Algoritma & Struktur Data

~ Ujian Tengah Semester ~

Kontak	: /$downarrow$
Email	naftali.gunawan@student.matanauniversity.ac.id
Instagram	https://www.instagram.com/nbrigittag/
RPubs	https://rpubs.com/naftalibrigitta/

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\\Naftali Brigitta\\data.csv",row.names = FALSE)

Soal 1

Kategorikan variabel Harga pada dataset di atas menjadi tiga kelompok sebagai berikut:

$\text{High} > 1200$
$1000 \le \text{Medium} \le 12000$
$\text{Low} < 10000$

Tetapkan ke dalam variabel baru yang disebut Kelas dengan menggunakan fungsi kontrol If, else if, dan else.

R

x = Data$Price

a= ifelse((x>12000),print("High"),
          ifelse((x>=10000 & x<= 12000), print("Medium"),print("Low")
                 ))

## [1] "High"
## [1] "Medium"
## [1] "Low"

Data$Class = a
datatable(Data)

Soal 2

Kategorikan variabel Harga pada dataset di atas menjadi enam kelompok sebagai berikut:

5 % Booking fee if the $\text{Price} < 8000$
6 % Booking fee if the $8000 \le \text{Price} < 9000$
7 % Booking fee if the $9000 \le \text{Price} < 10000$
8 % Booking fee if the $10000 \le \text{Price} < 11000$
9 % Booking fee if the $11000 \le \text{Price} < 13000$
10 % Booking fee if the $13000 \le \text{Price} \le 15000$

Tetapkan ke dalam variabel baru yang disebut Booking_fee dengan menggunakan fungsi kontrol If, else if, dan else.

R

Data$Booking_Fee <- ifelse(Data$Price < 8000, 5/100*Data$Price,
                    ifelse(Data$Price < 9000 & Data$Price > 8000 , 6/100*Data$Price, 
                    ifelse(Data$Price < 10000 & Data$Price > 9000 , 7/100*Data$Price,
                    ifelse(Data$Price < 11000 & Data$Price > 10000 , 8/100*Data$Price,
                    ifelse(Data$Price < 12000 & Data$Price > 11000 , 9/100*Data$Price,
                                                                    10/100*Data$Price
                           )))))
datatable(Data)

Soal 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(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.4     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

datatable(
          for(x in "Lala") 
          {
            Data %>% filter (Marketing_Name==x) %>% head(5) %>% print()
          }
         )

##    Id Marketing_Name Work_Exp    City Cluster Price Date_Sales Advertisement
## 1  10           Lala      7.3   Depok Permata 14392 2018-04-05            10
## 2  60           Lala      7.3  Bekasi   Asoka  8646 2020-04-23             7
## 3 110           Lala      7.3   Depok   Arana 11951 2019-10-07            12
## 4 160           Lala      7.3 Jakarta   Arana  7480 2020-08-02            16
## 5 210           Lala      7.3 Jakarta Palmyra 13143 2018-11-09             3
##    Class Booking_Fee
## 1   High     1439.20
## 2    Low      518.76
## 3 Medium     1075.59
## 4    Low      374.00
## 5   High     1314.30

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

Guns = subset(Data, subset=(Marketing_Name == "Eliando"))
                
Y = ifelse((Guns$Work_Exp < 3),
           (Guns$Price * Guns$Booking_Fee)*(0.02),
           (Guns$Price * Guns$Booking_Fee)*(0.03))

Guns$Bonus = Y
Guns

Bonus = sum(Guns$Bonus)

Bonus

## [1] 65560647

Soal 5

Pada bagian ini, Anda diharapkan dapat menggunakan semua pernyataan yang baru saja Anda pelajari sebelumnya. Jadi, tolong jawab pertanyaan berikut:

Siapa nama marketing pemasaran terbaik?
Kota dan Cluster mana yang paling menguntungkan?
Hitung total biaya iklan Anda, jika Anda harus membayarnya $4 sekali.
Hitung rata-rata biaya iklan untuk setiap pemasaran.
Hitung Total Pendapatan (dalam Bulanan)

R

Siapa nama marketing pemasaran terbaik?

