Algoritma & Struktur Data
~ Ujian Tengah Semester ~
| Kontak | : \(\downarrow\) |
| yeni.arwanti@student.matanauniversity.com | |
| RPubs | https://rpubs.com/yeninawn/ |
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.
R
library(DT)
Data$Kelas=ifelse((Data$Price>12000),"High",
ifelse((Data$Price>=10000 & Data$Price<=12000),"Medium",
"Low"
))
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.
R
Data$Booking_Fee=ifelse((Data$Price<8000),(5/100)*Data$Price,
ifelse((Data$Price>=8000 & Data$Price<9000),(6/100)*Data$Price,
ifelse((Data$Price>=9000 & Data$Price<10000),(7/100)*Data$Price,
ifelse((Data$Price>=10000 & Data$Price<11000),(8/100)*Data$Price,
ifelse((Data$Price>=11000 & Data$Price<13000),(9/100)*Data$Price, (10/100)*Data$Price)))))
subset(Data, select=c(6,10))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
for (x in "Lala"){
print(subset(Data, subset=(Marketing_Name == x)))} # tuliskan koding R kalian disini## Id Marketing_Name Work_Exp City Cluster Price Date_Sales
## 10 10 Lala 7.3 Bogor Mutiara 9885 2019-07-31
## 60 60 Lala 7.3 Bogor Neon 10491 2020-07-31
## 110 110 Lala 7.3 Bogor Palmyra 13485 2020-01-10
## 160 160 Lala 7.3 Jakarta Asera 11623 2018-02-24
## 210 210 Lala 7.3 Tengerang Tiara 9010 2019-06-30
## 260 260 Lala 7.3 Depok Winona 7912 2018-09-09
## 310 310 Lala 7.3 Tengerang Mutiara 9015 2019-04-16
## 360 360 Lala 7.3 Depok Permata 14869 2019-03-23
## 410 410 Lala 7.3 Depok Victoria 7402 2020-08-10
## 460 460 Lala 7.3 Depok Palmyra 14466 2020-08-23
## 510 510 Lala 7.3 Tengerang Palmyra 12842 2019-07-14
## 560 560 Lala 7.3 Depok Sweethome 7219 2018-02-17
## 610 610 Lala 7.3 Jakarta Peronia 13413 2018-03-01
## 660 660 Lala 7.3 Tengerang Lavesh 10921 2019-12-21
## 710 710 Lala 7.3 Depok Peronia 8511 2019-10-06
## 760 760 Lala 7.3 Bekasi Victoria 11409 2019-08-03
## 810 810 Lala 7.3 Bekasi Tiara 9455 2019-09-10
## 860 860 Lala 7.3 Bogor Mutiara 13344 2020-02-08
## 910 910 Lala 7.3 Bekasi Asoka 9111 2019-01-19
## 960 960 Lala 7.3 Tengerang Primadona 11228 2020-08-29
## 1010 1010 Lala 7.3 Bogor Arana 8139 2018-08-23
## 1060 1060 Lala 7.3 Bekasi Albasia 10928 2020-06-13
## 1110 1110 Lala 7.3 Depok Adara 9594 2018-01-18
## 1160 1160 Lala 7.3 Depok Albasia 8957 2019-08-20
## 1210 1210 Lala 7.3 Jakarta Palmyra 12032 2019-01-08
## 1260 1260 Lala 7.3 Tengerang Neon 13620 2019-06-01
## 1310 1310 Lala 7.3 Bekasi Winona 9409 2018-12-18
## 1360 1360 Lala 7.3 Tengerang Tiara 12692 2019-10-29
## 1410 1410 Lala 7.3 Jakarta Winona 11961 2020-05-31
## 1460 1460 Lala 7.3 Bogor Peronia 11007 2019-08-17
## 1510 1510 Lala 7.3 Bekasi Albasia 10781 2018-07-24
## 1560 1560 Lala 7.3 Jakarta Asera 12036 2020-03-31
## 1610 1610 