Algoritma & Struktur Data
~ Ujian Tengah Semester ~
| Kontak | : \(\downarrow\) |
| novialbl02@gmail.com | |
| https://www.instagram.com/novia_labola/ | |
| RPubs | https://rpubs.com/noviaanita/ |
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 ALGORITMA.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, # disini saya menggunakan fungsi ifelse untuk mencari variabel yang memiliki nilai high,medium, dan low
"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
Data$Booking_fee<-ifelse(Data$Price < 8000,
"5%",
ifelse(Data$Price >=8000 & Data$ Price < 9000,
"6%",
ifelse(Data$Price >=9000 & Data$ Price < 10000,
"7%",
ifelse(Data$Price >=10000 & Data$ Price < 11000,
"8%",
ifelse(Data$Price >=11000 & Data$ Price < 13000,
"9%",
ifelse(Data$Price >=13000 & Data$ Price <= 15000,
"10%",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
library(DT) # di coding ini pertama saya memanggil library dahulu setelah itu saya menggunakan fungsi for untuk mengumpulkan informasi penjualan dan juga menggunakan fungsi subset agar informasi penjualan keluar secara lengkap
sales = "Angel"
for(x in sales){
print(subset(Data, subset=(Marketing_Name == x)))
}## Id Marketing_Name Work_Exp City Cluster Price Date_Sales
## 1 1 Angel 1.3 Jakarta Peronia 13082 2018-07-07
## 51 51 Angel 1.3 Bekasi Lavesh 9280 2018-04-19
## 101 101 Angel 1.3 Bogor Asoka 7887 2019-02-19
## 151 151 Angel 1.3 Bogor Adara 9177 2018-10-03
## 201 201 Angel 1.3 Tengerang Tiara 10253 2018-04-25
## 251 251 Angel 1.3 Depok Neon 12497 2018-03-13
## 301 301 Angel 1.3 Depok Alindra 7662 2019-11-19
## 351 351 Angel 1.3 Bogor Sweethome 7260 2018-09-23
## 401 401 Angel 1.3 Bekasi Peronia 10957 2020-02-10
## 451 451 Angel 1.3 Tengerang Sweethome 9151 2018-10-14
## 501 501 Angel 1.3 Bekasi Alindra 13018 2019-06-27
## 551 551 Angel 1.3 Bogor Narada 8944 2018-06-14
## 601 601 Angel 1.3 Bekasi Alindra 14491 2019-03-18
## 651 651 Angel 1.3 Jakarta Narada 10845 2019-10-23
## 701 701 Angel 1.3 Jakarta Arana 10567 2019-08-18
## 751 751 Angel 1.3 Jakarta Permata 13994 2020-04-02
## 801 801 Angel 1.3 Jakarta Arana 12344 2018-07-25
## 851 851 Angel 1.3 Bogor Tiara 10600 2019-06-06
## 901 901 Angel 1.3 Bekasi Asera 7508 2018-02-20
## 951 951 Angel 1.3 Jakarta Victoria 9151 2019-08-12
## 1001 1001 Angel 1.3 Depok Alindra 8145 2019-01-10
## 1051 1051 Angel 1.3 Bekasi Primadona 10870 2020-02-13
## 1101 1101 Angel 1.3 Depok Winona 12735 2019-07-21
## 1151 1151 Angel 1.3 Bekasi Albasia 13032 2018-01-26
## 1201 1201 Angel 1.3 Bogor Permata 11576 2019-04-12
## 1251 1251 Angel 1.3 Bogor Alamanda 14931 2018-08-14
## 1301 1301 Angel 1.3 Bogor Teradamai 11207 2019-04-24
## 1351 1351 Angel 1.3 Bekasi Albasia 10097 2020-01-24
## 1401 1401 Angel 1.3 Bekasi Asoka 7044 2018-11-16
## 1451 1451 Angel 1.3 Jakarta Neon 13936 2019-06-04
## 1501 1501 Angel 1.3 Bekasi Palmyra 13376 2019-07-25
## 1551 1551 Angel 1.3 Depok Palmyra 14772 2018-07-16
## 1601 1601 Angel 1.3 Bogor Sweethome 13910 2020-07-29
## 1651 1651 Angel 1.3 Bogor Alamanda 10671 2018-10-27
## 1701 1701 Angel 1.3 Depok Neon 12687 2018-12-07
## 1751 1751 Angel 1.3 