MarketName = 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") 

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"))

sum =
 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)) 

m_ns = data.frame (MarketName, 
                 sum)
m_ns

max(sum)

## [1] 2262210

Mencari = which.max(m_ns$sum)
Pemasar = m_ns[Mencari,]
Pemasar

Kota dan Cluster mana yang paling menguntungkan?

city = c("Jakarta", "Bogor", "Tangerang", "Depok", "Bekasi")

Jakarta   = subset(Data, subset=(City == "Jakarta"))
Bogor     = subset(Data, subset=(City == "Bogor"))
Tangerang = subset(Data, subset=(City == "Tangerang"))
Depok     = subset(Data, subset=(City == "Depok"))
Bekasi    = subset(Data, subset=(City == "Bekasi"))

mean = 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))

untung_city = data.frame(city,
                         mean) 
               
untung_city

max(mean)

## [1] NaN

Mencari = which.max(untung_city$mean)
Kota = untung_city[Mencari,]
Kota

cluster = c("Victoria", "Palmyra", "Winona", "Tiara", "Narada", "Peronia", "Lavesh",
            "Alindra", "Sweethome", "Asera", "Teradamai", "Albasia", "Adara", "Neon",
            "Arana", "Asoka", "Primadona", "Mutiara", "Permata", "Alamanda")

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 == "Terdamai"))
Neon      = subset (Data, subset=( Cluster == "Neon"))
Albasia   = subset (Data, subset=( Cluster == "Albasia"))
Adara     = subset (Data, subset=( Cluster == "Adara"))
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"))

mean = 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(Neon$Price)/length(Neon$Id), 
         sum(Albasia$Price)/length(Albasia$Id), 
         sum(Adara$Price)/length(Adara$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))

untung_cluster = data.frame(cluster,mean)

untung_cluster

max(mean)

## [1] NaN

Mencari = which.max(untung_cluster$mean)
Klaster = untung_cluster[Mencari,]
Klaster

Hitung total biaya iklan Anda, jika Anda harus membayarnya $4 sekali.

M_name = "Eliando"
Mar_Name = subset(Data, subset=(Marketing_Name == M_name))

Iklan = (Mar_Name$Advertisement * 4)

t_iklan = print(sum(Iklan))

## [1] 8016

iklannn =c(M_name, unlist(t_iklan))

woiii = function(x)
{
  print(iklannn)
  print(c(cat("Marketing Terbaik adalah", unlist(Pemasar),"\n", 
              "Kota yang Menguntungkan adalah", unlist(Kota),"\n", 
              "Cluster yang Menguntungkan adalah", unlist(Klaster),"\n")))
}
woiii(x)

## [1] "Eliando" "8016"   
## Marketing Terbaik adalah Imelda 2262210 
##  Kota yang Menguntungkan adalah Bogor 11059.3989924433 
##  Cluster yang Menguntungkan adalah Mutiara 11177.0365853659 
## NULL

Hitung rata-rata biaya iklan untuk setiap pemasaran.

Namaaa = 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")
Iklan_Market = for (x in Namaaa)
{
  t = subset(Data, subset=(Marketing_Name == x))
  i = sum(t$Advertisement * 4)
  print(cat(sum(i)/length(t$Id),(cat(x, "rata-rata biaya iklan nya adalah"))))}

