sherly 2.4 Jakarta Asera 11067 2020-06-21
## 6102 6102 Sherly 2.4 Jakarta Permata 13456 2018-12-10
## 6152 6152 Sherly 2.4 Bogor Victoria 11520 2020-08-11
## 6202 6202 Sherly 2.4 Bogor Asera 8913 2018-09-06
## 6252 6252 Sherly 2.4 Bekasi Neon 9449 2019-03-31
## 6302 6302 Sherly 2.4 Bekasi Asera 12765 2019-03-19
## 6352 6352 Sherly 2.4 Bogor Winona 14078 2018-04-18
## 6402 6402 Sherly 2.4 Bogor Adara 7331 2019-09-25
## 6452 6452 Sherly 2.4 Tengerang Winona 7040 2018-01-15
## 6502 6502 Sherly 2.4 Bekasi Albasia 8472 2019-01-25
## 6552 6552 Sherly 2.4 Bekasi Narada 13897 2019-09-14
## 6602 6602 Sherly 2.4 Bogor Mutiara 7475 2018-12-27
## 6652 6652 Sherly 2.4 Depok Albasia 7364 2020-06-28
## 6702 6702 Sherly 2.4 Tengerang Sweethome 9027 2018-06-15
## 6752 6752 Sherly 2.4 Jakarta Lavesh 9877 2020-09-03
## 6802 6802 Sherly 2.4 Bekasi Victoria 12921 2019-11-19
## 6852 6852 Sherly 2.4 Bogor Victoria 13624 2019-05-05
## 6902 6902 Sherly 2.4 Tengerang Alindra 8363 2018-09-05
## 6952 6952 Sherly 2.4 Bogor Winona 13116 2019-07-22
## 7002 7002 Sherly 2.4 Bogor Mutiara 10445 2019-05-31
## 7052 7052 Sherly 2.4 Jakarta Arana 10312 2019-11-27
## 7102 7102 Sherly 2.4 Tengerang Victoria 9022 2020-06-28
## 7152 7152 Sherly 2.4 Depok Arana 8558 2019-06-18
## 7202 7202 Sherly 2.4 Jakarta Adara 10027 2018-08-07
## 7252 7252 Sherly 2.4 Bekasi Victoria 10890 2020-07-10
## 7302 7302 Sherly 2.4 Bogor Permata 7985 2018-04-13
## 7352 7352 Sherly 2.4 Depok Primadona 8174 2018-07-07
## 7402 7402 Sherly 2.4 Bekasi Mutiara 10002 2020-02-20
## 7452 7452 Sherly 2.4 Bekasi Sweethome 13945 2019-05-22
## 7502 7502 Sherly 2.4 Bogor Adara 14529 2018-05-21
## 7552 7552 Sherly 2.4 Tengerang Peronia 7885 2020-05-17
## 7602 7602 Sherly 2.4 Tengerang Winona 12363 2019-02-03
## 7652 7652 Sherly 2.4 Tengerang Asera 8598 2019-08-17
## 7702 7702 Sherly 2.4 Tengerang Winona 7885 2018-11-18
## 7752 7752 Sherly 2.4 Bekasi Permata 13320 2018-06-18
## 7802 7802 Sherly 2.4 Bekasi Mutiara 14779 2018-08-10
## 7852 7852 Sherly 2.4 Bekasi Lavesh 13620 2019-02-13
## 7902 7902 Sherly 2.4 Jakarta Lavesh 9719 2018-01-24
## 7952 7952 Sherly 2.4 Tengerang Sweethome 11244 2020-02-02
## 8002 8002 Sherly 2.4 Depok Asera 9645 2020-06-03
## 8052 8052 Sherly 2.4 Tengerang Teradamai 9221 2018-11-22
## 8102 8102 Sherly 2.4 Depok Tiara 8542 2019-09-27
## 8152 8152 Sherly 2.4 Bekasi Teradamai 8196 2019-08-21
## 8202 8202 Sherly 2.4 Jakarta Asoka 11625 2019-11-15
## 8252 8252 Sherly 2.4 Jakarta Teradamai 13894 2018-11-07
## 8302 8302 Sherly 2.4 Bekasi Albasia 10298 2019-04-12
## 8352 8352 Sherly 2.4 Bekasi Sweethome 13582 2018-10-05
## 8402 8402 Sherly 2.4 Jakarta Arana 11345 2020-04-22
## 8452 8452 Sherly 2.4 Bekasi Sweethome 10301 2018-09-15
## 8502 8502 Sherly 2.4 Bogor