Tengerang Teradamai 9891 2019-04-25
## 1801 1801 Angel 1.3 Bogor Neon 11673 2018-06-20
## 1851 1851 Angel 1.3 Tengerang Peronia 8313 2018-10-01
## 1901 1901 Angel 1.3 Jakarta Asera 14924 2019-04-28
## 1951 1951 Angel 1.3 Depok Asoka 10859 2018-08-16
## 2001 2001 Angel 1.3 Bogor Arana 11543 2018-08-08
## 2051 2051 Angel 1.3 Depok Alamanda 11088 2018-01-24
## 2101 2101 Angel 1.3 Bogor Permata 8961 2019-05-11
## 2151 2151 Angel 1.3 Tengerang Palmyra 14412 2020-01-28
## 2201 2201 Angel 1.3 Depok Permata 12520 2020-03-20
## 2251 2251 Angel 1.3 Tengerang Palmyra 14091 2019-09-28
## 2301 2301 Angel 1.3 Bogor Albasia 10527 2020-09-03
## 2351 2351 Angel 1.3 Jakarta Sweethome 7084 2018-01-18
## 2401 2401 Angel 1.3 Bogor Peronia 9348 2019-03-09
## 2451 2451 Angel 1.3 Bogor Victoria 8702 2019-12-27
## 2501 2501 Angel 1.3 Jakarta Adara 13069 2019-06-05
## 2551 2551 Angel 1.3 Jakarta Albasia 12510 2018-12-27
## 2601 2601 Angel 1.3 Jakarta Permata 10146 2019-03-12
## 2651 2651 Angel 1.3 Tengerang Alamanda 14911 2019-05-18
## 2701 2701 Angel 1.3 Depok Tiara 14856 2020-07-09
## 2751 2751 Angel 1.3 Bekasi Mutiara 12404 2018-08-17
## 2801 2801 Angel 1.3 Depok Lavesh 10950 2020-02-03
## 2851 2851 Angel 1.3 Bogor Victoria 9628 2020-09-09
## 2901 2901 Angel 1.3 Jakarta Teradamai 9682 2020-01-11
## 2951 2951 Angel 1.3 Depok Arana 14634 2020-06-13
## 3001 3001 Angel 1.3 Tengerang Permata 9827 2020-04-26
## 3051 3051 Angel 1.3 Tengerang Alamanda 11573 2018-02-07
## 3101 3101 Angel 1.3 Depok Lavesh 11196 2020-04-23
## 3151 3151 Angel 1.3 Bogor Tiara 9261 2018-03-25
## 3201 3201 Angel 1.3 Jakarta Mutiara 7214 2019-02-21
## 3251 3251 Angel 1.3 Depok Lavesh 13807 2020-07-17
## 3301 3301 Angel 1.3 Tengerang Sweethome 12443 2018-08-17
## 3351 3351 Angel 1.3 Jakarta Sweethome 9318 2018-04-16
## 3401 3401 Angel 1.3 Bekasi Albasia 7622 2018-11-16
## 3451 3451 Angel 1.3 Bekasi Lavesh 12928 2020-02-25
## 3501 3501 Angel 1.3 Jakarta Victoria 7158 2020-05-13
## 3551 3551 Angel 1.3 Tengerang Permata 8453 2019-01-18
## 3601 3601 Angel 1.3 Bekasi Palmyra 10225 2020-05-23
## 3651 3651 Angel 1.3 Bogor Albasia 14277 2020-08-23
## 3701 3701 Angel 1.3 Tengerang Teradamai 11591 2020-03-05
## 3751 3751 Angel 1.3 Depok Tiara 14559 2018-08-24
## 3801 3801 Angel 1.3 Bekasi Asera 9602 2020-07-15
## 3851 3851 Angel 1.3 Bekasi Mutiara 12429 2020-09-04
## 3901 3901 Angel 1.3 Tengerang Alamanda 9563 2018-11-13
## 3951 3951 Angel 1.3 Bekasi Arana 11115 2018-06-26
## 4001 4001 Angel 1.3 Tengerang Albasia 14453 2018-12-05
## 4051 4051 Angel 1.3 Bogor Tiara 10250 2018-07-04
## 4101 4101 Angel 1.3 Jakarta Peronia 9480 2018-03-04
## 4151 4151 Angel 1.3 Bekasi Narada 8152 2019-10-15
## 4201 4201 Angel 1.3 Bogor Albasia 10269 2020-04-09
## 4251 4251 Angel 1.3 Bogor Asoka 12287 2018-01-26
## 4301 4301 Angel 1.3 Depok Teradamai 12586 2018-07-31
## 4351 4351 Angel 1.3 Bogor Narada 7549 2020-01-31
## 4401 4401 Angel 1.3 Tengerang Mutiara 11061 2019-10-02
## 4451 4451 Angel 1.3 Bekasi Lavesh 9667 2018-03-23
## 4501 4501 Angel 1.3 Bekasi Arana 8756 2020-01-05
## 4551 4551 Angel 1.3 Bogor Albasia 11164 2018-11-22
## 