TELK
OMNIKA
T
elecommunication,
Computing,
Electr
onics
and
Contr
ol
V
ol.
18,
No.
2,
April
2020,
pp.
907
918
ISSN:
1693-6930,
accredited
First
Grade
by
K
emenristekdikti,
No:
21/E/KPT/2018
DOI:
10.12928/TELK
OMNIKA.v18i2.12989
r
907
The
pr
ediction
of
mobile
data
traffic
based
on
the
ARIMA
model
and
disrupti
v
e
f
ormula
in
industry
4.0:
A
case
study
in
J
akarta,
Indonesia
Ajib
Sety
o
Arifin,
Muhammad
Idham
Habibie
Department
of
Electrical
Engineering,
F
aculty
of
Engineering,
Uni
v
ersitas
Indonesia,
Indonesia
Article
Inf
o
Article
history:
Recei
v
ed
Apr
23,
2019
Re
vised
Jan
5,
2020
Accepted
Feb
21,
2020
K
eyw
ords:
Capacity
planning
Disrupti
v
e
formula
Industry
4.0
IoT
Prediction
methods
ABSTRA
CT
Disrupti
v
e
technologi
es,
which
are
caused
by
the
cellular
e
v
olution
including
the
Inte
rnet
of
Things
(IoT),
ha
v
e
significantly
contrib
uted
data
traf
fic
to
the
mobile
telecommunication
netw
ork
in
the
era
of
Industry
4.0.
These
technologies
cause
erroneous
predictions
prompting
mobile
operators
to
upgrade
their
netw
ork,
which
leads
to
re
v
enue
loss.
Besides,
the
inaccurac
y
of
netw
ork
prediction
also
creates
a
bottlene
ck
problem
that
af
fects
the
performance
of
the
telecommunication
netw
ork,
especially
on
the
mobile
backhaul.
W
e
propose
a
ne
w
technique
to
predict
more
accurate
data
traf
fic.
This
research
used
a
uni
v
ariate
Autore
gressi
v
e
Inte
grated
Mo
ving
A
v
erage
(ARIMA)
model
combined
with
a
ne
w
disrupti
v
e
formula.
Another
model,
called
a
disrupti
v
e
formula,
uses
a
judgmental
approach
based
on
four
v
ariables:
Political,
Economic,
Social,
T
echnological
(PEST),
cost,
time
to
mark
et,
and
mark
et
share.
The
disrupti
v
e
formula
amplifies
the
ARIMA
calculation
as
a
ne
w
combination
formula
from
the
judgmental
and
statistical
approach.
The
results
sho
w
that
the
disrupti
v
e
formula
combined
with
the
ARIMA
model
has
a
lo
w
error
in
mobile
data
forecasting
compared
to
the
con
v
entional
ARIMA.
The
con
v
entional
ARIMA
sho
ws
the
a
v
erage
mobile
data
traf
fic
to
be
49.19
Mb/s
and
156.93
Mb/s
for
the
3G
and
4G,
respecti
v
ely;
whereas
the
ARIMA
with
disrupti
v
e
formula
sho
ws
more
optimized
traf
fic,
reaching
56.72
Mb/s
and
199.73
Mb/s.
The
higher
v
alues
in
the
ARIMA
with
disrupti
v
e
formula
are
closest
to
the
prediction
of
the
mobile
data
forecast.
This
result
suggests
that
the
combination
of
statistical
and
computational
approach
pro
vide
more
accurate
prediction
method
for
the
mobile
backhaul
netw
orks.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Ajib
Setyo
Arifin,
Department
of
Electrical
Engineering,
F
aculty
of
Engineering,
Uni
v
ersitas
Indonesia,
Depok
16424,
Indonesia.
Email:
ajib@eng.ui.ac.id
1.
INTR
ODUCTION
The
total
mobile
data
traf
fic
generated
by
telecommunication
technologies
has
been
significantly
con-
trib
uting
to
the
core
netw
ork
in
recent
years.
This
has
led
to
a
congestion
problem,
especially
in
mobile
backhaul
technologies,
which
play
a
significant
role
in
bringing
traf
fic
to
the
core
netw
ork.
If
the
mobile
back-
haul
is
congested,
the
operator
performance
may
return
a
pack
et
drop
or
higher
latenc
y
,
where
af
fecting
the
end
user
indirectly
.
Besides,
as
the
upcoming
Industry
4.0
has
already
been
introduced
in
se
v
eral
countries,
mobile
J
ournal
homepage:
http://journal.uad.ac.id/inde
x.php/TELK
OMNIKA
Evaluation Warning : The document was created with Spire.PDF for Python.
908
r
ISSN:
1693-6930
data
traf
fic
no
w
might
be
dif
ficult
to
predict.
This
issue
can
lead
to
erroneous
predictions
that
might
e
x
ert
a
ne
g
ati
v
e
impact,
such
as
high
Capital
Expenditure
(CAPEX)
in
v
estment
caused
by
incorr
ect
capacity
planning.
Disrupti
v
e
technologies
created
by
inno
v
ati
v
e
b
usiness
models
ha
v
e
changed
e
xisting
b
usiness
to
e
v
al-
uate
their
b
usiness
model
by
adapt
ing
ne
w
technologies
to
monetize
ne
w
re
v
enue
[1][2].
According
to
study
,
the
probability
of
creating
a
successful
b
usiness
with
an
inno
v
ate
approach
is
10
times
higher
than
using
a
non-
inno
v
ati
v
e
approach
[3].
This
kno
wledge
has
moti
v
ated
incumbent
b
usinesses
to
change
their
b
usiness
models
according
to
Industry
4.0,
which
introduces
more
automation
and
data-e
xchange
systems.
Therefore,
in
recent
years,
ne
w
or
e
xisting
industries
of
fer
ne
w
small
b
usiness
model,
simplified
products
and
services,
and
ef
ficient
solution.
This
has
contrib
uted
significantly
to
mobile
data
traf
fic
as
well.
The
lar
ge
capacity
pioneered
by
The
Third
Generation
(3G)
and
F
ourth
Generation
(4G)
ha
v
e
con-
trib
uted
significantly
to
mobile
backhaul
capacity
recently
.
Another
upcoming
technology
is
The
Fifth
Gen-
eration
(5G),
which
is
the
latest
generation
of
telecommunication
netw
orking
and
may
of
ficially
be
launched
in
2020.
The
5G,
which
is
named
as
Ne
w
Radio
(NR)
in
radio
access,
has
a
do
wnlink
and
uplink
rate
of
20
Gb/s
and
10
Gb/s
respecti
v
ely
[4],
according
to
the
International
T
elecommunication
Union
(ITU)
[5].
The
5G
systems
also
ha
v
e
w
orking
frequencies
in
the
millimetre
w
a
v
e
range;
such
minimal
distances
are
causing
infrastructure
to
become
denser
.
The
last
and
most
critical
f
actors
that
might
contrib
ute
to
the
netw
ork
in
v
olv
e
population
statistics.
The
Indonesian
population
has
increased
by
5.6
%
o
v
er
the
last
fi
v
e
years
and
is
e
xpected
to
continue
to
rise
[6].