## Angel rata-rata biaya iklan nya adalah43.24NULL
## Sherly rata-rata biaya iklan nya adalah43.9NULL
## Vanessa rata-rata biaya iklan nya adalah40.92NULL
## Irene rata-rata biaya iklan nya adalah40.4NULL
## Julian rata-rata biaya iklan nya adalah41.84NULL
## Jeffry rata-rata biaya iklan nya adalah42.38NULL
## Nikita rata-rata biaya iklan nya adalah40.48NULL
## Kefas rata-rata biaya iklan nya adalah41.26NULL
## Siana rata-rata biaya iklan nya adalah43.16NULL
## Lala rata-rata biaya iklan nya adalah43.88NULL
## Fallen rata-rata biaya iklan nya adalah40.44NULL
## Ardifo rata-rata biaya iklan nya adalah41.5NULL
## Kevin rata-rata biaya iklan nya adalah40.16NULL
## Juen rata-rata biaya iklan nya adalah41.52NULL
## Jerrel rata-rata biaya iklan nya adalah40.9NULL
## Imelda rata-rata biaya iklan nya adalah40.1NULL
## Widi rata-rata biaya iklan nya adalah42.3NULL
## Theodora rata-rata biaya iklan nya adalah39.76NULL
## Elvani rata-rata biaya iklan nya adalah43.42NULL
## Jonathan rata-rata biaya iklan nya adalah40.38NULL
## Sofia rata-rata biaya iklan nya adalah41.66NULL
## Abraham rata-rata biaya iklan nya adalah43NULL
## Siti rata-rata biaya iklan nya adalah38.9NULL
## Niko rata-rata biaya iklan nya adalah42.58NULL
## Sefli rata-rata biaya iklan nya adalah41.74NULL
## Bene rata-rata biaya iklan nya adalah43.42NULL
## Diana rata-rata biaya iklan nya adalah44.7NULL
## Pupe rata-rata biaya iklan nya adalah40.96NULL
## Andi rata-rata biaya iklan nya adalah41.38NULL
## Tatha rata-rata biaya iklan nya adalah43.76NULL
## Endri rata-rata biaya iklan nya adalah40.48NULL
## Monika rata-rata biaya iklan nya adalah42NULL
## Hans rata-rata biaya iklan nya adalah43.18NULL
## Debora rata-rata biaya iklan nya adalah39.5NULL
## Hanifa rata-rata biaya iklan nya adalah43.4NULL
## James rata-rata biaya iklan nya adalah41.3NULL
## Jihan rata-rata biaya iklan nya adalah42.12NULL
## Friska rata-rata biaya iklan nya adalah40.08NULL
## Ardiwan rata-rata biaya iklan nya adalah41.1NULL
## Bakti rata-rata biaya iklan nya adalah39.56NULL
## Anthon rata-rata biaya iklan nya adalah42.62NULL
## Amry rata-rata biaya iklan nya adalah41.9NULL
## Wiwik rata-rata biaya iklan nya adalah38.76NULL
## Bastian rata-rata biaya iklan nya adalah41.18NULL
## Budi rata-rata biaya iklan nya adalah44.04NULL
## Leo rata-rata biaya iklan nya adalah42.76NULL
## Simon rata-rata biaya iklan nya adalah42.36NULL
## Matius rata-rata biaya iklan nya adalah42.18NULL
## Arry rata-rata biaya iklan nya adalah42.46NULL
## Eliando rata-rata biaya iklan nya adalah40.08NULL

Hitung Total Pendapatan (dalam Bulanan)

Totalpend = function(x)
{
  Sum_Dahulu = sum(Data$Price) -(sum(Data$Advertisement)*4)
  
  return(cat("Total Pendapatan Perbulan Saya adalah", Sum_Dahulu))
}

Totalpend(x)

## Total Pendapatan Perbulan Saya adalah 109676007

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

Tolong berikan saya kumpulan data tentang informasi 50000 pelanggan yang mengacu pada setiap variabel di atas!