4601 4601 Angel 1.3 Depok Palmyra 8518 2019-03-11
## 4651 4651 Angel 1.3 Bogor Arana 14234 2020-04-01
## 4701 4701 Angel 1.3 Bekasi Adara 10415 2018-08-24
## 4751 4751 Angel 1.3 Bekasi Asera 12613 2018-05-26
## 4801 4801 Angel 1.3 Bekasi Sweethome 7526 2019-05-23
## 4851 4851 Angel 1.3 Bogor Palmyra 9164 2020-08-20
## 4901 4901 Angel 1.3 Tengerang Arana 10216 2018-08-02
## 4951 4951 Angel 1.3 Jakarta Primadona 14059 2018-10-28
## 5001 5001 Angel 1.3 Jakarta Asera 11794 2019-05-07
## 5051 5051 Angel 1.3 Bogor Asoka 8110 2019-09-26
## 5101 5101 Angel 1.3 Bekasi Teradamai 8622 2019-04-03
## 5151 5151 Angel 1.3 Bekasi Palmyra 11915 2018-06-19
## 5201 5201 Angel 1.3 Tengerang Winona 13396 2019-03-17
## 5251 5251 Angel 1.3 Bogor Asera 8525 2019-03-13
## 5301 5301 Angel 1.3 Depok Narada 12964 2019-03-20
## 5351 5351 Angel 1.3 Bogor Adara 12277 2018-10-29
## 5401 5401 Angel 1.3 Depok Teradamai 9482 2019-05-08
## 5451 5451 Angel 1.3 Bogor Winona 11506 2020-02-04
## 5501 5501 Angel 1.3 Jakarta Lavesh 10840 2018-04-25
## 5551 5551 Angel 1.3 Jakarta Arana 10963 2018-12-19
## 5601 5601 Angel 1.3 Tengerang Sweethome 12646 2020-04-17
## 5651 5651 Angel 1.3 Bekasi Arana 7130 2019-09-26
## 5701 5701 Angel 1.3 Bekasi Alindra 13138 2018-01-16
## 5751 5751 Angel 1.3 Bogor Peronia 8325 2018-02-27
## 5801 5801 Angel 1.3 Jakarta Victoria 14627 2019-11-06
## 5851 5851 Angel 1.3 Bekasi Arana 8573 2018-05-11
## 5901 5901 Angel 1.3 Jakarta Palmyra 14400 2018-12-10
## 5951 5951 Angel 1.3 Jakarta Sweethome 12223 2020-06-27
## 6001 6001 Angel 1.3 Tengerang Permata 11055 2020-01-30
## 6051 6051 Angel 1.3 Bogor Alindra 9159 2020-01-10
## 6101 6101 Angel 1.3 Depok Winona 9112 2019-06-02
## 6151 6151 Angel 1.3 Bekasi Alindra 13824 2019-08-20
## 6201 6201 Angel 1.3 Tengerang Lavesh 11151 2018-05-28
## 6251 6251 Angel 1.3 Jakarta Arana 12936 2019-09-04
## 6301 6301 Angel 1.3 Bekasi Alamanda 13197 2018-02-19
## 6351 6351 Angel 1.3 Bekasi Lavesh 7063 2019-10-26
## 6401 6401 Angel 1.3 Jakarta Permata 7427 2018-02-03
## 6451 6451 Angel 1.3 Tengerang Teradamai 11645 2020-08-12
## 6501 6501 Angel 1.3 Jakarta Narada 11854 2018-10-09
## 6551 6551 Angel 1.3 Bogor Mutiara 8478 2019-06-17
## 6601 6601 Angel 1.3 Bogor Alindra 14320 2019-02-09
## 6651 6651 Angel 1.3 Depok Albasia 7506 2018-12-18
## 6701 6701 Angel 1.3 Tengerang Lavesh 10019 2019-03-11
## 6751 6751 Angel 1.3 Jakarta Albasia 11835 2018-04-25
## 6801 6801 Angel 1.3 Jakarta Adara 13536 2019-02-10
## 6851 6851 Angel 1.3 Bekasi Neon 8463 2020-02-24
## 6901 6901 Angel 1.3 Jakarta Winona 7360 2019-06-21
## 6951 6951 Angel 1.3 Tengerang Neon 9025 2018-07-07
## 7001 7001 Angel 1.3 Tengerang Neon 12864 2019-05-18
## 7051 7051 Angel 1.3 Bogor Narada 13708 2019-03-23
## 7101 7101 Angel 1.3 Tengerang Winona 13047 2019-07-11
## 7151 7151 Angel 1.3 Tengerang Alindra 9480 2018-07-05
## 7201 7201 Angel 1.3 Depok Victoria 7301 2020-07-02
## 7251 7251 Angel 1.3 Jakarta Adara 13042 2018-05-13
## 7301 7301 Angel 1.3 Depok Winona 9149 2019-10-23
## 7351 7351 Angel 1.3 Jakarta Permata 7336 2019-07-16
## 7401 7401 Angel 1.3 Depok Peronia 10021 