Lala 7.3 Bogor Alindra 10868 2018-04-26
## 1660 1660 Lala 7.3 Tengerang Peronia 7035 2018-02-25
## 1710 1710 Lala 7.3 Bekasi Tiara 11666 2018-05-03
## 1760 1760 Lala 7.3 Tengerang Victoria 11589 2020-02-28
## 1810 1810 Lala 7.3 Bekasi Adara 7080 2020-03-26
## 1860 1860 Lala 7.3 Bekasi Alamanda 7864 2020-02-10
## 1910 1910 Lala 7.3 Bogor Lavesh 13273 2019-10-29
## 1960 1960 Lala 7.3 Depok Sweethome 8333 2019-04-25
## 2010 2010 Lala 7.3 Jakarta Alamanda 12517 2018-09-04
## 2060 2060 Lala 7.3 Tengerang Asera 9171 2020-04-08
## 2110 2110 Lala 7.3 Tengerang Sweethome 12506 2020-09-24
## 2160 2160 Lala 7.3 Jakarta Neon 7349 2019-02-12
## 2210 2210 Lala 7.3 Bekasi Arana 14828 2018-06-26
## 2260 2260 Lala 7.3 Bekasi Permata 7214 2018-10-25
## 2310 2310 Lala 7.3 Bekasi Permata 13406 2018-07-04
## 2360 2360 Lala 7.3 Bogor Peronia 9417 2018-02-19
## 2410 2410 Lala 7.3 Bekasi Primadona 12046 2018-06-05
## 2460 2460 Lala 7.3 Jakarta Tiara 12513 2018-08-07
## 2510 2510 Lala 7.3 Bogor Victoria 7264 2020-09-15
## 2560 2560 Lala 7.3 Jakarta Primadona 9088 2018-10-21
## 2610 2610 Lala 7.3 Jakarta Asera 8720 2019-08-25
## 2660 2660 Lala 7.3 Jakarta Narada 10187 2018-10-19
## 2710 2710 Lala 7.3 Tengerang Alindra 10743 2020-09-24
## 2760 2760 Lala 7.3 Depok Sweethome 11023 2018-11-01
## 2810 2810 Lala 7.3 Depok Sweethome 10226 2020-04-22
## 2860 2860 Lala 7.3 Depok Neon 8831 2019-04-16
## 2910 2910 Lala 7.3 Bekasi Adara 13047 2018-08-07
## 2960 2960 Lala 7.3 Bekasi Mutiara 7271 2018-02-18
## 3010 3010 Lala 7.3 Bekasi Arana 11847 2020-06-01
## 3060 3060 Lala 7.3 Jakarta Teradamai 7806 2019-08-23
## 3110 3110 Lala 7.3 Depok Sweethome 12412 2019-12-31
## 3160 3160 Lala 7.3 Jakarta Alamanda 7847 2020-01-18
## 3210 3210 Lala 7.3 Bekasi Lavesh 14342 2019-02-10
## 3260 3260 Lala 7.3 Bekasi Peronia 8977 2019-09-27
## 3310 3310 Lala 7.3 Tengerang Primadona 7101 2019-12-04
## 3360 3360 Lala 7.3 Jakarta Neon 12343 2020-07-14
## 3410 3410 Lala 7.3 Bekasi Permata 14715 2019-12-15
## 3460 3460 Lala 7.3 Bekasi Asera 9912 2020-07-09
## 3510 3510 Lala 7.3 Depok Permata 11653 2019-03-27
## 3560 3560 Lala 7.3 Jakarta Narada 9375 2019-06-13
## 3610 3610 Lala 7.3 Depok Palmyra 12309 2019-07-22
## 3660 3660 Lala 7.3 Bogor Mutiara 9306 2020-06-23
## 3710 3710 Lala 7.3 Tengerang Lavesh 11358 2020-08-08
## 3760 3760 Lala 7.3 Depok Albasia 11370 2019-10-01
## 3810 3810 Lala 7.3 Bekasi Victoria 10070 2020-08-11
## 3860 3860 Lala 7.3 Bekasi Arana 9088 2019-11-15
## 3910 3910 Lala 7.3 Bekasi Palmyra 8680 2018-03-11
## 3960 3960 Lala 7.3 Bekasi Lavesh 13806 2018-08-31
## 4010 4010 Lala 7.3 Bogor Narada 7595 2019-02-09
## 4060 4060 Lala 7.3 Jakarta Tiara 11971 2020-01-30
## 4110 