Mutiara 8790 2018-10-06
## 8552 8552 Sherly 2.4 Bekasi Arana 7477 2018-04-07
## 8602 8602 Sherly 2.4 Bogor Asoka 10404 2019-09-11
## 8652 8652 Sherly 2.4 Bekasi Teradamai 13774 2019-01-22
## 8702 8702 Sherly 2.4 Depok Asera 10363 2018-04-23
## 8752 8752 Sherly 2.4 Bogor Tiara 10550 2018-03-19
## 8802 8802 Sherly 2.4 Depok Winona 11701 2019-09-13
## 8852 8852 Sherly 2.4 Jakarta Winona 10788 2019-04-30
## 8902 8902 Sherly 2.4 Jakarta Permata 8998 2019-06-10
## 8952 8952 Sherly 2.4 Depok Adara 10731 2018-07-29
## 9002 9002 Sherly 2.4 Bogor Sweethome 8794 2020-02-26
## 9052 9052 Sherly 2.4 Bekasi Adara 8358 2018-02-09
## 9102 9102 Sherly 2.4 Bogor Narada 13218 2019-06-28
## 9152 9152 Sherly 2.4 Jakarta Victoria 11541 2018-11-28
## 9202 9202 Sherly 2.4 Jakarta Lavesh 13402 2019-03-14
## 9252 9252 Sherly 2.4 Bogor Narada 14641 2018-12-03
## 9302 9302 Sherly 2.4 Bogor Peronia 11258 2020-08-12
## 9352 9352 Sherly 2.4 Bogor Winona 11291 2020-03-30
## 9402 9402 Sherly 2.4 Tengerang Primadona 13362 2019-06-12
## 9452 9452 Sherly 2.4 Bogor Tiara 12924 2019-11-03
## 9502 9502 Sherly 2.4 Tengerang Narada 14250 2018-06-24
## 9552 9552 Sherly 2.4 Tengerang Mutiara 12275 2018-03-09
## 9602 9602 Sherly 2.4 Tengerang Arana 7729 2020-04-28
## 9652 9652 Sherly 2.4 Bekasi Victoria 9619 2019-05-21
## 9702 9702 Sherly 2.4 Jakarta Narada 12595 2019-07-26
## 9752 9752 Sherly 2.4 Bekasi Permata 11851 2020-05-23
## 9802 9802 Sherly 2.4 Tengerang Alindra 13210 2020-02-02
## 9852 9852 Sherly 2.4 Tengerang Peronia 13180 2020-02-05
## 9902 9902 Sherly 2.4 Depok Arana 12816 2018-10-30
## 9952 9952 Sherly 2.4 Bogor Lavesh 10963 2019-10-18
## Advertisement Kelas Booking_fee
## 2 1 High 1474.00
## 52 9 Low 681.10
## 102 5 Low 383.35
## 152 2 Low 492.60
## 202 18 High 1144.71
## 252 6 High 1404.00
## 302 18 High 1126.44
## 352 10 Low 528.06
## 402 11 Low 652.89
## 452 15 High 1474.10
## 502 4 High 1127.79
## 552 9 Medium 830.64
## 602 17 Medium 806.56
## 652 10 High 1441.50
## 702 12 Low 636.93
## 752 5 High 1141.38
## 802 15 High 1116.45
## 852 4 Medium 1010.43
## 902 14 Low 392.20
## 952 1 Low 658.77
## 1002 1 High 1408.60
## 1052 12 Low 498.78
## 1102 7 Low 490.50
## 1152 10 High 1154.70
## 1202 15 Low 649.18
## 1252 11 Medium 817.92
## 1302 20 Low 646.94
## 1352 2 Medium 1035.81
## 1402 6 High 1080.27
## 1452 9 Medium 1045.89
## 1502 18 High 1359.90
## 1552 17 Low 499.20
## 1602 10 Low 377.15
## 1652 7 Medium 851.12
## 1702 8 Low 372.40
## 1752 16 Low 644.63
## 1802 1 Low 359.00
## 1852 15 Low 670.46
## 1902 3 Low 514.56
## 1952 17 Low 504.36
## 2002 15 Low 363.15
## 2052 15 Low 641.06
## 2102 18 High 1411.80
## 2152 19 Low 685.09
## 2202 19 Medium 853.20
## 2252 4 High 1380.80
## 2302 7 High 1322.00
## 2352 19 Medium 867.44
## 2402 12 Low 