2018-05-13
## 7451 7451 Angel 1.3 Depok Mutiara 9190 2019-10-01
## 7501 7501 Angel 1.3 Depok Alamanda 10967 2019-08-27
## 7551 7551 Angel 1.3 Tengerang Alindra 11144 2019-05-19
## 7601 7601 Angel 1.3 Bogor Arana 13903 2018-06-02
## 7651 7651 Angel 1.3 Bogor Mutiara 7714 2019-12-21
## 7701 7701 Angel 1.3 Bogor Alindra 10521 2019-11-06
## 7751 7751 Angel 1.3 Bekasi Arana 9761 2018-04-28
## 7801 7801 Angel 1.3 Tengerang Albasia 8223 2018-04-27
## 7851 7851 Angel 1.3 Depok Victoria 9183 2020-02-05
## 7901 7901 Angel 1.3 Depok Asoka 10890 2018-12-07
## 7951 7951 Angel 1.3 Bekasi Neon 10759 2018-08-09
## 8001 8001 Angel 1.3 Bekasi Primadona 14837 2020-09-12
## 8051 8051 Angel 1.3 Bekasi Primadona 9588 2018-08-22
## 8101 8101 Angel 1.3 Bekasi Sweethome 10896 2019-11-02
## 8151 8151 Angel 1.3 Bekasi Arana 7369 2019-07-25
## 8201 8201 Angel 1.3 Depok Mutiara 7818 2018-11-28
## 8251 8251 Angel 1.3 Jakarta Permata 13201 2019-04-15
## 8301 8301 Angel 1.3 Bogor Albasia 11982 2019-12-12
## 8351 8351 Angel 1.3 Bekasi Winona 13540 2018-08-26
## 8401 8401 Angel 1.3 Bekasi Sweethome 12162 2020-05-06
## 8451 8451 Angel 1.3 Depok Lavesh 7905 2018-11-30
## 8501 8501 Angel 1.3 Bekasi Lavesh 11347 2019-11-09
## 8551 8551 Angel 1.3 Tengerang Sweethome 11523 2020-08-31
## 8601 8601 Angel 1.3 Bekasi Primadona 8426 2019-10-19
## 8651 8651 Angel 1.3 Jakarta Adara 12219 2020-04-15
## 8701 8701 Angel 1.3 Bogor Tiara 13649 2019-04-03
## 8751 8751 Angel 1.3 Bogor Asoka 13446 2019-08-06
## 8801 8801 Angel 1.3 Bogor Sweethome 14072 2019-06-20
## 8851 8851 Angel 1.3 Depok Neon 13021 2020-02-03
## 8901 8901 Angel 1.3 Tengerang Teradamai 11241 2018-06-13
## 8951 8951 Angel 1.3 Depok Teradamai 8919 2018-08-25
## 9001 9001 Angel 1.3 Bogor Lavesh 14490 2020-06-26
## 9051 9051 Angel 1.3 Depok Asera 8234 2020-02-20
## 9101 9101 Angel 1.3 Depok Tiara 7115 2018-03-14
## 9151 9151 Angel 1.3 Jakarta Palmyra 7614 2020-07-22
## 9201 9201 Angel 1.3 Tengerang Arana 7685 2019-10-01
## 9251 9251 Angel 1.3 Bekasi Adara 11082 2018-11-28
## 9301 9301 Angel 1.3 Jakarta Adara 8483 2019-08-27
## 9351 9351 Angel 1.3 Bogor Peronia 8121 2018-05-17
## 9401 9401 Angel 1.3 Bekasi Peronia 8645 2018-03-13
## 9451 9451 Angel 1.3 Jakarta Palmyra 14024 2019-08-22
## 9501 9501 Angel 1.3 Jakarta Tiara 10986 2018-06-10
## 9551 9551 Angel 1.3 Bekasi Neon 7832 2020-09-24
## 9601 9601 Angel 1.3 Bekasi Winona 10930 2019-02-03
## 9651 9651 Angel 1.3 Tengerang Peronia 11508 2018-09-12
## 9701 9701 Angel 1.3 Bogor Victoria 14726 2018-12-19
## 9751 9751 Angel 1.3 Bogor Lavesh 14754 2018-09-13
## 9801 9801 Angel 1.3 Bekasi Adara 13782 2018-11-02
## 9851 9851 Angel 1.3 Jakarta Tiara 7375 2020-06-06
## 9901 9901 Angel 1.3 Tengerang Victoria 13748 2018-12-30
## 9951 9951 Angel 1.3 Tengerang Permata 9881 2020-05-05
## Advertisement kelas Booking_fee
## 1 6 High 10%
## 51 20 Low 7%
## 101 8 Low 5%
## 151 4 Low 7%
## 201 19 Medium 8%
## 251 4 High 9%
## 301 19 Low 5%
## 351 19 Low 5%
## 401 6 Medium 8%
## 451 2 Low 7%
## 501 17 High 10%
## 551 5 Low 6%
## 601 8 High 10%
## 651 11 Medium 8%
## 701 17 Medium 8%
## 751 10 High 10%
## 