4110 Lala 7.3 Bekasi Mutiara 7500 2019-04-16
## 4160 4160 Lala 7.3 Bekasi Lavesh 13432 2018-05-08
## 4210 4210 Lala 7.3 Bogor Palmyra 10037 2018-01-28
## 4260 4260 Lala 7.3 Bekasi Lavesh 10222 2018-04-10
## 4310 4310 Lala 7.3 Jakarta Alindra 11519 2019-08-07
## 4360 4360 Lala 7.3 Bekasi Permata 13193 2018-08-30
## 4410 4410 Lala 7.3 Bogor Lavesh 11098 2018-11-23
## 4460 4460 Lala 7.3 Bogor Primadona 11539 2020-02-05
## 4510 4510 Lala 7.3 Depok Alamanda 10358 2018-11-09
## 4560 4560 Lala 7.3 Bekasi Alamanda 11207 2018-09-02
## 4610 4610 Lala 7.3 Depok Victoria 9908 2020-02-11
## 4660 4660 Lala 7.3 Tengerang Alindra 7012 2018-11-04
## 4710 4710 Lala 7.3 Tengerang Tiara 10111 2018-03-05
## 4760 4760 Lala 7.3 Tengerang Arana 11319 2020-04-23
## 4810 4810 Lala 7.3 Bekasi Asera 7734 2019-01-08
## 4860 4860 Lala 7.3 Depok Asera 9012 2019-05-05
## 4910 4910 Lala 7.3 Depok Alamanda 10380 2020-08-08
## 4960 4960 Lala 7.3 Bogor Sweethome 13188 2018-08-21
## 5010 5010 Lala 7.3 Depok Sweethome 11991 2019-06-30
## 5060 5060 Lala 7.3 Bogor Peronia 12388 2019-09-27
## 5110 5110 Lala 7.3 Tengerang Mutiara 13797 2020-05-24
## 5160 5160 Lala 7.3 Jakarta Winona 10966 2018-01-04
## 5210 5210 Lala 7.3 Bogor Permata 13714 2019-02-14
## 5260 5260 Lala 7.3 Jakarta Alindra 8595 2018-10-20
## 5310 5310 Lala 7.3 Depok Asera 13616 2019-09-08
## 5360 5360 Lala 7.3 Jakarta Lavesh 11925 2019-01-04
## 5410 5410 Lala 7.3 Depok Victoria 11680 2019-10-26
## 5460 5460 Lala 7.3 Tengerang Tiara 7328 2018-05-31
## 5510 5510 Lala 7.3 Bogor Peronia 11095 2018-01-19
## 5560 5560 Lala 7.3 Bekasi Sweethome 7862 2018-08-07
## 5610 5610 Lala 7.3 Jakarta Teradamai 9220 2020-06-24
## 5660 5660 Lala 7.3 Tengerang Adara 9785 2020-03-01
## 5710 5710 Lala 7.3 Jakarta Palmyra 13043 2019-03-09
## 5760 5760 Lala 7.3 Bogor Lavesh 14707 2018-10-14
## 5810 5810 Lala 7.3 Bogor Winona 8411 2019-10-25
## 5860 5860 Lala 7.3 Tengerang Asoka 13536 2018-08-24
## 5910 5910 Lala 7.3 Tengerang Arana 14204 2018-06-02
## 5960 5960 Lala 7.3 Depok Mutiara 8834 2020-09-10
## 6010 6010 Lala 7.3 Depok Alamanda 13216 2018-12-21
## 6060 6060 Lala 7.3 Bogor Lavesh 7481 2020-03-29
## 6110 6110 Lala 7.3 Bekasi Sweethome 9061 2019-01-27
## 6160 6160 Lala 7.3 Bogor Tiara 7328 2019-04-04
## 6210 6210 Lala 7.3 Bekasi Tiara 13265 2020-06-16
## 6260 6260 Lala 7.3 Bogor Tiara 8803 2019-02-25
## 6310 6310 Lala 7.3 Bekasi Sweethome 13868 2020-02-17
## 6360 6360 Lala 7.3 Jakarta Permata 9700 2019-08-27
## 6410 6410 Lala 7.3 Jakarta Alindra 10520 2018-09-18
## 6460 6460 Lala 7.3 Tengerang Neon 9323 2019-03-16
## 6510 6510 Lala 7.3 Tengerang Primadona 11493 2020-04-30
## 6560 6560 Lala 7.3 Bekasi Permata 8922 2019-01-12
## 