Berikut merupakan pengolahan billing penggunaan akses data customer DCP
Raw data diambil dari portal DCP yang bisa di-download oleh customer maupun tim Indosat m2m, raw data merupakan data detail billing customer per bulan.
'data.frame': 239951 obs. of 23 variables:
$ CALLTYPE : Factor w/ 1 level "gprs": 1 1 1 1 1 1 1 1 1 1 ...
$ CONTRACT.ID : Factor w/ 1 level "15000023_SC_01": 1 1 1 1 1 1 1 1 1 1 ...
$ SUBSCRIPTION.TYPE : Factor w/ 1 level "PP100015638": 1 1 1 1 1 1 1 1 1 1 ...
$ CALL.TIME : Factor w/ 222859 levels "2019-06-26 00:58:43",..: 6711 6713 6714 6637 6703 6716 6718 6720 6717 6721 ...
$ UTC.OFFSET : int 0 0 0 0 0 0 0 0 0 0 ...
$ A.NUMBER : num 6.28e+12 6.28e+12 6.28e+12 6.28e+12 6.28e+12 ...
$ B.NUMBER : logi NA NA NA NA NA NA ...
$ DIRECTION : Factor w/ 1 level "MOC": 1 1 1 1 1 1 1 1 1 1 ...
$ ZONE : Factor w/ 1 level "In Group A1": 1 1 1 1 1 1 1 1 1 1 ...
$ APN : Factor w/ 1 level "plnbanten.isat": 1 1 1 1 1 1 1 1 1 1 ...
$ ORIGINATING.COUNTRY: Factor w/ 1 level "Indonesia": 1 1 1 1 1 1 1 1 1 1 ...
$ ORIGINATING.NETWORK: Factor w/ 1 level "Indosat": 1 1 1 1 1 1 1 1 1 1 ...
$ DESTINATION.COUNTRY: logi NA NA NA NA NA NA ...
$ DESTINATION.NETWORK: logi NA NA NA NA NA NA ...
$ IMSI : num 5.1e+14 5.1e+14 5.1e+14 5.1e+14 5.1e+14 ...
$ CALL.SET.UP.FEE : int 0 0 0 0 0 0 0 0 0 0 ...
$ UNIT : int 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ...
$ UNIT.PRICE : num 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 ...
$ BILLABLE.QUANTITY : int 2000 2000 1000 97000 12000 2000 2000 2000 9000 4000 ...
$ ACTUAL.QUANTITY : int 1032 1574 908 96935 11257 1442 1132 1598 8502 3151 ...
$ AMOUNT : int 0 0 0 0 0 0 0 0 0 0 ...
$ VOLUME.SENT : int 366 676 360 60335 6331 596 452 636 5100 1524 ...
$ VOLUME.RECEIVED : int 666 898 548 36600 4926 846 680 962 3402 1627 ... CALLTYPE CONTRACT.ID SUBSCRIPTION.TYPE CALL.TIME UTC.OFFSET
1 gprs 15000023_SC_01 PP100015638 2019-07-01 16:20:15 0
2 gprs 15000023_SC_01 PP100015638 2019-07-01 16:20:51 0
3 gprs 15000023_SC_01 PP100015638 2019-07-01 16:21:08 0
4 gprs 15000023_SC_01 PP100015638 2019-07-01 16:03:07 0
5 gprs 15000023_SC_01 PP100015638 2019-07-01 16:17:43 0
6 gprs 15000023_SC_01 PP100015638 2019-07-01 16:21:52 0
A.NUMBER B.NUMBER DIRECTION ZONE APN
1 6.281419e+12 NA MOC In Group A1 plnbanten.isat
2 6.281419e+12 NA MOC In Group A1 plnbanten.isat
3 6.281419e+12 NA MOC In Group A1 plnbanten.isat
4 6.281419e+12 NA MOC In Group A1 plnbanten.isat
5 6.281419e+12 NA MOC In Group A1 plnbanten.isat
6 6.281419e+12 NA MOC In Group A1 plnbanten.isat
ORIGINATING.COUNTRY ORIGINATING.NETWORK DESTINATION.COUNTRY
1 Indonesia Indosat NA
2 Indonesia Indosat NA
3 Indonesia Indosat NA
4 Indonesia Indosat NA
5 Indonesia Indosat NA
6 Indonesia Indosat NA
DESTINATION.NETWORK IMSI CALL.SET.UP.FEE UNIT UNIT.PRICE
1 NA 5.10012e+14 0 1000 1.5
2 NA 5.10012e+14 0 1000 1.5
3 NA 5.10012e+14 0 1000 1.5
4 NA 5.10012e+14 0 1000 1.5
5 NA 5.10012e+14 0 1000 1.5
6 NA 5.10012e+14 0 1000 1.5
BILLABLE.QUANTITY ACTUAL.QUANTITY AMOUNT VOLUME.SENT VOLUME.RECEIVED
1 2000 1032 0 366 666
2 2000 1574 0 676 898
3 1000 908 0 360 548
4 97000 96935 0 60335 36600
5 12000 11257 0 6331 4926
6 2000 1442 0 596 846Dari data tersebut, akan di-grouping berdasarkan total penggunaan data per hari, untuk exract informasi Tanggal dari kolom CALL.TIME digunakan date conversion dari base library dan mutate dari dplyr library, sehingga dapat dilakukan grouping dan pivot untuk mendapatkan data yang diinginkan.
Data ke-2 merupakan informasi top 10 MSISDN dengan penggunaan data terbanyak. Proses grouping dan pivot tidak jauh berbeda dengan data sebelumnya, kemudian dilakukan sorting untuk mendapatkan info top 10 data.
plnbanten.isat| DATE | TOTAL.USAGE |
|---|---|
| 2019-06-26 | 9951000 |
| 2019-06-30 | 163181000 |
| 2019-07-01 | 363981000 |
| 2019-07-02 | 452135000 |
| 2019-07-03 | 370521000 |
| 2019-07-04 | 370777000 |
| 2019-07-05 | 384351000 |
| 2019-07-06 | 389547000 |
| 2019-07-07 | 399441000 |
| 2019-07-08 | 399769000 |
| 2019-07-09 | 399128000 |
| 2019-07-10 | 368762000 |
| 2019-07-11 | 407962000 |
| 2019-07-12 | 349690000 |
| 2019-07-13 | 381809000 |
| 2019-07-14 | 389530000 |
| 2019-07-15 | 387520000 |
| 2019-07-16 | 387236000 |
| 2019-07-17 | 363593000 |
| 2019-07-18 | 371982000 |
| 2019-07-19 | 370482000 |
| 2019-07-20 | 380244000 |
| 2019-07-21 | 366055000 |
| 2019-07-22 | 388563000 |
| 2019-07-23 | 358467000 |
| 2019-07-24 | 359118000 |
| 2019-07-25 | 358010000 |
| 2019-07-26 | 364507000 |
| 2019-07-27 | 368113000 |
| 2019-07-28 | 366131000 |
| 2019-07-29 | 381937000 |
| 2019-07-30 | 370776000 |
| 2019-07-31 | 277293000 |