801 18 High 9%
## 851 17 Medium 8%
## 901 9 Low 5%
## 951 11 Low 7%
## 1001 7 Low 6%
## 1051 18 Medium 8%
## 1101 14 High 9%
## 1151 18 High 10%
## 1201 20 Medium 9%
## 1251 2 High 10%
## 1301 10 Medium 9%
## 1351 5 Medium 8%
## 1401 6 Low 5%
## 1451 13 High 10%
## 1501 2 High 10%
## 1551 19 High 10%
## 1601 1 High 10%
## 1651 16 Medium 8%
## 1701 1 High 9%
## 1751 20 Low 7%
## 1801 19 Medium 9%
## 1851 15 Low 6%
## 1901 18 High 10%
## 1951 13 Medium 8%
## 2001 10 Medium 9%
## 2051 4 Medium 9%
## 2101 14 Low 6%
## 2151 16 High 10%
## 2201 7 High 9%
## 2251 8 High 10%
## 2301 10 Medium 8%
## 2351 8 Low 5%
## 2401 17 Low 7%
## 2451 19 Low 6%
## 2501 3 High 10%
## 2551 7 High 9%
## 2601 7 Medium 8%
## 2651 10 High 10%
## 2701 8 High 10%
## 2751 9 High 9%
## 2801 10 Medium 8%
## 2851 13 Low 7%
## 2901 20 Low 7%
## 2951 12 High 10%
## 3001 4 Low 7%
## 3051 20 Medium 9%
## 3101 9 Medium 9%
## 3151 11 Low 7%
## 3201 5 Low 5%
## 3251 16 High 10%
## 3301 17 High 9%
## 3351 17 Low 7%
## 3401 12 Low 5%
## 3451 14 High 9%
## 3501 12 Low 5%
## 3551 13 Low 6%
## 3601 7 Medium 8%
## 3651 1 High 10%
## 3701 18 Medium 9%
## 3751 5 High 10%
## 3801 12 Low 7%
## 3851 17 High 9%
## 3901 13 Low 7%
## 3951 6 Medium 9%
## 4001 2 High 10%
## 4051 16 Medium 8%
## 4101 4 Low 7%
## 4151 2 Low 6%
## 4201 12 Medium 8%
## 4251 12 High 9%
## 4301 7 High 9%
## 4351 2 Low 5%
## 4401 10 Medium 9%
## 4451 6 Low 7%
## 4501 2 Low 6%
## 4551 5 Medium 9%
## 4601 3 Low 6%
## 4651 5 High 10%
## 4701 11 Medium 8%
## 4751 14 High 9%
## 4801 13 Low 5%
## 4851 19 Low 7%
## 4901 16 Medium 8%
## 4951 15 High 10%
## 5001 20 Medium 9%
## 5051 10 Low 6%
## 5101 10 Low 6%
## 5151 13 Medium 9%
## 5201 20 High 10%
## 5251 1 Low 6%
## 5301 7 High 9%
## 5351 20 High 9%
## 5401 17 Low 7%
## 5451 2 Medium 9%
## 5501 20 Medium 8%
## 5551 14 Medium 8%
## 5601 8 High 9%
## 5651 6 Low 5%
## 5701 3 High 10%
## 5751 16 Low 6%
## 5801 15 High 10%
## 5851 18 Low 6%
## 5901 7 High 10%
## 5951 11 High 9%
## 6001 7 Medium 9%
## 6051 17 Low 7%
## 6101 2 Low 7%
## 6151 4 High 10%
## 6201 3 Medium 9%
## 6251 1 High 9%
## 6301 3 High 10%
## 6351 4 Low 5%
## 6401 15 Low 5%
## 6451 16 Medium 9%
## 6501 9 Medium 9%
## 6551 17 Low 6%
## 6601 5 High 10%
## 6651 14 Low 5%
## 6701 16 Medium 8%
## 6751 1 Medium 9%
## 6801 14 High 10%
## 6851 4 Low 6%
## 6901 8 Low 5%
## 6951 20 Low 7%
## 7001 3 High 9%
## 7051 4 High 10%
## 7101 8 High 10%
## 7151 6 Low 7%
## 7201 7 Low 5%
## 7251 7 High 10%
## 7301 3 Low 7%
## 7351 18 Low 5%
## 7401 2 Medium 8%
## 7451 2 Low 7%
## 7501 3 Medium 8%
## 7551 3 Medium 9%
## 7601 11 High 10%
## 7651 4 Low 5%
## 7701 7 Medium 8%
## 7751 15 Low 7%
## 7801 14 Low 6%
## 7851 18 Low 7%
## 7901 19 Medium 8%
## 7951 19 Medium 8%
## 8001 20 High 10%
## 8051 6 Low 7%
## 8101 10 Medium 8%
## 8151 11 Low 5%
## 8201 11 Low 5%
## 8251 6 High 10%
## 8301 18 Medium 9%
## 8351 15 High 10%
## 8401 9 High 9%
## 8451 9 Low 5%
## 8501 12 Medium 9%
## 8551 20 Medium 9%
## 8601 19 Low 6%
## 8651 4 High 9%
## 8701 15 High 10%
## 8751 11 High 10%
## 8801 10 High 10%
## 8851 8 High 10%
## 8901 10 Medium 9%
## 8951 9 Low 6%
## 9001 9 High 10%
## 9051 3 Low 6%
## 9101 16 Low 5%
## 9151 19 Low 5%
## 9201 12 Low 5%
## 9251 16 Medium 9%
## 9301 19 Low 6%
## 9351 2 Low 6%
## 9401 20 Low 6%
## 9451 9 High 10%
## 9501 12 Medium 8%
## 9551 5 Low 5%
## 9601 5 Medium 8%
## 9651 11 Medium 9%
## 9701 6 High 10%
## 9751 12 High 10%
## 9801 1 High 10%
## 9851 1 Low 5%
## 9901 20 High 10%
## 9951 17 Low 7%Soal 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 $4 setiap kali iklan.
