UTS Algoritma dan Struktur Data
~ Ujian Tengah Semester ~
Karen Natalie(20204920015)
October 16, 2021
| Kontak | : \(\downarrow\) |
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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)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.
Data$Kelas <- ifelse(Data$Price>12000,
"High",
ifelse(Data$Price<10000,
"Low",
"Medium"))
library(DT)
datatable(Data)Soal 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.
Data$Booking_fee <- ifelse(Data$Price >= 13000,
Data$Price*0.1,
ifelse(Data$Price>=11000,
Data$Price*0.09,
ifelse(Data$Price>=10000,
Data$Price*0.08,
ifelse(Data$Price>=9000,
Data$Price*0.07,
ifelse(Data$Price>=8000,
Data$Price*0.06,
Data$Price*0.05)))))
library(DT)
library(dplyr)subset(Data, select = c(6, 10)) %>% datatable()Data$Booking_fee<- Data$Booking_fee
print(head(Data, 10))## Id Marketing_Name Work_Exp City Cluster Price Date_Sales
## 1 1 Angel 1.3 Depok Mutiara 12782 2020-06-27
## 2 2 Sherly 2.4 Bekasi Albasia 9985 2018-01-27
## 3 3 Vanessa 2.5 Bekasi Narada 13617 2018-02-04
## 4 4 Irene 3.6 Tengerang Teradamai 12920 2018-03-18
## 5 5 Julian 3.7 Bogor Mutiara 8781 2020-09-25
## 6 6 Jeffry 4.7 Tengerang Adara 8700 2020-05-11
## 7 7 Nikita 5.7 Bekasi Narada 12606 2019-06-11
## 8 8 Kefas 6.7 Bogor Asoka 11253 2020-06-06
## 9 9 Siana 7.7 Tengerang Asoka 13136 2019-10-26
## 10 10 Lala 7.3 Tengerang Sweethome 7898 2018-12-14
## Advertisement Kelas Booking_fee
## 1 1 High 1150.38
## 2 13 Low 698.95
## 3 3 High 1361.70
## 4 18 High 1162.80
## 5 14 Low 526.86
## 6 16 Low 522.00
## 7 15 High 1134.54
## 8 12 Medium 1012.77
## 9 20 High 1313.60
## 10 16 Low 394.90Soal 3
Untuk data penjualan di perusahaan ‘ABC Property’ pada nama “Bakti” sebagai berikut :
library(dplyr)for (i in "Bakti"){
Data%>%filter(Marketing_Name==i)%>%head(10)%>%print()
break
}## Id Marketing_Name Work_Exp City Cluster Price Date_Sales
## 1 40 Bakti 8.2 Jakarta Teradamai 7462 2018-09-20
## 2 90 Bakti 8.2 Jakarta Winona 13784 2018-12-19
## 3 140 Bakti 8.2 Jakarta Primadona 12199 2020-04-22
## 4 190 Bakti 8.2 Jakarta Peronia 7403 2018-03-23
## 5 240 Bakti 8.2 Depok Tiara 9223 2019-03-20
## 6 290 Bakti 8.2 Bogor Primadona 11677 2019-07-16
## 7 340 Bakti 8.2 Bekasi Palmyra 14787 2018-08-12
## 8 390 Bakti 8.2 Depok Sweethome 11525 2020-01-03
## 9 440 Bakti 8.2 Jakarta Tiara 11291 2020-08-05
## 10 490 Bakti 8.2 Tengerang Victoria 11841 2018-09-09
## Advertisement Kelas Booking_fee
## 1 2 Low 373.10
## 2 17 High 1378.40
## 3 10 High 1097.91
## 4 2 Low 370.15
## 5 12 Low 645.61
## 6 14 Medium 1050.93
## 7 11 High 1478.70
## 8 14 Medium 1037.25
## 9 16 Medium 1016.19
## 10 4 Medium 1065.69Soal 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.
library(dplyr)
list<-c()
for (i in Data$Marketing_Name){
Data$Bonus <- ifelse(Data$Work_Exp>3, Data$Booking_fee*0.03, Data$Booking_fee*0.02)
list[[i]]<- aggregate(Bonus ~ Marketing_Name, data = Data, sum, T)
break}
result<-list[[i]]
result <- result[order(result$Bonus,decreasing=T),]
result%>%filter(Marketing_Name=="Bakti")%>%print()## Marketing_Name Bonus
## 1 Bakti 5574.946Soal 5
Pada bagian ini, Anda diharapkan dapa 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 $4 setiap kali iklan.
- Hitung rata-rata biaya iklan untuk setiap marketing di Perusahaan tersebut.