683.90
## 2452 15 Medium 1045.08
## 2502 2 Low 658.70
## 2552 7 Low 663.39
## 2602 14 Medium 806.48
## 2652 15 Medium 1044.18
## 2702 17 Low 483.00
## 2752 17 Low 690.41
## 2802 2 Low 381.25
## 2852 9 Low 488.82
## 2902 13 Medium 1002.78
## 2952 15 High 1355.30
## 3002 5 Low 537.84
## 3052 13 High 1096.20
## 3102 2 Medium 1050.48
## 3152 16 High 1384.60
## 3202 17 High 1333.80
## 3252 3 High 1334.50
## 3302 20 Medium 841.76
## 3352 7 High 1167.66
## 3402 6 Medium 1057.95
## 3452 5 Low 518.40
## 3502 8 High 1142.10
## 3552 5 Medium 862.64
## 3602 2 Medium 853.36
## 3652 18 High 1435.10
## 3702 20 Low 653.31
## 3752 10 High 1099.98
## 3802 2 Low 521.40
## 3852 2 Medium 1009.08
## 3902 7 Medium 1001.61
## 3952 14 Low 511.32
## 4002 10 Low 389.95
## 4052 11 High 1419.40
## 4102 6 Low 522.24
## 4152 18 Low 668.50
## 4202 5 High 1488.70
## 4252 5 High 1364.30
## 4302 14 Medium 849.76
## 4352 20 Medium 820.00
## 4402 3 Low 522.48
## 4452 8 High 1434.50
## 4502 4 Medium 1030.32
## 4552 10 Low 513.90
## 4602 1 Low 483.00
## 4652 3 High 1131.93
## 4702 8 Medium 851.44
## 4752 19 Low 512.76
## 4802 4 High 1128.87
## 4852 15 Low 379.45
## 4902 11 Medium 822.00
## 4952 20 High 1449.50
## 5002 8 High 1487.80
## 5052 15 Low 395.20
## 5102 8 High 1158.03
## 5152 2 Medium 1056.33
## 5202 12 Low 534.24
## 5252 18 High 1354.70
## 5302 5 Low 659.89
## 5352 2 Low 489.60
## 5402 4 Medium 875.76
## 5452 4 Medium 1000.98
## 5502 4 Low 672.07
## 5552 15 Low 632.66
## 5602 19 Low 526.68
## 5652 15 Medium 804.08
## 5702 11 Medium 998.19
## 5752 4 Low 673.68
## 5802 11 High 1352.40
## 5852 14 Medium 817.52
## 5902 3 Medium 1011.33
## 5952 12 Medium 1057.68
## 6002 15 High 1332.10
## 6052 13 Medium 996.03
## 6102 13 High 1345.60
## 6152 15 Medium 1036.80
## 6202 7 Low 534.78
## 6252 17 Low 661.43
## 6302 15 High 1148.85
## 6352 13 High 1407.80
## 6402 2 Low 366.55
## 6452 14 Low 352.00
## 6502 10 Low 508.32
## 6552 4 High 1389.70
## 6602 1 Low 373.75
## 6652 7 Low 368.20
## 6702 5 Low 631.89
## 6752 10 Low 691.39
## 6802 9 High 1162.89
## 6852 15 High 1362.40
## 6902 20 Low 501.78
## 6952 17 High 1311.60
## 7002 18 Medium 835.60
## 7052 16 Medium 824.96
## 7102 2 Low 631.54
## 7152 3 Low 513.48
## 7202 10 Medium 802.16
## 7252 14 Medium 871.20
## 7302 9 Low 399.25
## 7352 9 Low 490.44
## 7402 14 Medium 800.16
## 7452 8 High 1394.50
## 7502 15 High 1452.90
## 7552 13 Low 394.25
## 7602 9 High 1112.67
## 7652 5 Low 515.88
## 7702 13 Low 394.25
## 7752 2 High 1332.00
## 7802 7 High 1477.90
## 7852 10 High 1362.00
## 7902 14 Low 680.33
## 7952 2 Medium 1011.96
## 8002 4 Low 675.15
## 8052 9 Low 645.47
## 8102 5 Low 512.52
## 8152 7 Low 491.76
## 8202 10 Medium 1046.25
## 8252 19 High 1389.40
## 8302 18 Medium 823.84
## 8352 3 High 