6610 6610 Lala 7.3 Depok Narada 13462 2019-12-12
## 6660 6660 Lala 7.3 Tengerang Arana 7220 2018-01-17
## 6710 6710 Lala 7.3 Bogor Adara 9662 2018-06-22
## 6760 6760 Lala 7.3 Depok Victoria 8659 2019-01-20
## 6810 6810 Lala 7.3 Bogor Palmyra 14038 2018-08-23
## 6860 6860 Lala 7.3 Jakarta Sweethome 8218 2020-07-04
## 6910 6910 Lala 7.3 Bekasi Primadona 7827 2020-01-24
## 6960 6960 Lala 7.3 Depok Sweethome 14028 2018-11-12
## 7010 7010 Lala 7.3 Tengerang Narada 7788 2018-05-05
## 7060 7060 Lala 7.3 Jakarta Alamanda 11165 2019-05-18
## 7110 7110 Lala 7.3 Jakarta Asoka 13076 2019-10-29
## 7160 7160 Lala 7.3 Depok Tiara 10827 2018-06-21
## 7210 7210 Lala 7.3 Depok Sweethome 7305 2018-02-03
## 7260 7260 Lala 7.3 Bogor Asera 12257 2019-06-09
## 7310 7310 Lala 7.3 Tengerang Albasia 8464 2020-01-18
## 7360 7360 Lala 7.3 Bekasi Adara 8884 2019-02-16
## 7410 7410 Lala 7.3 Tengerang Tiara 13453 2019-02-16
## 7460 7460 Lala 7.3 Bekasi Asera 11998 2018-10-24
## 7510 7510 Lala 7.3 Bekasi Permata 8841 2020-07-20
## 7560 7560 Lala 7.3 Bekasi Lavesh 8837 2019-04-02
## 7610 7610 Lala 7.3 Jakarta Peronia 10761 2018-01-18
## 7660 7660 Lala 7.3 Bogor Lavesh 9200 2019-09-23
## 7710 7710 Lala 7.3 Bekasi Primadona 14370 2020-03-07
## 7760 7760 Lala 7.3 Depok Adara 8096 2019-09-30
## 7810 7810 Lala 7.3 Tengerang Alamanda 10240 2020-05-27
## 7860 7860 Lala 7.3 Jakarta Palmyra 10927 2019-12-02
## 7910 7910 Lala 7.3 Bogor Alamanda 13425 2020-06-27
## 7960 7960 Lala 7.3 Depok Victoria 11453 2019-06-24
## 8010 8010 Lala 7.3 Jakarta Adara 10770 2020-08-21
## 8060 8060 Lala 7.3 Bekasi Asera 9426 2019-02-19
## 8110 8110 Lala 7.3 Bekasi Mutiara 9225 2018-01-18
## 8160 8160 Lala 7.3 Depok Winona 12299 2020-05-22
## 8210 8210 Lala 7.3 Tengerang Narada 7257 2020-07-29
## 8260 8260 Lala 7.3 Bekasi Teradamai 14547 2020-01-18
## 8310 8310 Lala 7.3 Depok Albasia 7140 2019-05-07
## 8360 8360 Lala 7.3 Tengerang Adara 12996 2019-02-24
## 8410 8410 Lala 7.3 Tengerang Permata 10080 2018-07-26
## 8460 8460 Lala 7.3 Depok Sweethome 13323 2019-01-31
## 8510 8510 Lala 7.3 Tengerang Palmyra 10274 2019-11-04
## 8560 8560 Lala 7.3 Bekasi Adara 8922 2020-04-22
## 8610 8610 Lala 7.3 Bogor Asoka 13694 2018-07-02
## 8660 8660 Lala 7.3 Jakarta Permata 10516 2018-12-21
## 8710 8710 Lala 7.3 Depok Lavesh 9971 2019-09-15
## 8760 8760 Lala 7.3 Bogor Lavesh 9094 2019-05-08
## 8810 8810 Lala 7.3 Jakarta Albasia 13355 2020-08-27
## 8860 8860 Lala 7.3 Bekasi Primadona 7286 2020-05-20
## 8910 8910 Lala 7.3 Jakarta Albasia 14916 2019-02-21
## 8960 8960 Lala 7.3 Bekasi Adara 11412 2019-11-29
## 9010 9010 Lala 7.3 Bekasi Palmyra 7064 2018-05-29
## 9060 9060 Lala 7.3 Jakarta Sweethome 12844 2019-07-09