Accordingly
,
the
number
of
people
assumed
to
be
the
acti
v
e
users
of
application/web
services
will
also
rise,
in
turn
increasing
traf
fic
in
the
netw
ork.
This
can
be
correlated
with
the
number
of
smart
phones
and
International
Mobile
Subscriber
Identities
(IMSIs)
which
ha
v
e
already
pro
vides
much
traf
fic
to
the
traf
fic
congestion
[6].
Based
on
these
f
actors
,
a
huge
capacity
is
ur
gently
needed
on
the
backhaul
to
support
this
kind
of
technology
.
By
summing
the
t
raf
fic
peak
rate
of
these
inte
grated
technologies,
we
e
xpected
a
2
1
Gb/s
pipeline
to
be
supported
in
the
backhaul.
Current
mobile
backhaul
technologies,
which
use
an
outdated
micro
w
a
v
e
system,
support
around
150-300
Mb/s
in
each
link;
this
should
be
changed
to
support
a
higher
capacity
by
,
for
instance,
using
fiber
optics
or
millimeter
-w
a
v
e
links
of
up
to
1
Gb/s.
This
paper
aims
to
predict
the
forecast
traf
fic
based
on
current
technologies
using
second
generation
(2G),
third
generation
(3G),
fourth
generation
(4G),
and
to
implement
some
IoT
de
vices
to
predict
the
short
forecast
for
the
ne
xt
fe
w
years
of
mix
ed
traf
fic
(2G,
3G,
4G,
L
TE,
IoT
,
and
5G).
Some
researchers
ha
v
e
sho
wn
that
ARIMA
model
has
been
widely
used
in
man
y
t
raf
fic
forecasting
cases,
for
instance,
determining
T
raf
fic
Channels
(TCH)
in
GSM
obtained
in
NMS
in
China
[7]
and
determine
T
ime
Series
v
alue
for
W
i-Fi
data
[8].
Other
references
sho
w
that
ARIMA
able
to
be
combined
with
dif
ferent
techniques
to
impro
v
e
the
accurac
y
of
the
predicted
traf
fic
in
the
session:
F
or
e
xample,
the
prediction
traf
fic
of
the
802.11
W
ireless
Local
Access
Netw
ork
(WLAN)
using
Seasonal
Autore
gressi
v
e
Inte
grated
Mo
ving
A
v
erage
(SARIMA)
[9].
In
another
case,
the
ARIMA
model
w
as
combined
with
the
T
ra
v
el
Distance
Algorithm
(TDE)
to
predict
road
v
ehicle
traf
fic
to
obtain
more
accurate
real-time
traf
fic
data
[10].
Besides,
It
is
also
well-kno
wn
that
ARIMA
is
also
used
in
for
currenc
y
prediction
[11].
In
this
paper
,
we
attempt
to
predict
the
forecast
using
a
uni
v
ariate
ARIMA
model
base
d
on
autore-
gression,
mo
ving
a
v
erage,
and
dif
ferentiated
princ
iples
combined
with
disrupti
v
e
formulas
to
mak
e
the
forecast
more
accurate.
Combining
a
statistical
method
with
a
judgmental
technique
(using
disrupti
v
e
equations)
might
impro
v
e
the
accurac
y
of
the
predicted
traf
fic,
b
ut
sometimes
this
combination
might
leads
to
the
erroneous
trend
[12,
13].
Ho
we
v
er
,
research
has
sho
wn
that
using
a
con
v
entional
ARIMA
model
yields
a
a
lar
ge
error
rate
compared
with
the
ARIMA
combination
model
with
another
technique
[14,
15].
Besides,
the
ARIMA
model
is
cate
gorized
as
a
short
forecast,
where
it
predicts
a
fe
w
time-series
after
data
set.
The
disrupti
v
e
formula
analysis
w
as
based
on
the
time
to
mark
et,
v
alue
proposition,
cost,
and
PEST
analysis
[3].
The
main
moti
v
ation
of
this
research
w
as
to
analyze
and
predict
the
disrupti
v
eness
of
inte
grated
traf
fic
by
considering
a
lo
w
cost
and
secure
bandwidth
solution.
Ideally
,
preparing
an
une
xpected
traf
fic
in
the
radio
access
netw
ork
mobile
operators
should
i
n
v
est
a
lar
ger
pipeline.
But
these
operators
are
mostly
sa
ving
in
v
est-
ment
cost
in
the
radio
access
netw
ork.
Therefore,
this
research
attempted
to
predict
the
traf
fic
in
the
mobile
backhaul
precisely
with
the
cost
and
bandwidth
consideration.
This
paper
is
or
g
anized
as
follo
ws:
Section
2
gi
v
es
a
literature
re
vie
w
of
forecasting
methods
that
sup-
port
a
basic
understanding
of
this
research.
Section
3
e
xplains
the
methodological
points
of
traf
fic
forecasting
that
were
used
in
this
research.
Section
4
and
5
present
the
results
and
discussion
of
this
research,
respecti
v
ely
,
including
the
analysis
and
some
findings.
The
last
section
pro
vides
a
conclusion
of
this
w
ork.
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
18,
No.
2,
April
2020
:
907
–
918
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
r
909
2.
FORECASTING
METHODS
Se
v
eral
techniques
ha
v
e
been
proposed
to
predict
technological
disrupti
v
eness,
especially
in
I
nd
us
try
4.0.
The
first
technique
is
called
F
orw
ard-Citation
Node
P
air
Algorithm
(FCN
A),
which
w
as
introduced
by
Changw
oo
Choi
and
Y
ongtae
P
ark
[16],
and
uses
a
patent-citation
matrix
consisting
of
a
set
of
nodes
con-
nected
by
arcs.
This
technique
aims
to
identify
the
main
de
v
elopment
path
of
the
comple
x
patent-citation
by
understanding
both
present
and
past
technologies
[17],
where
the
leading
technology
possesses
the
main
patents
that
are
link
ed
to
the
selected
arcs.
The
other
technique,
to
impro
v
e
FCN
A,
is
K-core
analysis,
which
concentrates
on
the
sub-groups’
nodes
rather
than
on
the
main
patents
[17].
This
technique
aims
to
remo
v
e
the
central
patents
(which
are
a
ssumed
to
be
the
most
disrupti
v
e
technologies)
by
distrib
uting
them
into
dif
ferent
subgroups
that
can
help
identify
the
essential
data
[18].
The
last
technique
to
identify
disrupti
v
eness
in
Industry
4.0
is
called
topic
modelling
and
is
similar
to
a
search
engine.
T
opic
modelling
is
mostly
used
in
the
cluster
that
has
been
defined
in
the
K-core
analysis.
The
highest
number
of
repetiti
v
e
w
ords
in
each
cluster
defined
in
K-core
analysis
leads
to
the
most
important
aspects
of
the
te
chno
l
ogy
.
Afterw
ard,
to
v
alidate
the
results
raised
by
this
technique,
it
is
recommended
that
tw
o
e
xperts
re
vie
w
them
[17].
All
these
techniques
are
mostly
aimed
at
identifying
the
most
important
disrupti
v
e
technologies
in
the
mark
et
through
clustering.