1358.20
## 8402 3 Medium 1021.05
## 8452 20 Medium 824.08
## 8502 20 Low 527.40
## 8552 15 Low 373.85
## 8602 6 Medium 832.32
## 8652 16 High 1377.40
## 8702 9 Medium 829.04
## 8752 16 Medium 844.00
## 8802 20 Medium 1053.09
## 8852 16 Medium 863.04
## 8902 6 Low 539.88
## 8952 13 Medium 858.48
## 9002 2 Low 527.64
## 9052 7 Low 501.48
## 9102 8 High 1321.80
## 9152 17 Medium 1038.69
## 9202 16 High 1340.20
## 9252 1 High 1464.10
## 9302 20 Medium 1013.22
## 9352 5 Medium 1016.19
## 9402 12 High 1336.20
## 9452 9 High 1163.16
## 9502 19 High 1425.00
## 9552 8 High 1104.75
## 9602 11 Low 386.45
## 9652 9 Low 673.33
## 9702 1 High 1133.55
## 9752 1 Medium 1066.59
## 9802 12 High 1321.00
## 9852 9 High 1318.00
## 9902 3 High 1153.44
## 9952 7 Medium 877.04i## [1] "Sherly"Soal 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
Total_Bonus=subset(Data,
subset = Marketing_Name == "Sherly")
Data$Total_Bonus<-ifelse(Total_Bonus$Work_Exp >3,
Total_Bonus$Booking_fee*(2/100+1/100),Total_Bonus$Booking_fee*2/100)
DataSoal 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
# Data
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"))
Theodora=subset(Data,subset = (Marketing_Name=="Theodora"))
Elvani=subset(Data,subset = (Marketing_Name=="Elvani"))
Jonathan=subset(Data,subset = (Marketing_Name=="Jonathan"))
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"))
Nama_Sales = 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")
propertysold = 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$Price))
datamarketing=data.frame(Nama_Sales, propertysold)
datamarketingdatamarketing[which.max(datamarketing$propertysold),] # Marketing Pemasaran Terbaik# Kota dan Cluster yang paling menguntungkan
profitable= Data[,c("City","Cluster","Price")]
profitable[which.max(profitable$Price),]# total biaya iklan Anda, jika Anda harus membayarnya $4 setiap kali iklan
costforad = subset(Data,subset = (Marketing_Name=="Sheryl"))
adscost = (costforad$Advertisement*4)
total_cost = print(sum(adscost))## [1] 0# rata-rata biaya iklan untuk setiap marketing di Perusahaan tersebut
Nama_Sales = 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")
ratacost = for (x in Nama_Sales) {
f=subset(Data, subset = (Marketing_Name==x))
c=sum(f$Advertisement*4)
print(cat(sum(c)/length(f$Id),(cat(x,'rata-rata biaya iklan'))))
}## Angel rata-rata biaya iklan44.96NULL