## 9110 9110 Lala 7.3 Tengerang Alamanda 11182 2019-12-01
## 9160 9160 Lala 7.3 Bekasi Permata 7177 2020-08-08
## 9210 9210 Lala 7.3 Bekasi Primadona 14423 2020-08-30
## 9260 9260 Lala 7.3 Jakarta Permata 12833 2020-01-23
## 9310 9310 Lala 7.3 Bogor Tiara 7395 2019-05-06
## 9360 9360 Lala 7.3 Bogor Asoka 7826 2018-10-20
## 9410 9410 Lala 7.3 Jakarta Sweethome 14963 2018-11-18
## 9460 9460 Lala 7.3 Tengerang Palmyra 14332 2019-11-10
## 9510 9510 Lala 7.3 Tengerang Sweethome 7429 2020-04-24
## 9560 9560 Lala 7.3 Depok Albasia 7820 2019-01-19
## 9610 9610 Lala 7.3 Tengerang Narada 12737 2020-04-25
## 9660 9660 Lala 7.3 Depok Permata 12636 2018-07-19
## 9710 9710 Lala 7.3 Tengerang Sweethome 14142 2018-10-02
## 9760 9760 Lala 7.3 Bekasi Mutiara 9508 2019-12-07
## 9810 9810 Lala 7.3 Bekasi Peronia 10655 2019-12-16
## 9860 9860 Lala 7.3 Bekasi Arana 14834 2019-08-02
## 9910 9910 Lala 7.3 Bekasi Palmyra 9015 2020-09-24
## 9960 9960 Lala 7.3 Tengerang Neon 13594 2020-03-01
## Advertisement Kelas Booking_Fee
## 10 7 Low 691.95
## 60 13 Medium 839.28
## 110 8 High 1348.50
## 160 15 Medium 1046.07
## 210 10 Low 630.70
## 260 14 Low 395.60
## 310 7 Low 631.05
## 360 17 High 1486.90
## 410 5 Low 370.10
## 460 13 High 1446.60
## 510 9 High 1155.78
## 560 1 Low 360.95
## 610 7 High 1341.30
## 660 18 Medium 873.68
## 710 7 Low 510.66
## 760 17 Medium 1026.81
## 810 19 Low 661.85
## 860 2 High 1334.40
## 910 6 Low 637.77
## 960 16 Medium 1010.52
## 1010 19 Low 488.34
## 1060 16 Medium 874.24
## 1110 19 Low 671.58
## 1160 12 Low 537.42
## 1210 1 High 1082.88
## 1260 17 High 1362.00
## 1310 15 Low 658.63
## 1360 4 High 1142.28
## 1410 14 Medium 1076.49
## 1460 15 Medium 990.63
## 1510 12 Medium 862.48
## 1560 9 High 1083.24
## 1610 11 Medium 869.44
## 1660 5 Low 351.75
## 1710 1 Medium 1049.94
## 1760 7 Medium 1043.01
## 1810 10 Low 354.00
## 1860 12 Low 393.20
## 1910 19 High 1327.30
## 1960 5 Low 499.98
## 2010 5 High 1126.53
## 2060 11 Low 641.97
## 2110 13 High 1125.54
## 2160 15 Low 367.45
## 2210 14 High 1482.80
## 2260 4 Low 360.70
## 2310 14 High 1340.60
## 2360 3 Low 659.19
## 2410 14 High 1084.14
## 2460 3 High 1126.17
## 2510 5 Low 363.20
## 2560 11 Low 636.16
## 2610 17 Low 523.20
## 2660 6 Medium 814.96
## 2710 5 Medium 859.44
## 2760 1 Medium 992.07
## 2810 17 Medium 818.08
## 2860 16 Low 529.86
## 2910 5 High 1304.70
## 2960 9 Low 363.55
## 3010 10 Medium 1066.23
## 3060 20 Low 390.30
## 3110 14 High 1117.08
## 3160 2 Low 392.35
## 3210 2 High 1434.20
## 3260 12 Low 538.62
## 3310 16 Low 355.05
## 3360 13 High 1110.87
## 3410 14 High 1471.50
## 3460 7 Low 693.84
## 3510 2 Medium 1048.77
## 3560 16 Low 656.25
## 3610 4 High 1107.81