Such
techniques
help
mark
et
leaders
identify
which
technologies
are
more
disrupti
v
e,
b
ut
the
y
do
not
determine
ho
w
much
traf-
fic
each
will
contrib
ute.
Based
on
the
analysis
of
these
techniques,
the
major
contrib
ution
technologies
are
IoT
,
artificial
intelligence,
financial
technology
(including
blockchain),
virtual
reality
,
and
autonomous
v
ehicles.
2.1.
T
ypes
of
f
or
ecasts
Judgmental
methods
are
based
on
intui
tion,
personal
i
nterest,
and
user
e
xperiences
[19].
One
e
xample
of
a
judgmental
method
is
the
Delphi
method,
which
emplo
ys
a
panel
of
e
xperts
to
analyze
research
results
to
ensure
v
alidity
.
A
judgmental
method
will
also
be
used
in
this
prediction
to
analyze
the
disrupti
v
e
formula
using
a
risk-f
actor
technique.
Uni
v
ariate
methods
depend
on
past
and
present
v
alues
that
ha
v
e
been
forecasted
in
a
single
series
[19].
Uni
v
ariate
met
h
ods
are
used
in
this
research
to
analyze
the
predicted
traf
fic
forecast.
The
ARIMA
model
consists
of
both
uni
v
ariate
and
multi
v
ariate
models.
Multi
v
ariate
models
use
more
than
one
independent
v
ariable
(time
series)
simultaneously
to
predict
the
forecast.
These
v
ariables
might
comprise
interrelationships
using
a
dif
ferent
time
v
ariable.
2.2.
The
ARIMA
model
ARIMA
stands
for
Autore
gressi
v
e
Inte
grated
Mo
ving
A
v
erage
[19].
The
ARIMA
model
is
based
on
the
Box
and
Jenkins
method
of
using
three
dif
ferent
concepts:
Autore
gression
(AR),
Mo
ving
A
v
erage
(MA),
and
inte
gration,
together
classified
as
an
ARIMA(
p;
d;
q
);
p
defines
the
AR;
d
defines
the
dif
ferential;
and
q
defines
the
MA.
AR
is
a
technique
for
analyzing
the
past
and
present
v
alues
of
a
data
set.
AR
is
denoted
as
p
,
where
it
sho
ws
the
weighted
linear
of
sum
p
v
alues
based
on
ARIMA
(
p;
d;
q
)
terminology
.
The
p
v
alue
indicates
the
number
of
order
.
The
formula
to
denote
this
AR
is
sho
wn
in
(1):
y
t
=
1
y
t
1
+
2
y
t
2
+
:
:
:
+
p
y
t
p
+
(1)
where
p
is
used
to
determine
the
number
of
orders
of
past
v
alues;
t
is
the
time
seri
es;
is
the
slope
coef
fic
ient
of
the
weighted
past
v
alues;
and
y
is
the
time-series
function
of
the
AR
IMA
model.
The
error
term
is
normally
distrib
uted
with
mean
zero
and
v
ariance
2
.
The
MA
process
is
denoted
by
order
q
in
the
ARIMA
(
p;
d;
q
)
classification,
which
sho
ws
an
error
v
alue
in
(1).
The
error
term
is
normally
distrib
uted
with
mean
zero
and
v
ariance
2
.
MA
also
uses
the
number
of
orders
in
the
past
v
alues,
as
denoted
in
(2):
y
t
=
t
+
1
t
1
+
:
:
:
+
q
t
q
(2)
where
t
is
the
time
series;
is
the
slope
coef
ficient
of
the
weighted
past
v
alue;
is
the
number
of
orders
needed
to
identify
the
past
v
alues;
and
y
is
the
time-series
function
of
the
ARIMA
model.
T
o
identify
ho
w
man
y
orders
are
in
the
calculation
of
AR,
the
parameter
of
q
is
used.
MA
has
been
used
for
stock
trading.
MA
aims
to
eliminate
the
noises
or
peaks
from
random
noise
fluctuations
in
the
graph,
where
it
leads
to
the
erroneous
prediction.
T
o
calculate
the
a
v
erage
v
alue
in
the
chart,
MA
tak
es
a
certain
period,
such
as
se
v
en
v
alues,
to
calculate
the
a
v
erage
as
sho
wn
in
Figure
1.
The
pr
ediction
of
mobile
data...
(Ajib
Setyo
Arifin)
Evaluation Warning : The document was created with Spire.PDF for Python.
910
r
ISSN:
1693-6930
Figure
1.
The
MA
concept
e
xample
Figure
1
describes
an
MA
concept
where
a
v
erage
A,
B,
and
C
are
calculated.
The
results
of
A,
B,
and
C
are
1
;
2
:
6
;
and
2
:
6
,
respecti
v
ely
.
The
a
v
erage
A
shares
the
same
a
v
erage
v
alue
wit
h
each
v
alue
in
the
sequence,
which
is
one.
Due
to
inconsistent
sequences
in
series
six,
the
a
v
erages
B
and
C
ha
v
e
di
f
ferent
v
al-
ues
compared
to
A.
Ho
we
v
er
,
instead
of
using
an
original
sequence
v
alue
that
has
a
significant
peak,
among
others,
the
MA
sho
ws
a
smooth
graph.
Otherwise,
Inte
grated
or
dif
ferentiated
v
ersions
are
denoted
as
d
in
ARIMA
(
p;
d;
q
),
which
is
defined
as
the
parameter
that
checks
whether
the
graph
is
stationary
[20].
As
a
best
practice
case,
the
time-series
graph
is
mostly
non-stationary
.
Therefore,
MA
and
AR
are
not
suf
ficient
to
deter
-
mine
the
prediction.
Being
non-stationary
might
cause
problems
that
can
lead
to
error
prediction.
Therefore,
a
dif
ferential
or
inte
grated
model
is
one
of
the
best
techniques
a
v
ailable
to
mak
e
graphs
stationary
[19].
3.
TRAFFIC
FORECASTING
FORMULA
TION
The
ARIMA
model
is
a
statistical
model
that
predicts
the
forecas
t
based
on
past
and
present
v
alues.
The
statistical
model
might
be
inaccurate
if
ne
w
technologies
or
trends
are
af
fecting
the
graph,
making
them
more
disrupti
v
e.
Since
a
judgmental
approach
can
predict
the
future
more
accurately
[13],
this
paper
proposes
a
ne
w
closest
prediction
approach
to
identify
traf
fic
usi
n
g
a
combination
of
judgmental
and
statistical
approaches.
T
o
use
both
approaches,
three
important
procedures
must
be
completed:
Analysis
of
the
data
set
for
3G
and
L
TE
traf
fic
using
the
ARIMA
model;
generation
of
a
disrupti
v
e
formula
based
on
en
vironmental
technologies
in
the
particular
country;
v
alidate
the
disrupti
v
e
formula
combined
with
the
ARIMA
model.
3.1.
Determining
the
data
set
The
data
set
w
as
obtained
from
one
of
the
b
usiest
traf
fic
site
in
Indonesia
where
2G,
3G,
and
L
TE
ha
v
e
been
installed.