## Sherly rata-rata biaya iklan40.7NULL
## Vanessa rata-rata biaya iklan41.36NULL
## Irene rata-rata biaya iklan44NULL
## Julian rata-rata biaya iklan42.9NULL
## Jeffry rata-rata biaya iklan40.22NULL
## Nikita rata-rata biaya iklan42.16NULL
## Kefas rata-rata biaya iklan43.66NULL
## Siana rata-rata biaya iklan41.8NULL
## Lala rata-rata biaya iklan43.58NULL
## Fallen rata-rata biaya iklan43.34NULL
## Ardifo rata-rata biaya iklan41.6NULL
## Kevin rata-rata biaya iklan44.32NULL
## Juen rata-rata biaya iklan43NULL
## Jerrel rata-rata biaya iklan42.16NULL
## Imelda rata-rata biaya iklan42.94NULL
## Widi rata-rata biaya iklan42.52NULL
## Theodora rata-rata biaya iklan39.1NULL
## Elvani rata-rata biaya iklan45.02NULL
## Jonathan rata-rata biaya iklan45.22NULL
## Sofia rata-rata biaya iklan42.46NULL
## Abraham rata-rata biaya iklan42.18NULL
## Siti rata-rata biaya iklan42.22NULL
## Niko rata-rata biaya iklan42.7NULL
## Sefli rata-rata biaya iklan42.58NULL
## Bene rata-rata biaya iklan40.26NULL
## Diana rata-rata biaya iklan41.12NULL
## Pupe rata-rata biaya iklan42.4NULL
## Andi rata-rata biaya iklan42.5NULL
## Tatha rata-rata biaya iklan44.2NULL
## Endri rata-rata biaya iklan42.78NULL
## Monika rata-rata biaya iklan42.22NULL
## Hans rata-rata biaya iklan42.88NULL
## Debora rata-rata biaya iklan43.72NULL
## Hanifa rata-rata biaya iklan44.44NULL
## James rata-rata biaya iklan41.6NULL
## Jihan rata-rata biaya iklan44.76NULL
## Friska rata-rata biaya iklan41.6NULL
## Ardiwan rata-rata biaya iklan41.44NULL
## Bakti rata-rata biaya iklan43.32NULL
## Anthon rata-rata biaya iklan42.72NULL
## Amry rata-rata biaya iklan42.82NULL
## Wiwik rata-rata biaya iklan41.36NULL
## Bastian rata-rata biaya iklan38.58NULL
## Budi rata-rata biaya iklan41.34NULL
## Leo rata-rata biaya iklan42.34NULL
## Simon rata-rata biaya iklan43.88NULL
## Matius rata-rata biaya iklan42.06NULL
## Arry rata-rata biaya iklan38.82NULL
## Eliando rata-rata biaya iklan43.26NULL# Total Pendapatan (dalam Bulanan)
revenue=(sum(Data$Price)-(sum(Data$Advertisement)*4))/((max(Data$Work_Exp))*12)
revenue## [1] 909522.6Kasus 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
Marital_Status <- sample(c("Ya","Tidak"),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)
Academic <- sample(c("J.School","H.School","Sarjana","Magister","Phd"),50000,replace = T)
Job <- ifelse(Academic=="J.School",
sample(c("Office Boy","Staf Gudang")),
ifelse(Academic=="H.School",
sample(c("staff admin","Customer Relationship")),
ifelse(Academic=="Sarjana",
sample(c("Finance Accounting Executive","Asisten Administrasi (ADM)")),
ifelse(Academic=="Magister",
sample(c("Senior Finansial Planner","Surveyor")),
ifelse(Academic=="Phd",
sample(c("Insurance Broker","Reinsurance Broker")),0)))))
Grade <- sample(c(1:5),50000,replace = T)
Income <- ifelse(Job == "Office Boy",
2500000,
ifelse(Job == "Staf Gudang",
3000000,
ifelse(Job == "staff admin",
3250000,
ifelse(Job == "Customer Relationship",
3500000,
ifelse(Job == "Finance Accounting Executive",
5000000,
ifelse(Job == "Asisten Administrasi (ADM)",
5500000,
ifelse(Job == "Senior Finansial Planner",
6000000,