- Hitung rata-rata biaya iklan untuk setiap marketing di Perusahaan tersebut.
- Hitung Total Pendapatan (dalam Bulanan)
R
# Siapa nama marketing pemasaran terbaik?
nama_sales = c("Angel","Sherly","Vanessa","Irene","Julian",
"Jeffry","Nikita","Kefas","Siana","Lala",
"Fallen","Ardifo","Kevin","Juen","Jerrel",
"Imelda","Widi","Theodora","Elvani","Jonathan",
"Sofia","Abraham","Siti","Niko","Sefli",
"Bene", "Diana", "Pupe", "Andi", "Tatha",
"Endri", "Monika", "Hans", "Debora","Hanifa",
"James", "Jihan", "Friska","Ardiwan", "Bakti",
"Anthon","Amry", "Wiwik", "Bastian", "Budi",
"Leo","Simon","Matius","Arry", "Eliando")
n <- Data$Marketing_Name
Angel = subset(Data, (Marketing_Name == "Angel"))
Sherly = subset(Data, (Marketing_Name == "Sherly"))
Vanessa = subset(Data, (Marketing_Name == "Vanessa"))
Irene = subset(Data, (Marketing_Name == "Irene"))
Julian = subset(Data, (Marketing_Name == "Julian"))
Jeffry = subset(Data, (Marketing_Name == "Jeffry"))
Nikita = subset(Data, (Marketing_Name == "Nikita"))
Kefas = subset(Data, (Marketing_Name == "Kefas"))
Siana = subset(Data, (Marketing_Name == "Siana"))
Lala = subset(Data, (Marketing_Name == "Lala"))
Fallen = subset(Data, (Marketing_Name == "Fallen"))
Ardifo = subset(Data, (Marketing_Name == "Ardifo"))
Kevin = subset(Data, (Marketing_Name == "Kevin"))
Juen = subset(Data, (Marketing_Name == "Juen"))
Jerrel = subset(Data, (Marketing_Name == "Jerrel"))
Imelda = subset(Data, (Marketing_Name == "Imelda"))
Widi = subset(Data, (Marketing_Name == "Widi"))
Theodora = subset(Data, (Marketing_Name == "Theodora"))
Elvani = subset(Data, (Marketing_Name == "Elvani"))
Jonathan = subset(Data, (Marketing_Name == "Jonathan"))
Sofia = subset(Data, (Marketing_Name == "Sofia"))
Abraham = subset(Data, (Marketing_Name == "Abraham"))
Siti = subset(Data, (Marketing_Name == "Siti"))
Niko = subset(Data, (Marketing_Name == "Niko"))
Sefli = subset(Data, (Marketing_Name == "Sefli"))
Bene = subset(Data, (Marketing_Name == "Bene"))
Diana = subset(Data, (Marketing_Name == "Diana"))
Pupe = subset(Data, (Marketing_Name == "Pupe"))
Andi = subset(Data, (Marketing_Name == "Andi"))
Tatha = subset(Data, (Marketing_Name == "Tatha"))
Endri = subset(Data, (Marketing_Name == "Endri"))
Monika = subset(Data, (Marketing_Name == "Monika"))
Hans = subset(Data, (Marketing_Name == "Hans"))
Debora = subset(Data, (Marketing_Name == "Debora"))
Hanifa = subset(Data, (Marketing_Name == "Hanifa"))
James = subset(Data, (Marketing_Name == "James"))
Jihan = subset(Data, (Marketing_Name == "Jihan"))
Friska = subset(Data, (Marketing_Name == "Friska"))
Ardiwan = subset(Data, (Marketing_Name == "Ardiwan"))