W
e
took
an
e
xample
from
the
capital
c
ity
of
Indonesia,
Jakarta,
where
L
TE
w
as
installed
in
the
first
month
of
2017.
This
site
is
strate
gically
located
in
the
capital
city
,
where
internet
penetration
is
relati
v
ely
greater
than
other
locations.
Besides,
this
site
also
represents
the
possibility
t
o
implement
the
IoT
and
the
5G
netw
ork.
L
TE
and
3G
mobile
data
t
raf
fic
data
were
collected
for
one
year
.
Because
of
mobile
operators
ha
v
e
not
in
v
ested
in
2G
technology
an
ymore,
this
w
as
assumed
to
be
stagnant
and
e
xcluded
in
the
ARIMA
and
disrupti
v
e
calculations.
Based
on
the
Cisco
V
isual
Netw
orking
Inde
x,
the
penetration
of
2G
de
vices
has
been
decreasing
gradually
o
v
er
the
last
fi
v
e
years
[21].
Therefore,
we
assumed
that
2G
mobi
le
traf
fic
consumes
a
maximum
of
2
Mb/s
at
each
site
based
on
maximum
capacity
,
where
traf
fic
remains
stable.
IoT
sensors
were
also
e
xcluded
in
the
calculation
because
Indonesia
just
will
launch
the
Narro
w-band
IoT
License
in
Indonesia
in
the
latest
2018,
where
IoT
just
of
ficially
starting
in
the
ne
xt
year
.
.
Ho
we
v
er
,
it
is
predicted
that
400,000
IoT
sensors
might
be
installed
by
2022
[22].
This
trend
is
similar
to
that
for
5G
systems,
which
are
e
xpected
to
e
xpand
by
2020,
especially
in
Indonesia
[23].
Therefore,
this
research
made
se
v
eral
assumptions
about
these
technologies
(2G,
5G,
and
IoT),
whereas
3G
and
L
TE
were
more
clearly
demonstrated.
3.2.
A
new
disrupti
v
e
f
ormula
The
peak
data
rate
of
L
TE
and
3G,
which
are
300
Mb/s
do
wnlink
and
63
Mb/s
do
wnlink
(with
3rd
carriers)
respecti
v
ely
,
ha
v
e
not
sho
wn
a
real
f
act
that
full
throughput
is
used.
Ev
en
4G
and
3G
ha
v
e
300
Mb/s
and
63
Mb/s
in
the
do
wnlink,
only
approximately
20%
of
the
full
throughput
w
ould
be
used
[24].
As
a
result,
traf
fic
is
unpredictable
and
might
halt
one
day
,
depending
on
human
beha
viour
and
compan
y
profiles.
This
paper
proposes
a
ne
w
modification
to
the
ARIMA
formula.
A
ne
w
disrupti
v
e
formula
w
as
defined
as
a
judgemental
method
that
might
in
v
olv
e
e
xperts
to
identify
the
resul
ts.
The
combination
of
the
judgemental
and
analytical
methods
can
impro
v
e
the
accurac
y
of
the
prediction
[13].
W
e
proposed
a
ne
w
formulation,
to
predict
disrupti
v
eness
in
the
future:
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
18,
No.
2,
April
2020
:
907
–
918
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
r
911
y
t
=
(
1
y
t
1
+
2
y
t
2
+
:
:
:
+
p
y
t
p
+
t
+
1
t
1
+
:
:
:
+
q
t
q
)
2
D
:
(3)
where
D
depends
on
four
v
ariables:
time
to
mark
et
(TTM),
Cost,
Politics,
Economics,
Social,
and
T
echnological
(PEST),
and
Mark
et
Share.
The
v
alue
of
disrupti
v
eness
ranges
by
0
D
1
.
The
v
alue
of
tw
o
and
the
disrupti
v
eness
range
were
inspired
by
the
Global
mobile
data
traf
fic
forecast
in
[28],
where
it
defines
data
traf
fic
which
ne
v
er
reached
more
than
tw
o
times
compared
to
the
pre
vious
year
e
v
ery
year;
this
moti
v
ated
the
creation
of
Equation
(3)
to
define
the
D
formula,
which
applies
to
both
mobile
backhaul
and
mobile
backbone
traf
fic.
In
this
paper
,
we
proposed
an
ARIMA
model
as
a
le
g
ac
y
formula
for
modification.
As
a
consequence,
the
results
obtained
from
the
ARIMA
model
were
amplified
using
the
disrupti
v
eness
formula
based
on
four
v
ariables.
As
a
result,
the
result
of
the
time-series
function
returns
Equation
(3),
which
is
based
on
the
disrupti
v
eness
and
le
g
ac
y
formula.
W
e
can
immediately
see
that
the
Global
mobile
data
traf
fic
forecast
until
2021
increases
gradually
each
year
[25].
The
a
v
erage
increase
o
v
er
fi
v
e
years
is
around
47.4
%
.
W
e
e
xtract
the
incremental
traf
fic
each
year
as
illustrated
in
T
able
1.
The
incremental
traf
fic
each
year
moti
v
ated
us
to
analyze
more
deeply
ho
w
to
determine
the
disrupti
v
e
formula.
T
able
1
sho
ws
that
the
mobile
traf
fic
trend
ne
v
er
reached
a
roun
d
tw
o
times
compared
to
the
pre
vious
year
,
which
w
as
later
defined
as
the
maximum
disrupti
v
eness.
Ho
we
v
er
,
due
to
unpredicted
technologies,
mobile
data
traf
fic
might
not
increase
at
all.
Therefore,
the
range
of
disrupti
v
eness
v
alues
applies
from
0
to
1,
where
1
defines
the
maximum
disrupti
v
eness
by
tw
o
times
the
formula,
and
0
defines
the
minimum
disrupti
v
eness,
which
remains
the
same.
T
able
1.
Incremental
traf
fic
based
on
the
Global
mobile
data
traf
fic
forecast
[25]
Y
ear
Incremental
T
raf
fic
(
%
)
2017
54.14
2018
54.54
2019
41.18
2020
45.83
2021
40
The
disrupti
v
eness
v
alue
is
a
judgemental
method
based
on
current
beha
viour
represented
in
the
TTM,
cost,
PEST
,
and
mark
et
share
v
ariables.
These
v
ariables
might
af
fect
the
disrupti
v
eness
v
alue
i
n
the
formula.
TTM
is
the
period
during
which
a
product
has
been
agreed
upon
and
resources
ha
v
e
been
committed
to
a
project.
The
TTM
is
di
vided
into
tw
o
v
ariables:
impact
and
probability
.
The
length
of
the
TTM
gi
v
es
it
fle
xibility
to
decrease
a
nd
increase
significantly
,
depending
on
time-related
processes
[26].
The
simpler
the
products
or
services
pro
vided,
the
shorter
the
TTM
will
be.
The
impact
of
the
implementation
products/services
and
the
probability
that
it
will
be
de
v
eloped
af
fect
the
v
alue
of
the
TTM.
T
able
2
sho
ws
ho
w
scoring
the
TTM
to
define
the
disrupti
v
eness
formula.
Cost
defines
the
total
cost,
including
the
v
ariable
and
fix
ed
costs
and
e
v
en
operational
and
capital
costs.