R

Number              <- (1:50000)
Marital_Status      <- sample(c("Ya", "Tidak"), 50000, replace = T)
Address             <- sample(c("Jakarta", "Tangerang", "Depok", "Bekasi", "Bogor"), 50000,
                              replace = T)
Work_Location       <- sample(c("Jakarta", "Bogor", "Depok", "Tangerang", "Bekasi"), 50000,
                              replace = T)
Age                 <- sample(c(19, 20, 23, 45, 59, 34, 37, 52, 41, 60), 50000, replace = T)
Academic            <- sample(c("J.School", "H.School", "Sarjana", "Magister", "Phd"), 50000,
                              replace = T)
Job                 <- sample(c("Teacher", "Chef", "Data Scientist", "Professor",       
                                "Scientist", "Mechanic", "Lecturer", "Designer",                                             "Architect", "Actuary"), 50000, replace = T)
Grade               <- sample(c("A", "B", "C", "D", "E"), 50000, replace = T)
Income              <- sample(c(5.5 , 10 , 11 , 4 , 9.5 , 20 , 16 , 10.5 , 8 , 7), 50000,
                              replace = T)
Spending            <- sample(c(2.3 , 3.1 , 7 , 1.5), 50000, replace = T)
Number_of_Children  <- sample(c(1, 6, 8, 10, 5, 3, 7, 9, 2, 4), 50000, replace = T) 
Private_Vehicle     <- sample(c("Motor", "Mobil", "ATV", "Kendaraan Umum", "Sepeda"), 50000,
                              replace = T)
Home                <- sample(c("Sewa", "Milik", "Kredit"), 50000, replace = T)

Data_Pelanggan      <- data.frame(Number, 
                                  Marital_Status,
                                  Address,
                                  Work_Location,
                                  Age,
                                  Academic,
                                  Job, 
                                  Grade, 
                                  Income, 
                                  Spending, 
                                  Number_of_Children, 
                                  Private_Vehicle, 
                                  Home)

Data_Pelanggan

Soal 2

Ringkasan Statistik penting seperti apa yang bisa Anda dapatkan dari kumpulan data Anda?

R

Data_Pelanggann = function (x)
{

Max = max(x)
Min = min(x)
Mean = round(sum(x)/length(x))
Median = x[median.default(x)] 
Mode = x[which.max(x)]
Variansi = sum((x-Mean)^2)/(length(x)-1) 
Standev = sqrt(sum((x-Mean)^2)/(length(x)-1))

 return(cat(c("Minimum =", Min, "\n", 
              "Maksimum =", Max,"\n", 
              "Median =",Median, "\n", 
              "Modus =", Mode, "\n", 
              "Rata-rata =", Mean, "\n", 
              "Varians = ", Variansi, "\n", 
              "Standar Deviasi =", Standev,"\n"
              )))
}

Data_Pelanggann(Data_Pelanggan$Number)

## Minimum = 1 
##  Maksimum = 50000 
##  Median = 25000 
##  Modus = 50000 
##  Rata-rata = 25000 
##  Varians =  208337500.250005 
##  Standar Deviasi = 14433.9010752466

Data_Pelanggann(Data_Pelanggan$Age)

## Minimum = 19 
##  Maksimum = 60 
##  Median = 52 
##  Modus = 60 
##  Rata-rata = 39 
##  Varians =  209.897297945959 
##  Standar Deviasi = 14.4878327553143

Data_Pelanggann(Data_Pelanggan$Income)

## Minimum = 4 
##  Maksimum = 20 
##  Median = 5.5 
##  Modus = 20 
##  Rata-rata = 10 
##  Varians =  20.8495069901398 
##  Standar Deviasi = 4.56612603747858

Data_Pelanggann(Data_Pelanggan$Spending)

## Minimum = 1.5 
##  Maksimum = 7 
##  Median = 7 
##  Modus = 7 
##  Rata-rata = 3 
##  Varians =  4.69323246464929 
##  Standar Deviasi = 2.16638696096734

Data_Pelanggann(Data_Pelanggan$Number_of_Children)

## Minimum = 1 
##  Maksimum = 10 
##  Median = 7 
##  Modus = 10 
##  Rata-rata = 6 
##  Varians =  8.53787075741515 
##  Standar Deviasi = 2.92196351062349

Soal 3

Menurut perhitungan dan analisis Anda, pelanggan mana yang potensial untuk Anda pertahankan?

R

Data_Pelanggan$Pendapatan = Data_Pelanggan$Income - Data_Pelanggan$Spending

x <- filter(Data_Pelanggan, Income >= 5)

library(DT)
datatable(x)

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

Referensi

ref 1
ref 2
ref 3

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