## 3660 11 Low 651.42
## 3710 17 Medium 1022.22
## 3760 20 Medium 1023.30
## 3810 12 Medium 805.60
## 3860 19 Low 636.16
## 3910 2 Low 520.80
## 3960 11 High 1380.60
## 4010 7 Low 379.75
## 4060 2 Medium 1077.39
## 4110 13 Low 375.00
## 4160 10 High 1343.20
## 4210 8 Medium 802.96
## 4260 11 Medium 817.76
## 4310 1 Medium 1036.71
## 4360 19 High 1319.30
## 4410 14 Medium 998.82
## 4460 19 Medium 1038.51
## 4510 9 Medium 828.64
## 4560 19 Medium 1008.63
## 4610 11 Low 693.56
## 4660 2 Low 350.60
## 4710 8 Medium 808.88
## 4760 13 Medium 1018.71
## 4810 15 Low 386.70
## 4860 15 Low 630.84
## 4910 7 Medium 830.40
## 4960 8 High 1318.80
## 5010 4 Medium 1079.19
## 5060 5 High 1114.92
## 5110 1 High 1379.70
## 5160 8 Medium 877.28
## 5210 19 High 1371.40
## 5260 4 Low 515.70
## 5310 2 High 1361.60
## 5360 20 Medium 1073.25
## 5410 5 Medium 1051.20
## 5460 6 Low 366.40
## 5510 6 Medium 998.55
## 5560 18 Low 393.10
## 5610 3 Low 645.40
## 5660 14 Low 684.95
## 5710 6 High 1304.30
## 5760 6 High 1470.70
## 5810 11 Low 504.66
## 5860 15 High 1353.60
## 5910 16 High 1420.40
## 5960 8 Low 530.04
## 6010 14 High 1321.60
## 6060 16 Low 374.05
## 6110 19 Low 634.27
## 6160 14 Low 366.40
## 6210 18 High 1326.50
## 6260 14 Low 528.18
## 6310 20 High 1386.80
## 6360 8 Low 679.00
## 6410 13 Medium 841.60
## 6460 19 Low 652.61
## 6510 7 Medium 1034.37
## 6560 2 Low 535.32
## 6610 3 High 1346.20
## 6660 4 Low 361.00
## 6710 7 Low 676.34
## 6760 14 Low 519.54
## 6810 17 High 1403.80
## 6860 2 Low 493.08
## 6910 1 Low 391.35
## 6960 8 High 1402.80
## 7010 8 Low 389.40
## 7060 10 Medium 1004.85
## 7110 4 High 1307.60
## 7160 17 Medium 866.16
## 7210 5 Low 365.25
## 7260 3 High 1103.13
## 7310 9 Low 507.84
## 7360 3 Low 533.04
## 7410 18 High 1345.30
## 7460 9 Medium 1079.82
## 7510 15 Low 530.46
## 7560 7 Low 530.22
## 7610 17 Medium 860.88
## 7660 11 Low 644.00
## 7710 15 High 1437.00
## 7760 15 Low 485.76
## 7810 5 Medium 819.20
## 7860 16 Medium 874.16
## 7910 19 High 1342.50
## 7960 3 Medium 1030.77
## 8010 19 Medium 861.60
## 8060 3 Low 659.82
## 8110 11 Low 645.75
## 8160 19 High 1106.91
## 8210 16 Low 362.85
## 8260 8 High 1454.70
## 8310 10 Low 357.00
## 8360 12 High 1169.64
## 8410 18 Medium 806.40
## 8460 5 High 1332.30
## 8510 14 Medium 821.92
## 8560 15 Low 535.32
## 8610 14 High 1369.40
## 8660 2 Medium 841.28
## 8710 8 Low 697.97
## 8760 11 Low 636.58
## 8810 5 High 1335.50
## 8860 8 Low 364.30
## 8910 7 High 1491.60
## 8960 12 Medium 1027.08
## 9010 17 Low 353.20
## 9060 1 High 1155.96
## 9110 5 Medium 1006.38
## 9160 5 Low 358.85
## 9210 9 High 1442.30
## 9260 14 High 1154.97
## 9310 2 Low 369.75
## 9360 3 Low 391.30
## 9410 3 High 1496.30
## 9460 11 High 1433.20
## 9510 6 Low 371.45
## 9560 2 Low 391.00
## 9610 20 High 1146.33
## 9660 16 High 1137.24
## 9710 1 High 1414.20
## 9760 10 Low 665.56
## 9810 10 Medium 852.40
## 9860 14 High 1483.40
## 9910 11 Low 631.05
## 9960 12 High 1359.40Soal 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
sales="Julian"