The
main
general
cost
discussed
here
is
the
amount
needed
to
create
a
ne
w
project
[27].
The
cost
is
di
vided
into
tw
o
dif
ferent
v
ariables:
impact
and
probability
.
The
cost
v
ariable’
s
v
alue
ranges
from
0
to
1;
its
definition
is
pro
vided
in
T
able
2.
PEST
is
considered
in
this
disrupti
v
e
technique
to
determine
the
performance
and
acti
vities
of
b
usinesses,
especially
in
the
long
term
[28].
It
is
clear
that
PEST
might
af
f
ect
technology
,
especially
when
the
technology
i
s
le
g
alized.
F
or
e
xample,
a
ne
w
technology
might
not
be
implemented
in
a
ne
w
project
if
the
rele
v
ant
authorities
do
not
allo
w
it;
this
will
in
turn
af
fect
the
implementation
of
the
ne
w
technology
.
T
able
2
sho
ws
the
PEST
correlation
in
this
v
ariable
which
is
di
vided
by
tw
o;
PEST
Impact
and
PEST
Probability
.
As
with
TTM
and
cost,
thi
s
PEST
v
alue
ranges
from
0
to
1.
Mark
et
Share
v
ariable
represents
the
percentage
of
mark
et
share
of
an
industry
mark
ets
total
sales
o
v
er
a
certain
period.
The
mark
et
s
h
a
re
is
v
ery
important
to
determining
the
le
v
el
of
competiti
v
eness
among
competitors.
T
able
2
sho
ws
that
the
mark
et
share
w
as
di
vided
into
tw
o
v
ariables:
mark
et
share
impact
and
mark
et
share
probability
.
The
mark
et
share
ranges
from
0
to
1;
each
definition
is
sho
wn
in
T
able
2.
The
formula
used
to
identify
disrupti
v
eness
incorporates
the
four
v
ariables
as
sho
wn
in
T
able
2.
These
four
v
ariables
were
used
to
identify
the
v
alue
of
the
disrupti
v
eness,
as
sho
wn
in
(3).
Each
v
ariable
has
its
o
wn
weight
which
af
fect
the
disrupti
v
eness
formula
in
(4).
The
pr
ediction
of
mobile
data...
(Ajib
Setyo
Arifin)
Evaluation Warning : The document was created with Spire.PDF for Python.
912
r
ISSN:
1693-6930
D
=
4
Cost
+
3
TTM
+
2
PEST
+
Mark
et
Share
10
Impact
+
4
Cost
+
3
TTM
+
2
PEST
+
Mark
et
Share
10
Probability
(4)
The
range
of
the
disrupti
v
eness
v
alue
is
defined
in
T
able
2.
Each
v
ariable
has
its
o
wn
weight
or
priority
,
as
sho
wn
in
T
able
2.
The
priority
of
e
v
ery
v
ariable
such
as
Mark
et
Share,
PEST
,
TTM,
and
Cost
ha
v
e
dif
ferent
v
alues
as
sho
wn
in
T
able
2,
which
will
af
fect
the
formula
of
disrupti
v
eness
in
(4).
It
is
assumed
that
the
v
ariable
of
Cost
leads
as
a
first
priority
to
af
fect
disrupti
v
eness,
whereas
mark
et
share
has
the
lo
west
priority
.
The
first
or
last
priority
identify
the
weight
v
alue
in
each
v
ariable
which
leads
t
o
the
final
formula
of
disrupti
v
eness,
as
e
xpressed
in
(4).
T
able
2.
The
risk
f
actor
and
v
ariables
in
detail
2*V
ariable
2*Priority
2*Disrupti
v
eness
Risk
F
actor
Impact
Probability
2*TTM
2*2
Score
1
TTM
runs
shorter
as
the
impact
of
the
implementation
product/services
is
LARGE
The
probability
that
a
SHOR
T
TTM
for
these
products/services
are
implemented
in
the
netw
ork
Score
1
TTM
runs
longer
as
the
impact
of
the
implementation
product/services
is
SMALL
The
probability
that
a
LONG
TTM
for
these
products/services
are
implemented
in
the
netw
ork
2*Cost
2*1
Score
1
T
otal
Cost
compared
to
the
impact
of
the
V
alue
Proposition.
HIGH
means
still
af-
fordable
The
probability
that
the
products
will
sell
to
the
customer
.
HIGH
means
sti
ll
af-
fordable
Score
0
T
otal
Cost
compared
to
the
impact
of
the
V
alue
Proposition.
LO
W
means
rela-
ti
v
ely
not
af
fordable
The
LO
W
probability
of
the
technology
to
of
fer
support
in
terms
of
PEST
2*PEST
2*3
Score
1
Ha
ving
a
LARGE
impact
on
PEST
for
supporting
these
technologies
The
HIGH
probability
of
the
technology
to
of
fer
support
in
terms
of
PEST
Score
0
Ha
ving
a
SMALL
impact
on
PEST
for
supporting
these
technologies
The
LO
W
probability
of
the
technology
to
of
fer
support
in
terms
of
PEST
2*Mark
et
Share
2*4
Score
1
The
HIGH
impact
of
the
mark
et
share
on
subscribers
The
percentage
of
mark
et
share
to
lead
others
Score
0
The
LO
W
impact
of
mark
et
share
on
subscribers
The
percentage
of
mark
et
share
to
lead
others
3.3.
F
or
ecasting
err
or
management
The
analysis
of
this
formula
compares
the
global
mobile
data
forecast
prediction
with
the
model
calculation
used
in
this
paper
.
The
disrupti
v
e
formula
w
as
analyzed
using
a
percentage
error
comparing
between
con
v
entional
ARIMA
and
ARIMA
with
disrupti
v
e
formula,
which
are
defined
as
follo
ws:
%
E
r
r
or
=
x
y
y
100%
:
(5)
where
x
refers
to
the
Global
mobile
data
traf
fic
forecast
,
and
y
refers
to
the
type
of
model
used–in
this
case,
the
con
v
entional
ARIMA
and
the
ARIMA
with
disrupti
v
e
formula.
The
percentage
error
defined
the
significance
of
an
error
compared
to
the
mobile
data
traf
fic,
where
the
lo
west
error
rate
led
to
better
performance
in
deciding
the
forecast
v
alue.
4.
3G
AND
4G
FORECASTING
The
formula
of
disrupti
v
eness
combined
with
ARIMA
has
been
defined.
This
section
will
e
xplain
tw
o
main
analyses:
3G
and
4G
forecasting.
Based
on
the
mobile
dat
a
traf
fic
data
set
in
Figure
2,
the
3G
traf
fic
reached
the
highest
payload
traf
fic
in
September
2016.
Ho
we
v
er
,
during
the
subsequent
year
,
3G
traf
fic
reduced
slo
wly
in
March
2017,
and
remains
stable
afterw
ards.
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
18,
No.
2,
April
2020
:
907
–
918
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
r
913
Figure
2.
Comparison
between
ARIMA
model
and
ARIMA
with
disrupti
v
e
formula
Based
on
the
ARIMA
calculation,
defined
in
the
black
line
in
Figure
2,
the
prediction
of
this
tr
af
fic
sho
ws
a
small
decrease
in
the
first
month,
after
which
it
remained
stable
until
the
end
of
March
2019.