Bakti = subset(Data, (Marketing_Name == "Bakti"))
Anthon = subset(Data, (Marketing_Name == "Anthon"))
Amry = subset(Data, (Marketing_Name == "Amry"))
Wiwik = subset(Data, (Marketing_Name == "Wiwik"))
Bastian = subset(Data, (Marketing_Name == "Bastian"))
Budi = subset(Data, (Marketing_Name == "Budi"))
Leo = subset(Data, (Marketing_Name == "Leo"))
Simon = subset(Data, (Marketing_Name == "Simon"))
Matius = subset(Data, (Marketing_Name == "Matius"))
Arry = subset(Data, (Marketing_Name == "Arry"))
Eliando = subset(Data, (Marketing_Name == "Eliando"))
y = 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(nama_sales,
y)
marketing_ # di coding ini saya membuat data manual karena saya tidak tahu cara olah data otomatisa = data.frame(n,y) # setelah itu saya membuat data frame dan menggunakan fungsi which.max untuk mengembalikan posisi
b = which.max(a$y)
c = a[b,]
library(DT)
datatable(c)# Kota dan Cluster mana yang paling menguntungkan?
a <- Data$City
Jakarta = subset(Data, City == "Jakarta")
Bogor = subset(Data, City == "Bogor")
Depok = subset(Data, City == "Depok")
Tangerang = subset(Data, City == "Tangerang")
Bekasi = subset(Data, City == "Bekasi")
b = c(sum(Jakarta$Price),sum(Bogor$Price),sum(Depok$Price),sum(Tangerang$Price),sum(Bekasi$Price))
c = data.frame(a,b)
d = which.max(c$b)
e = c[d,]
library(DT)
datatable(e)# Hitung total biaya iklan Anda, jika Anda harus membayarnya $4 setiap kali iklan.
Advertise=subset(Data, subset=(Marketing_Name=="Angel")) # disini saya menggunakan fungsi subset, advertise, advertisement dan sum untuk mencari total biaya iklan
Biaya_Iklan=(Advertise$Advertisement*4)
Totalbiaya=print(sum(Biaya_Iklan))## [1] 8492# Hitung rata-rata biaya iklan untuk setiap marketing di Perusahaan tersebut.
Data$Biaya_Iklan <- ifelse(Data$Advertisement >= 1, Data$Advertisement*4, 0)
x = c("Angel","Sherly","Vanessa","Irene","Julian",
"Jeffry","Nikita","Kefas","Siana","Lala",
"Fallen","Ardifo","Kevin","Juen","Jerrel",
"Imelda","Widi","Theodora","Elvani","Jonathan",
"Sofia","Abraham","Siti","Niko","Sefli",
"Bene", "Diana", "Pupe", "Andi", "Tatha",
"Endri", "Monika", "Hans", "Debora","Hanifa",
"James", "Jihan", "Friska","Ardiwan", "Bakti",
"Anthon","Amry", "Wiwik", "Bastian", "Budi",
"Leo","Simon","Matius","Arry", "Eliando")
for (i in x){
r <- subset(Data, Marketing_Name == i)
s <- sum(r$Biaya_Iklan)
print(cat(sum(s)/length(r$Id), (cat(i, "Rerata Biaya : "))))
}## Angel Rerata Biaya : 42.46NULL
## Sherly Rerata Biaya : 39.88NULL
## Vanessa Rerata Biaya : 43.9NULL
## Irene Rerata Biaya : 43.86NULL
## Julian Rerata Biaya : 42.34NULL
## Jeffry Rerata Biaya : 42.5NULL
## Nikita Rerata Biaya : 44.94NULL
## Kefas Rerata Biaya : 40.36NULL
## Siana Rerata Biaya : 42.54NULL
## Lala Rerata Biaya : 40.92NULL
## Fallen Rerata Biaya : 40.62NULL
## Ardifo Rerata Biaya : 43.14NULL
## Kevin Rerata Biaya : 45.8NULL
## Juen Rerata Biaya : 40.56NULL
## Jerrel Rerata Biaya : 43.78NULL
## Imelda Rerata Biaya : 43.02NULL
## Widi Rerata Biaya : 42.78NULL
## Theodora Rerata Biaya : 46.02NULL
## Elvani Rerata Biaya : 42.74NULL
## Jonathan Rerata Biaya : 44.66NULL
## Sofia