a<-subset(Data, subset=(Marketing_Name == sales))
b = ifelse((a$Work_Exp < 3),
(a$Booking_Fee)*(2/100),
(a$Booking_Fee)*(3/100))
b=a$Bonus
aBonus=sum(a$Bonus)
Bonus## [1] 0Soal 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)
R
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")
totalsales = 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$Price))
marketingdata=data.frame(Nama_Sales, totalsales)
marketingdata#Nama Marketing Pemasaran yang Baik
marketingdata[which.max(marketingdata$totalsales),]#Kota dan Cluster paling menguntungkan
profitable=Data[c("City","Cluster","Price")]
profitable[which.max(profitable$Price),]#Total Biaya iklan Lala
Advertise=subset(Data,subset=(Marketing_Name=="Lala"))
biaya_Advertise=(Advertise$Advertisement*4)
Totalbiaya=print(sum(biaya_Advertise))## [1] 8208ratarata_biaya= for (i in Nama_Sales){
r=subset(Data, subset=(Marketing_Name==i))
b=sum(r$Advertisement*4)
print(cat(sum(b)/length(r$Id), (cat(x, "Rata rata biaya iklan"))))}## Lala Rata rata biaya iklan39.48NULL
## Lala Rata rata biaya iklan41.06NULL
## Lala Rata rata biaya iklan39.28NULL
## Lala Rata rata biaya iklan42.18NULL
## Lala Rata rata biaya iklan45.14NULL
## Lala Rata rata biaya iklan40.22NULL
## Lala Rata rata biaya iklan43.34NULL
## Lala Rata rata biaya iklan42.32NULL
## Lala Rata rata biaya iklan41.76NULL
## Lala Rata rata biaya iklan41.04NULL
## Lala Rata rata biaya iklan42.88NULL
## Lala Rata rata biaya iklan43.76NULL
## Lala Rata rata biaya iklan43.4NULL
## Lala Rata rata biaya iklan42.44NULL
## Lala Rata rata biaya iklan42.86NULL
## Lala Rata rata biaya iklan42.8NULL
## Lala Rata rata biaya iklan41.92NULL
## Lala Rata rata biaya iklan41.86NULL
## Lala Rata rata biaya iklan41.44NULL
## Lala Rata rata biaya iklan40.12NULL
## Lala Rata rata biaya iklan45.06NULL
## Lala Rata rata biaya iklan43.44NULL
## Lala Rata rata biaya iklan40.5NULL
## Lala Rata rata biaya iklan39.68NULL
## Lala Rata rata biaya iklan43.24NULL
## Lala Rata rata biaya iklan41.2NULL
## Lala Rata rata biaya iklan40.86NULL
## Lala Rata rata biaya iklan44.14NULL
## Lala Rata rata biaya iklan41.76NULL
## Lala Rata rata biaya iklan42.34NULL
## Lala Rata rata biaya iklan41.78NULL
## Lala Rata rata biaya iklan40.42NULL
## Lala Rata rata biaya iklan41.3NULL
## Lala Rata rata biaya iklan43.06NULL
## Lala Rata rata biaya iklan43.96NULL