ifelse(Job == "Surveyor",
6500000,
ifelse(Job == "Insurance Broker",
7000000,
ifelse(Job == "Reinsurance Broker",
7500000,0))))))))))
Spending <- ifelse(Job == "Office Boy",
1000000,
ifelse(Job == "Staf Gudang",
1500000,
ifelse(Job == "staff admin",
2000000,
ifelse(Job == "Customer Relationship",
2500000,
ifelse(Job == "Finance Accounting Executive",
3000000,
ifelse(Job == "Asisten Administrasi (ADM)",
3500000,
ifelse(Job == "Senior Finansial Planner",
4000000,
ifelse(Job == "Surveyor",
4500000,
ifelse(Job == "Insurance Broker",
5000000,
ifelse(Job == "Reinsurance Broker",
550000,0))))))))))
Number_of_children <- ifelse(Marital_Status == "Ya", sample(c(0:10)),"-")
Private_vehicle <- sample(c("mobil","sepeda motor","umum"),50000, replace = T)
Home <- sample(c("Sewa","Milik","Kredit"),50000, replace = T)
Data_Kasus02 <- data.frame(Marital_Status,
Address,
Work_Location,
Age,
Academic,
Job,
Grade,
Income,
Spending,
Number_of_children,
Private_vehicle,
Home)
library(DT)
datatable(Data_Kasus02)write.csv(Data_Kasus02,"c:\\Users\\Public\\Kasus02.csv", row.names = FALSE)Soal 2
Ringkasan Statistik penting seperti apa yang bisa Anda dapatkan dari kumpulan data Anda?
R
summary(Data_Kasus02)## Marital_Status Address Work_Location Age
## Length:50000 Length:50000 Length:50000 Min. :19.0
## Class :character Class :character Class :character 1st Qu.:29.0
## Mode :character Mode :character Mode :character Median :40.0
## Mean :39.5
## 3rd Qu.:50.0
## Max. :60.0
## Academic Job Grade Income
## Length:50000 Length:50000 Min. :1.000 Min. :2500000
## Class :character Class :character 1st Qu.:2.000 1st Qu.:3250000
## Mode :character Mode :character Median :3.000 Median :5500000
## Mean :2.993 Mean :4979180
## 3rd Qu.:4.000 3rd Qu.:6500000
## Max. :5.000 Max. :7500000
## Spending Number_of_children Private_vehicle Home
## Min. : 550000 Length:50000 Length:50000 Length:50000
## 1st Qu.:1500000 Class :character Class :character Class :character
## Median :3000000 Mode :character Mode :character Mode :character
## Mean :2757034
## 3rd Qu.:4000000
## Max. :5000000Soal 3
Menurut perhitungan dan analisis Anda, pelanggan mana yang potensial untuk Anda pertahankan?
R
Data_Kasus02$Pendapatan = Data_Kasus02$Income - Data_Kasus02$Spending
x <- filter(Data_Kasus02, Income >= 5000000)
library(DT)
datatable(x)Referensi
- ref 1
- ref 2
- ref 3
---
title: "Algoritma & Struktur Data"
subtitle: "~ Ujian Tengah Semester ~"
author: "Sausan Ramadhani"
date:  "`r format(Sys.Date(), '%B %d, %Y')`"
output:
  rmdformats::robobook:   # https://github.com/juba/rmdformats
    self_contained: true
    thumbnails: true
    lightbox: true
    gallery: true
    lib_dir: libs
    df_print: "paged"
    code_folding: "show"
    code_download: yes
    css: "style.css"