- Hitung Total Pendapatan (dalam Bulanan)
Siapa nama marketing pemasaran terbaik?
library(dplyr)
library(tidyverse)library(dplyr)
list_by_Price <-aggregate(Price~ Marketing_Name , data = Data, sum)
result_price <-list_by_Price[order(list_by_Price$Price,decreasing=T),]
list_by_adv <-aggregate(Advertisement~ Marketing_Name , data = Data, sum)
Datasales <- data.frame(left_join(result_price, list_by_adv))%>%head(1)Datasales## Marketing_Name Price Advertisement
## 1 Amry 2274538 2183Kota dan Cluster mana yang paling menguntungkan?
library(dplyr)
Profitable_City_Cluster <- aggregate ( Price ~ Cluster + City, data= Data, sum)
Data_City_Cluster <-Profitable_City_Cluster[order(Profitable_City_Cluster$Price,decreasing=T),]%>%head(1)
Data_City_Cluster## Cluster City Price
## 86 Asera Tengerang 1389003Hitung total biaya iklan Anda, jika Anda harus membayarnya $4 setiap kali iklan
Data$Advertisement <- as.numeric(Data$Advertisement)
Data$Advertisement_Cost <- Data$Advertisement * 4
Total_Cost <- sum(Data$Advertisement_Cost)
paste("The total cost was", Total_Cost)## [1] "The total cost was 418604"Hitung rata-rata biaya iklan untuk setiap marketing di Perusahaan tersebut
Data$Advertisement.cost <- Data$Advertisement*4
aggregate(Advertisement.cost ~ Marketing_Name, data= Data, mean)## Marketing_Name Advertisement.cost
## 1 Abraham 39.44
## 2 Amry 43.66
## 3 Andi 40.66
## 4 Angel 43.22
## 5 Anthon 41.90
## 6 Ardifo 40.38
## 7 Ardiwan 45.32
## 8 Arry 38.34
## 9 Bakti 41.06
## 10 Bastian 42.62
## 11 Bene 40.24
## 12 Budi 44.10
## 13 Debora 41.80
## 14 Diana 40.44
## 15 Eliando 41.52
## 16 Elvani 42.54
## 17 Endri 44.06
## 18 Fallen 47.12
## 19 Friska 41.56
## 20 Hanifa 40.80
## 21 Hans 42.44
## 22 Imelda 42.50
## 23 Irene 44.98
## 24 James 43.14
## 25 Jeffry 40.22
## 26 Jerrel 44.02
## 27 Jihan 40.60
## 28 Jonathan 42.78
## 29 Juen 44.96
## 30 Julian 41.42
## 31 Kefas 41.56
## 32 Kevin 43.56
## 33 Lala 39.14
## 34 Leo 39.98
## 35 Matius 40.08
## 36 Monika 39.72
## 37 Nikita 43.98
## 38 Niko 40.84
## 39 Pupe 39.52
## 40 Sefli 42.12
## 41 Sherly 41.14
## 42 Siana 43.58
## 43 Simon 42.50
## 44 Siti 40.38
## 45 Sofia 43.76
## 46 Tatha 39.00
## 47 Theodora 42.42
## 48 Vanessa 39.26
## 49 Widi 40.52
## 50 Wiwik 42.12Hitung Total Pendapatan (dalam Bulanan)
library(dplyr)
library(tidyverse)Data$Revenue <- Data$Price - Data$Booking_fee
Monthly_Rev <- Data%>%separate(Date_Sales, c("Year", "Month", "Day"), sep = "-")%>%
select(Year, Month, Revenue)
Monthly.Rev <-aggregate(Revenue ~ Month + Year, data = Monthly_Rev, sum)
Monthly.Rev## Month Year Revenue
## 1 01 2018 3391985
## 2 02 2018 2897540
## 3 03 2018 3233762
## 4 04 2018 3151002
## 5 05 2018 3438817
## 6 06 2018 2754942
## 7 07 2018 3326189
## 8 08 2018 3219698
## 9 09 2018 2853057
## 10 10 2018 2893327
## 11 11 2018 3022508
## 12 12 2018 3436697
## 13 01 2019 3056697
## 14 02 2019 2505635
## 15 03 2019 2950020
## 16 04 2019 3481030
## 17 05 2019 3065328
## 18 06 2019 2898673
## 19 07 2019 3094487
## 20 08 2019 3207818
## 21 09 2019 2833959
## 22 10 2019 2976701
## 23 11 2019 2868009
## 24 12 2019 2977550
## 25 01 2020 3371527
## 26 02 2020 3002308
## 27 03 2020 3050490
## 28 04 2020 3084955
## 29 05 2020 3116299
## 30 06 2020 3333659
## 31 07 2020 2818103
## 32 08 2020 3144749
## 33 09 2020 2475544