The
ARIMA
calculation
used
an
ordered
ARIMA
(2,1,3),
where
it
defined
order
tw
o
for
AR,
one
for
dif
ferentiating,
and
three
for
MA.
The
order
calculation
is
calculated
by
data
analysis
tools,
whi
ch
i
s
called
R
studio,
to
support
predicti
v
e
analysis
using
a
Akaik
e
Information
Criterion
(AIC).
Based
on
the
combined
ARIMA
with
a
disrupti
v
eness
formula,
as
sho
wn
in
the
dotted
line
in
Figure
2,
the
graph
sho
ws
a
significant
v
alue
on
ARIMA
with
a
disrupti
v
e
formula
compared
to
the
con
v
entional
ARIMA.
The
disrupti
v
eness
v
alue
w
as
based
on
the
cost,
TTM,
PEST
,
and
mark
et
share
v
ariables,
which
are
defined
in
T
able
3.
T
able
3.
The
disrupti
v
e
v
ariables
in
the
3G
and
4G
Netw
orks
2*V
ariable
3G
Netw
ork
4G
Netw
ork
Impact
Probability
Impact
Probability
TTM
0.2
0.2
0.5
0.7
Cost
0.4
0.8
0.7
0.5
PEST
0.3
0.8
0.5
0.5
Mark
et
Share
0.6
0.6
0.7
0.7
D
0.34
0.6
0.6
0.58
Based
on
the
results
in
Figure
2,
there
w
as
a
dif
ference
in
traf
fic
of
around
8
Mb/s
o
v
er
the
year
.
W
e
conclude
that
from
2018
to
2019,
based
on
T
able
3,
the
PEST
and
cost
v
ariables,
especially
with
respect
to
probability
,
sho
wed
a
significant
v
alue,
which
reached
0.8
from
1.0.
This
might
ha
v
e
caused
the
3G
mobile
netw
ork
penetration,
which
is
still
promised
for
technology
implementation
across
the
Indonesian
islands.
In
f
act,
Base
Station
(BS)
are
still
una
v
ailable
on
se
v
eral
islands
especially
rural
areas.
Therefore,
3G
might
be
preferred
for
application
compared
to
other
technologies
re
g
arding
PEST
and
cost
probability
.
Ho
we
v
er
,
PEST
might
not
af
fect
the
o
v
erall
disrupti
v
eness
v
alues
much,
since
it
is
a
third
priority
,
after
cost
and
TTM.
The
TTM
v
ariable
illustrated
in
T
able
3
sho
ws
a
small
v
alue
of
0.2
from
1.0,
which
w
as
caused
by
the
3G
netw
ork
trend
in
2019.
Since
mobile
netw
ork
t
rends
are
mo
ving
to
w
ards
L
TE
and
5G
netw
orks
in
2019
to
support
lo
w-po
wer
de
vices,
3G
might
not
be
preferable
for
implementation
in
mobile
traf
fic.
Figure
3
sho
ws
from
the
trends
in
the
data
set
that
L
TE
mobile
data
traf
fic
significantly
increased
from
March
2017
until
the
end
of
No
v
ember
2017,
b
ut
then
remained
stable
until
March
2018.
The
prediction
sho
ws
constant
data
traf
fic
o
v
er
a
year
starting
f
rom
March
18.
Based
on
tw
o
calculations,
the
trend
prediction
analysis
using
ARIMA
and
the
ARIMA
with
disrupti
v
e
formula
as
depicted
in
the
graph
in
Figure
4
after
March
2018,
sho
ws
dif
ferent
payload
traf
fic
around
40
Mb/s
o
v
er
a
year
.
Figure
3.
Comparison
of
4G
mobile
traf
fic
with
a
prediction
The
pr
ediction
of
mobile
data...
(Ajib
Setyo
Arifin)
Evaluation Warning : The document was created with Spire.PDF for Python.
914
r
ISSN:
1693-6930
Based
on
ARIMA
as
sho
wn
in
the
black
line
in
Figure
3,
the
prediction
traf
fic
sho
ws
a
small
decrease
at
first
and
then
remains
stable
o
v
er
a
year
.
The
ARIMA
model
used
in
this
formula
used
ARIMA
(1,1,1)
based
on
the
AIC
calculat
ion
in
R
Studio.
Based
on
ARIMA
with
disrupti
v
e
formula
in
the
dotted
line
from
Figure
3,
the
ef
fects
of
four
v
ariables
introduced
in
Section
IV
.B
sho
w
a
significant
contrast
with
the
ARIMA
formula.
The
dif
ference
in
v
alue
between
ARIMA
with
disrupti
v
e
formula
and
the
ARIMA
model
is
approximately
40
Mb/s
o
v
er
the
time
series.
During
this
year
,
the
four
v
ariables
mostly
had
the
same
a
v
erage
for
the
Impact
and
Probability
.
In
the
4G
netw
ork,
based
on
T
able
3,
the
mark
et
share
leads
in
4G
using
a
relati
v
e
higher
v
alue
compared
to
other
v
ariables,
which
is
0.7
for
both
probability
and
impact.
This
is
mainly
caused
by
4G
netw
ork
penetration,
which
increased
relati
v
ely
from
2018
to
2019.
The
TTM
Probability
and
cost
Impact
had
a
v
alue
of
0.7
from
1.0,
since
4G
is
an
af
fordable
technology
to
support
higher
traf
fic.
A
higher
cost
for
4G
might
still
be
preferable
if
compared
to
the
impact
of
this
technology
on
users,
where
most
people
and
some
de
vices
are
using
more
data
than
in
pre
vious
years.
This
might
cause
the
cost
impact
to
be
higher
than
others.
The
TTM
Probability
,
sho
wing
0.7
from
1.0
in
T
able
3,
w
as
caused
by
the
impact
of
this
technology
as
well.
The
beha
viour
of
people
in
2019
is
e
xpected
to
support
digitalization
technology
that
consumes
more
data
traf
fic
in
the
netw
ork.
This
will
lead
to
shorter
TTM
to
support
the
mobility
de
vices
in
the
netw
ork.
5.
RESUL
T
AND
DISCUSSION
The
simulation
results
using
ARIMA
model
and
an
ARIMA
combination
with
disrupti
v
e
formula
ha
v
e
been
described
in
Figure
2
and
Figure
3,
respecti
v
ely
.
The
increasing
traf
fic
using
a
disrupti
v
eness
formula
for
3G
and
4G
technologies
significantly
escalated
the
base
v
alue
of
ARIMA
model
by
8
Mb/s
and
40
Mb/s,
respecti
v
ely
.
The
four
v
ariables
were
deemed
more
promising
for
accurate
prediction
compared
with
using
the
ARIMA
model.
The
ARIMA
calculation,
which
w
as
based
on
past
and
present
v
alues
and
MA,
might
generate
inaccurate
predictions
if
disrupti
v
e
technologies
are
not
considered.
T
o
assess
this
issue,
this
study
utilised
a
percentage
error
that
compared
between
con
v
entional
ARIMA
and
ARIMA
with
dis
rupti
v
e
formula
with
the
global
mobile
data
traf
fic
forecast.