## Lala Rata rata biaya iklan42.32NULL
## Lala Rata rata biaya iklan43.06NULL
## Lala Rata rata biaya iklan39.98NULL
## Lala Rata rata biaya iklan44.6NULL
## Lala Rata rata biaya iklan42.54NULL
## Lala Rata rata biaya iklan41.3NULL
## Lala Rata rata biaya iklan40.24NULL
## Lala Rata rata biaya iklan40.1NULL
## Lala Rata rata biaya iklan43.88NULL
## Lala Rata rata biaya iklan40.96NULL
## Lala Rata rata biaya iklan41.9NULL
## Lala Rata rata biaya iklan42.38NULL
## Lala Rata rata biaya iklan41.76NULL
## Lala Rata rata biaya iklan44.58NULL
## Lala Rata rata biaya iklan44.4NULLKasus 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
Id=(1:50000)
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("Satpam", "Penjaga Toko","Baby Sitter",
"Ibu Rumah Tangga","Office Boy","Office Girl",
"Buruh", "Supir",
"Pedagang Keliling",
"tukang cuci"),length(Academic=="J.School"),replace=T),
ifelse(Academic == "H.School",sample(c("Penulis","Karyawan Pabrik","Sukarelawan",
"Trader","Kasir","Pedagang","Sales","Security",
"Pelayan Resto","Guru Les"),
length(Academic=="H.School"),replace=T),
ifelse(Academic == "Sarjana",sample(c("Karyawan Swasta","Polisi",
"Anggota Partai","Pejabat","Wirausaha",
"Pengamat Hukum","Praktisi","Guru Bahasa","Politisi",
"Anggota Partai"),length(Academic == "Sarjana"),replace=T),
ifelse(Academic == "Magister",sample(c("CEO","Guru Besar","Youtuber",
"Ketua DPR","Wakil Ketua DPR",
"Dosen tetap",
"Karyawan Tetap","Motivator",
"Pengusaha","Direktur Utama"),
length(Academic=="Magister"),replace=T),
sample(c("kepala sekolah","ketua pbb",
"menteri","ketua partai","dokter ahli",
"perawat tetap","designer",
"editor","rektor",
"asisten presiden"),
length(Academic=="Phd"),replace=T)))))
Income=sample(c(5000:15000),50000,replace=T)
Spending=sample(c(5000:12000),50000,replace=T)
Grade=sample(c("A+","A-","B+","B-","C"),50000, replace=T)
Number_of_Children=sample(c(0:10),50000,replace=T)
Private_Vehicle=sample(c("Mobil","Sepeda Motor","Kendaraan Umum"), 50000, replace =T)
Home=sample(c("Sewa","Milik Sendiri","Kredit"),50000, replace=T)
Number_of_Children <- ifelse(Marital_Status == "Yes", (sample(c(0:10))),0)
Customer=data.frame(Id,Marital_Status,Address,
Work_Location,Age,Academic,
Job,Grade,Income,Spending, Number_of_Children,
Private_Vehicle,Home)
library(DT)
datatable(Customer)Soal 2
Ringkasan Statistik penting seperti apa yang bisa Anda dapatkan dari kumpulan data Anda?
R
ringkasan=summary(Customer)
datatable(ringkasan)Soal 3
Menurut perhitungan dan analisis Anda, pelanggan mana yang potensial untuk Anda pertahankan?
R
Customer$NetWorth=Customer$Income=Customer$Spending
subset(Customer, NetWorth>=7000)Referensi
- ref 1
- ref 2
- ref 3