---

```{r include=FALSE}
knitr::opts_chunk$set(class.source = "nocopy",
                      class.output = "nocopy",
                      message = F,
                      warning = F)

library(reticulate)
library(Rcpp)
use_condaenv("py38", required = TRUE)
```

<br>

<img style="float: right; margin: -50px 50px 0px 50px; width:30%" src="fotoprofil.png"/>

|
:---- |:----
**Kontak**| **: $\downarrow$**
Email| mugemisausan05@gmail.com
Instagram | https://www.instagram.com/saram.05/ 
RPubs  | https://rpubs.com/sausanramadhani/ 

***

# Kasus 1 

Asumsikan Anda telah mengumpulkan beberapa kumpulan data dari perusahaan `ABC Property` seperti yang dapat kita lihat pada tabel berikut: 

```{r, message=F}
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)
```
```{r}
write.csv(Data,"c:\\Users\\Public\\UTS.csv", row.names = FALSE)
```


## Soal 1 {.tabset .tabset-pills}

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

```{r}
Data$Kelas<-ifelse(Data$Price > 12000,
                   "High",
            ifelse(Data$Price >=10000 & Data$ Price <=12000,
                   "Medium",
            ifelse(Data$Price <10000,
                   "Low",0)))
Data
```


## Soal 2 {.tabset .tabset-pills}

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

```{r}
x<-Data$Price
Data$Booking_fee<-ifelse(x < 8000,
                         x*5/100,
                  ifelse(x >=8000 & x <9000,
                         x*6/100,
                  ifelse(x >=9000 & x <10000,
                         x*7/100,
                  ifelse(x >=10000 & x <11000,
                         x*8/100,
                  ifelse(x >=11000 & x <13000,
                         x*9/100,
                  ifelse(x >=13000 & x <=15000,
                         x*10/100, 0))))))
Data
```


## Soal 3 {.tabset .tabset-pills}

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

```{r}
sales ="Sherly"

z = for(i in sales){
  print(subset(Data,
        subset=(Marketing_Name == i)))
}
i
```


## Soal 4 {.tabset .tabset-pills}

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

```{r}
Total_Bonus=subset(Data,
            subset = Marketing_Name == "Sherly")
Data$Total_Bonus<-ifelse(Total_Bonus$Work_Exp >3,
                         Total_Bonus$Booking_fee*(2/100+1/100),Total_Bonus$Booking_fee*2/100)
Data
```


## Soal 5 {.tabset .tabset-pills}

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

```{r}
# Data
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"))
Theodora=subset(Data,subset = (Marketing_Name=="Theodora"))
Elvani=subset(Data,subset = (Marketing_Name=="Elvani"))
Jonathan=subset(Data,subset = (Marketing_Name=="Jonathan"))
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"))

Nama_Sales = 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")
propertysold = 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))
datamarketing=data.frame(Nama_Sales, propertysold)

datamarketing
```

```{r}
datamarketing[which.max(datamarketing$propertysold),]    # Marketing Pemasaran Terbaik
```

```{r}
# Kota dan Cluster yang paling menguntungkan
profitable= Data[,c("City","Cluster","Price")]
profitable[which.max(profitable$Price),]
```

```{r}
# total biaya iklan Anda, jika Anda harus membayarnya $4 setiap kali iklan
costforad  = subset(Data,subset = (Marketing_Name=="Sheryl"))
adscost    = (costforad$Advertisement*4)
total_cost = print(sum(adscost))
```

```{r}
# rata-rata biaya iklan untuk setiap marketing di Perusahaan tersebut
Nama_Sales = 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")
ratacost = for (x in Nama_Sales) {
  f=subset(Data, subset = (Marketing_Name==x))
  c=sum(f$Advertisement*4)
  print(cat(sum(c)/length(f$Id),(cat(x,'rata-rata biaya iklan'))))
}
```

```{r}
# Total Pendapatan (dalam Bulanan)
revenue=(sum(Data$Price)-(sum(Data$Advertisement)*4))/((max(Data$Work_Exp))*12)
revenue
```


# Kasus 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 {.tabset .tabset-pills}

Tolong berikan saya kumpulan data tentang informasi 50000 pelanggan yang mengacu pada setiap variabel di atas! 