5.1.
Err
or
perf
ormance
The
Error
performance
subsection
aims
to
compare
the
percentage
error
between
tw
o
models
in
3G
and
4G
traf
fic
based
in
the
results
obtained
in
Section
4.
Based
on
(5),
the
global
data
traf
fic
v
ariable
used
a
global
mobile
data
forecast
[28],
which
w
as
identified
using
a
data
set
multiplied
by
the
incremental
v
alue
in
T
able
1
in
the
year
2018-2019.
Additionally
,
the
model
calculation
in
(5)
applied
the
con
v
entional
ARIMA
and
ARIMA
with
disrupti
v
e
formula.
The
global
mobile
data
traf
fic
calculated
the
a
v
erage
mobile
data
traf
fic
obtained
from
the
data
set
in
3G
and
4G—i
n
this
case,
from
2016
to
2018.
The
data
set
w
as
a
v
eraged
o
v
er
2016
to
2018
and
multiplied
using
an
incremental
v
alue
based
on
T
able
1,
which
w
as
41.17
%
from
2018-2019.
By
obtaining
the
data
set
traf
fic
and
the
incremental
v
alue
from
this
data,
the
optimized
prediction
traf
fic
w
as
obtained,
which
is
sho
wn
in
T
able
4.
T
able
4
sho
ws
optimized
traf
fic
based
on
Mobile
Data
F
orecast,
where
it
increases
41.17
%
from
2018
to
2019.
The
data
traf
fic
in
2019
based
on
T
able
4
will
be
compared
to
the
prediction
based
on
Con
v
entional
ARIMA
and
ARIMA
+
Disrupti
v
e
F
ormula.
It
assumed
that
the
global
mobile
data
traf
fic
forecast
has
more
accurate
prediction
based
on
se
v
eral
types
of
research.
T
able
4.
T
raf
fic
comparison
between
3G
and
4G
in
2018-2019
2*T
echnology
Y
ear
2018
2019
3G
55.06
Mb/s
77.63
Mb/s
4G
207.87
Mb/s
293.09
Mb/s
In
3G,
the
ARIMA
with
disrupti
v
e
formula
reaches
56.67
Mb/s,
which
is
almost
optimized
to
72.17
Mb/s
in
T
able
4.
The
error
rate
seemed
to
be
lo
wer
than
in
the
con
v
entional
ARIMA.
Besides,
in
4G,
the
ARIMA
with
disrupti
v
e
formula
reaches
199.6
Mb/s,
whereas
con
v
entional
ARIMA
reaches
156.93
Mb/s,
which
is
f
ar
higher
than
the
global
mobile
data
traf
fic,
293.09
Mb/s
in
T
able
4.
The
error
rate
in
both
v
al-
ues
is
caused
by
se
v
eral
f
actors,
i.e.:
backhaul
traf
fic
and
de
v
eloping
country
f
actors.
The
calculation
of
the
ARIMA
model
used
backhaul
traf
fic,
where
it
seemed
to
be
more
ne
g
ati
v
e
compared
to
the
global
data
forecast.
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
18,
No.
2,
April
2020
:
907
–
918
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
T
elecommun
Comput
El
Control
r
915
Ho
we
v
er
,
the
lo
wer
error
rate
in
ARIMA
with
disrupti
v
e
formula
is
more
promising
compared
to
the
con
v
entional
ARIMA,
where
it
defined
more
optimized
v
alues
from
2018
to
2019.
This
demonstrates
that
the
ARIMA
with
disrupti
v
e
formula
had
more
accurate
prediction
compared
to
the
con
v
entional
ARIMA.
Internet
penetrat
ion
in
de
v
eloping
countries
has
increased
relat
i
v
ely
slo
wly
compared
to
de
v
eloped
countries,
which
might
af
fect
mobile
data
traf
fic.
In
this
case,
compar
ed
to
global
mobile
data
traf
fic,
the
con
v
entional
ARIMA
sho
ws
a
small
increase
o
v
er
a
year
for
3G
and
4G,
as
sho
wn
in
Figures
3
and
4.
Ho
we
v
er
,
the
ARIMA
with
disrupti
v
e
formula
w
as
more
positi
v
e,
which
is
closely
related
to
the
mobile
data
traf
fic
trend
and
the
internet
implementation
program
across
Indonesia.
This
led
to
more
accurate
prediction.
5.2.
V
ariable
analysis
The
four
f
actors
that
ha
v
e
been
defined-TTM,
Cost,
PEST
,
and
Mark
et
Share-significantly
af
fected
the
con
v
entional
ARIMA.
As
e
xplained
earlier
,
the
v
ariable
cost
had
maximum
priority
o
v
er
others,
while
mark
et
share
w
as
less
important.
T
o
v
alidate
the
v
ariables,
we
identified
the
dif
ferent
le
v
els
of
each
v
ariable
using
the
maximum
incremental
traf
fic
of
Impact
and
Probability
.
The
maximum
order
of
Impact
and
Probability
,
M
,
can
be
e
xpressed,
M
=
N
max
(
I
mpact
P
r
obabil
ity
)
:
(6)
where
N
is
equal
to
the
number
of
weight,
which
is
identified
in
T
able
2.
F
or
e
xample,
the
v
ariable
cost
has
four
maximum
weights,
where
the
v
ariable
mark
et
share
has
1
maximum
weight.
Besides,
Impact
and
Probability
are
assumed
to
be
at
maximum
probability
,
which
is
equal
to
1.
It
also
assumes
that
the
other
v
ariables
are
zero
if
a
particular
v
ariable
is
calculated.
Based
on
(6),
T
able
5
sho
ws
dif
ferent
maximum
orders
in
each
v
ariable.
Moreo
v
er
,
Figure
4
took
an
e
xample
in
the
3G
netw
ork,
sho
wing
the
dif
ferent
incremental
traf
fic
using
the
four
v
ariables
identified
in
T
able
5.
The
incremental
traf
fic
sho
ws
a
dif
ferent
prediction
w
as
used
in
the
ARIMA
model.
T
able
5.
Maximum
incremental
traf
fic
Disrupti
v
e
V
ariables
Incremental
T
raf
fic
(
%
)
TTM
9
Cost
16
PEST
4
Mark
et
Share
9
Figure
4.
Comparison
of
the
dif
ferent
mobile
netw
orks
Based
on
Figure
4
and
T
able
5,
the
v
ariable
cos
t
will
af
fect
a
maximum
16
%
incremental
v
alue
compared
t
o
the
con
v
entional
ARIMA.
Cost
is
cross-related
to
the
re
v
enues
of
companies.
This
is
reasonable
since
if
we
imagine
that
companies
ha
v
e
a
significant
amount
of
re
v
enues
to
utilize
the
initial
and
maintenance
costs
for
Capital
Expenditure
(CAPEX)
and
Operational
Expenditure
(OPEX),
the
y
will
prioritize
customer
demands,
including
data
traf
fic
speed
and
latenc
y
,
which
are
essential
for
users.