Rerata Biaya : 37.44NULL
## Abraham Rerata Biaya : 40.1NULL
## Siti Rerata Biaya : 43.82NULL
## Niko Rerata Biaya : 41.34NULL
## Sefli Rerata Biaya : 39.44NULL
## Bene Rerata Biaya : 44.26NULL
## Diana Rerata Biaya : 41.54NULL
## Pupe Rerata Biaya : 39.52NULL
## Andi Rerata Biaya : 40.32NULL
## Tatha Rerata Biaya : 41.8NULL
## Endri Rerata Biaya : 42.04NULL
## Monika Rerata Biaya : 39.68NULL
## Hans Rerata Biaya : 41.92NULL
## Debora Rerata Biaya : 41.98NULL
## Hanifa Rerata Biaya : 42.8NULL
## James Rerata Biaya : 41.86NULL
## Jihan Rerata Biaya : 41.6NULL
## Friska Rerata Biaya : 43.84NULL
## Ardiwan Rerata Biaya : 43.46NULL
## Bakti Rerata Biaya : 42.98NULL
## Anthon Rerata Biaya : 40.12NULL
## Amry Rerata Biaya : 46.42NULL
## Wiwik Rerata Biaya : 41.96NULL
## Bastian Rerata Biaya : 43.76NULL
## Budi Rerata Biaya : 41.3NULL
## Leo Rerata Biaya : 43.4NULL
## Simon Rerata Biaya : 42.48NULL
## Matius Rerata Biaya : 44.24NULL
## Arry Rerata Biaya : 43.84NULL
## Eliando Rerata Biaya : 39.32NULLKasus 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
Data <- (1:50000)
Marital_Status <- sample(c("Yes","No"), 50000, replace = T)
Address <- sample(c("Bekasi", "Jakarta", "Bogor", "Depok", "Tangerang"), 50000, replace = T)
Work_Location <- sample(c("Bekasi", "Jakarta", "Bogor", "Depok", "Tangerang") , 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("Student", "Barista")),
ifelse(Academic == "H.School", sample(c("Waitress", "Sales Promotion")),
ifelse(Academic == "Sarjana", sample(c("Guru","dokter")),
ifelse(Academic == "Magister", sample(c("Dosen", "Manager")),
ifelse(Academic == "Phd", sample(c("Pengusaha", "Peneliti")),0)))))
Grade <- sample(c("A", "B", "C", "D", "E"), 50000, replace = T)
Income <- ifelse(Job == "Student", 0,
ifelse(Job == "Barista", 2500000,
ifelse(Job == "Waitress", 2000000,
ifelse(Job == "Sales Promotion", 2000000,
ifelse(Job == "Guru", 3500000,
ifelse(Job == "dokter", 5000000,
ifelse(Job == "Dosen", 6000000,
ifelse(Job == "Manager", 5000000,
ifelse(Job == "Pengusaha", 10000000,
ifelse(Job == "Peneliti", 10000000, 0))))))))))
Spending <- sample(c(500000:1000000), 50000, replace = T)
Private_vehicle <- sample(c("Mobil", "Sepeda Motor", "Umum"), 50000, replace = T)
Home <- sample(c("Sewa", "Milik", "Kredit"), 50000, replace = T)
Informasi_Pelanggan <- data.frame(Marital_Status,
Address,
Work_Location,
Age,
Academic,
Job,
Grade,
Income,
Spending,
Private_vehicle,
Home)
library(DT)
datatable(Informasi_Pelanggan) # disini saya membuat coding mengikuti data pada kasus 1## 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.htmlSoal 2
Ringkasan Statistik penting seperti apa yang bisa Anda dapatkan dari kumpulan data Anda?
R
Ringkasan=summary(Informasi_Pelanggan)
datatable(Ringkasan)Soal 3
Menurut perhitungan dan analisis Anda, pelanggan mana yang potensial untuk Anda pertahankan?
R
Informasi_Pelanggan$Penghasilan = Informasi_Pelanggan$Income - Informasi_Pelanggan$Spending
y <- subset(Informasi_Pelanggan, Penghasilan >= 3000000)
library(DT)
datatable(y)## Warning in instance$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