### R 

```{r}
Marital_Status <- sample(c("Ya","Tidak"),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)
Academic       <- sample(c("J.School","H.School","Sarjana","Magister","Phd"),50000,replace = T)
Job            <- ifelse(Academic=="J.School",
                         sample(c("Office Boy","Staf Gudang")),
                  ifelse(Academic=="H.School",
                         sample(c("staff admin","Customer Relationship")),
                  ifelse(Academic=="Sarjana",
                         sample(c("Finance Accounting Executive","Asisten Administrasi (ADM)")),
                  ifelse(Academic=="Magister",
                         sample(c("Senior Finansial Planner","Surveyor")),
                  ifelse(Academic=="Phd",
                         sample(c("Insurance Broker","Reinsurance Broker")),0)))))
Grade          <- sample(c(1:5),50000,replace = T)
Income         <- ifelse(Job == "Office Boy",
                         2500000,
                  ifelse(Job == "Staf Gudang",
                         3000000,
                  ifelse(Job == "staff admin",
                         3250000,
                  ifelse(Job == "Customer Relationship",
                         3500000,
                  ifelse(Job == "Finance Accounting Executive",
                         5000000,
                  ifelse(Job == "Asisten Administrasi (ADM)",
                         5500000,
                  ifelse(Job == "Senior Finansial Planner",
                         6000000,
                  ifelse(Job == "Surveyor",
                         6500000,
                  ifelse(Job == "Insurance Broker",
                         7000000,
                  ifelse(Job == "Reinsurance Broker",
                         7500000,0))))))))))
Spending       <- ifelse(Job == "Office Boy",
                         1000000,
                  ifelse(Job == "Staf Gudang",
                         1500000,
                  ifelse(Job == "staff admin",
                         2000000,
                  ifelse(Job == "Customer Relationship",
                         2500000,
                  ifelse(Job == "Finance Accounting Executive",
                         3000000,
                  ifelse(Job == "Asisten Administrasi (ADM)",
                         3500000,
                  ifelse(Job == "Senior Finansial Planner",
                         4000000,
                  ifelse(Job == "Surveyor",
                         4500000,
                  ifelse(Job == "Insurance Broker",
                         5000000,
                  ifelse(Job == "Reinsurance Broker",
                         550000,0))))))))))
Number_of_children <- ifelse(Marital_Status == "Ya", sample(c(0:10)),"-")
Private_vehicle    <- sample(c("mobil","sepeda motor","umum"),50000, replace = T)
Home               <- sample(c("Sewa","Milik","Kredit"),50000, replace = T)

Data_Kasus02 <- data.frame(Marital_Status,
                           Address,
                           Work_Location,
                           Age,
                           Academic,
                           Job,
                           Grade,
                           Income,
                           Spending,
                           Number_of_children,
                           Private_vehicle,
                           Home)
library(DT)
datatable(Data_Kasus02)
```

```{r}
write.csv(Data_Kasus02,"c:\\Users\\Public\\Kasus02.csv", row.names = FALSE)
```


## Soal 2 {.tabset .tabset-pills}

Ringkasan Statistik penting seperti apa yang bisa Anda dapatkan dari kumpulan data Anda?

### R

```{r}
summary(Data_Kasus02)
```


## Soal 3 {.tabset .tabset-pills}

Menurut perhitungan dan analisis Anda, pelanggan mana yang potensial untuk Anda pertahankan? 

### R

```{r}
Data_Kasus02$Pendapatan = Data_Kasus02$Income - Data_Kasus02$Spending

x <- filter(Data_Kasus02, Income >= 5000000)

library(DT)
datatable(x)
```




# Referensi

1.   ref 1
2.   ref 2
3.   ref 3