By
this
v
ariable,
in
the
Indonesian
case,
the
operators
ha
v
e
an
opportunity
to
either
spread
the
mobile
base
stations
into
dif
ferent
locations
or
to
mak
e
re
gular
netw
orks
denser
to
increase
their
capacity
.
The
cost
might
correlate
with
other
v
ariables,
such
as
PEST
,
TTM,
and
Mark
et
Share.
If
mobile
operators
h
a
v
e
more
re
v
enues,
this
will
af
fect
disrupti
v
eness
and
other
v
ariables.
F
or
e
xample
,
the
re
v
enues
will
consider
the
frequenc
y
allocation
that
has
been
determined
by
the
re
gulators
in
each
particular
country
,
where
more
re
v
enues
will
probably
decide
more
frequenc
y
allocations.
The
pr
ediction
of
mobile
data...
(Ajib
Setyo
Arifin)
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916
r
ISSN:
1693-6930
TTM
and
PEST
are
the
second
and
third
pri
orities
in
the
disrupti
v
eness
v
ariables.
The
maximum
total
incremental
traf
fic
is
16
%
and
9
%
,
respecti
v
ely
,
as
sho
wn
in
Figure
4.
The
e
xample
real-w
orld
case
of
this
v
ariable
is
the
license
readiness
to
implement
ne
w
frequencies
and
technologies
i
n
the
country
.
F
or
e
xample,
the
main
challenge
for
the
license
readiness
e
xample
is
the
millimetre
w
a
v
e
in
5G
technology
,
where
se
v
eral
steps
are
needed
to
assess
the
frequenc
y
spectrum
in
their
country
.
The
ne
w
spectrum
in
millimetre
w
a
v
e
should
consider
permission
to
open
a
ne
w
license
spectrum.
This
license
is
also
a
dependent
f
actor
with
the
cost
profile
of
the
mobile
operators,
where
ha
ving
a
ne
w
spectrum
leads
to
higher
risk
of
spending
at
more
considerable
cost.
Besides,
technology
readiness
is
also
a
dependent
f
actor
with
the
cost
profile
of
mobile
companies,
where
technology
readiness
might
be
delayed
if
traf
fic
penetration
in
the
country
is
not
co
v
ered
100
%
.
As
an
e
xample,
in
Indonesia,
the
L
TE
mobile
stations
will
be
implemented
later
,
since
3G
stations
ha
v
e
not
yet
been
implemented
across
the
whole
of
Indonesia.
Therefore,
to
support
more
ef
ficienc
y
,
3G
is
still
preferable
to
L
TE,
which
reduces
more
CAPEX
and
OPEX,
for
ne
w
stations.
Both
cases
might
cause
a
delay
in
technology
implementation
for
the
country
,
where
both
v
ariables
still
depend
mostly
on
cost.
As
a
result,
cost
is
still
the
highest
priority
af
fecting
mobile
data
traf
fic.
Mark
et
share,
the
lo
west
priorit
y
,
determines
only
1
%
of
the
incremental
traf
fic.
Mark
et
share
does
not
af
fect
mobile
data
traf
fic
v
ery
much
if
traf
fic
and
re
v
enues
are
relati
v
ely
increasing
in
v
ersely
,
which
has
occurred
in
the
telecommunication
en
vironment.
This
mak
es
mark
et
share
the
lo
west
priority
in
the
disrupti
v
eness
v
alue.
T
o
conclude,
four
v
ariabl
es
are
the
main
f
actors
of
disrupti
v
e
traf
fic,
with
cost/re
v
enues
being
the
most
dominant
f
actors
that
af
fect
disrupti
v
e
traf
fic.
5.3.
Backhaul
analysis
The
arri
v
al
of
disrupti
v
e
technologies
will
af
fect
the
total
capacities
in
the
mobile
backhaul.
As
sho
wn
in
Section
5,
4G
and
3G
traf
fic
ha
v
e
amplified
traf
fic
in
the
con
v
entional
ARIMA,
around
35
Mb/s
and
10
Mb/s,
respecti
v
ely
.
Other
technology
,
such
as
2G
systems,
which
is
assumed
to
be
stagnant,
consumes
around
2
Mb/s
each
year
.
Besides,
the
5G
netw
ork
has
not
been
implemented
yet,
and
while
IoT
systems
are
increasing
this
year
,
we
still
assumed
no
IoT
due
to
ha
ving
the
lo
west
bit
rate
and
smallest
total
number
of
de
vices.
Illustrating
all
these
f
actors,
T
able
6
presents
an
o
v
ervie
w
of
the
predicted
a
v
erage
mobile
data
traf
fic
between
2017
and
2018,
mainly
in
each
site,
using
ARIMA
with
disrupti
v
e
formula.
T
able
6.
A
v
erage
Mobile
Data
T
raf
fic
2017
and
2018
2*T
echnologies
A
v
erage
Mobile
Data
T
raf
fic(Mb/s)
2018
2019
2G
2
2
3G
54.62
56.67
4G
198.03
199.6
T
otal
254.65
258.23
By
calculating
the
ARIMA
with
disrupti
v
e
formula
model,
we
conclude
that
the
mobile
backhaul
could
support
the
old
micro
w
a
v
e
technologies,
where
the
future
backhaul
will
need
at
least
the
a
v
erage
of
258.2
Mb/s
each
site
based
on
T
able
6.
This
capacity
is
basically
could
be
supported
by
the
e
xisting
micro
w
a
v
e
technologies.
Ho
we
v
er
,
to
anticipate
the
une
xpected
traf
fic
in
the
future,
this
paper
recommends
a
list
of
feature
for
the
mobile
backhaul,
which
are
(from
the
most
ef
ficient):
Using
HOM
that
supports
traf
fic
greater
than
300
Mb/s,
for
instance,
with
1024
or
2048
QAM,
implementing
more
antennas
in
the
mobile
backhaul
systems
to
increase
capacity
,
such
as
MIMO
or
Massi
v
e
MIMO,
migrating
old
micro
w
a
v
e
technologies
to
fiber
optics.
This
paper
founds
an
ef
fecti
v
e
and
accurate
w
ay
to
predict
the
traf
fic
forecast
based
on
statistical
and
judgemental
approach.
W
ith
the
combination
of
ARIMA
model
and
disrupti
v
e
formula
that
this
paper
proposed,
it
has
sho
wn
that
ARIMA
is
more
accurate
if
it
is
associated
with
a
Judgemental
approach
to
correct
the
errors.
6.
CONCLUSION
The
major
contrib
ution
of
the
study
is
the
de
v
elopment
of
a
ne
w
formula
in
the
ARIMA
model
to
predict
forecast
traf
fic
based
on
four
v
ariables:
TTM,
cost,
PEST
,
and
mark
et
share.
Our
research
confirms
that
disrupti
v
e
technology
af
fect
the
mobile
data
traf
fic
if:
the
telecommunication
companies
are
profitable;
the
time
to
mark
et
to
implement
ne
w
project
is
acceptable,
the
en
vironment
of
PEST
supports
ne
w
technologies
TELK
OMNIKA
T
elecommun
Comput
El
Control,
V
ol.
18,
No.
2,
April
2020
:
907
–
918
Evaluation Warning : The document was created with Spire.PDF for Python.