TELKOM
NIKA
, Vol.13, No
.3, Septembe
r 2015, pp. 1
069
~10
7
8
ISSN: 1693-6
930,
accredited
A
by DIKTI, De
cree No: 58/DIK
T
I/Kep/2013
DOI
:
10.12928/TELKOMNIKA.v13i3.1544
1069
Re
cei
v
ed Fe
brua
ry 11, 20
15; Re
vised
May 14, 20
15
; Accepte
d
Ju
ne 6, 2015
Simulation and Implementation Model of Productivity
Measur
e
ment Internet Bandwidth Usage
Tjahjanto*
1
, Benh
ard
Sitohang
2
, Sudarso Kaderi
Wir
y
ono
3
1,2
ST
EI-IT
B
, Schoo
l of Electric
al Eng
i
ne
eri
ng
and
Informatic
s
, Institute
T
e
chno
log
y
B
and
u
ng,
Institut T
e
knolo
g
i
Ban
d
u
ng,
La
btek V, Lantai I
V
, Jl. Ganesa No. 10 Ban
d
u
n
g
, Indon
esia
3
SBM-IT
B
, School of Busi
ness
and Man
a
g
e
m
ent, Institute
T
e
chn
o
lo
g
y
Ban
dun
g
*Corres
p
o
ndi
n
g
uthor, e-mai
l
: cah
y
anto
2
0
0
0
@
gmai
l.com
1
, ben
hard
@
stei.i
tb.ac.id
2
,
sudars
o_k
w
@
sbm-itb.ac.id
3
A
b
st
r
a
ct
T
he Intern
et is
used
by
a very
larg
e n
u
m
b
e
r
of us
ers, fro
m
t
he or
di
nary
us
er co
mmu
n
ity,
throug
h
speci
a
l
users, l
i
ke p
e
o
p
le
w
i
th hig
h
i
n
tel
l
ectu
al l
e
ve
l. T
he
gr
ow
th in th
e n
u
m
b
e
r of
users
i
s
incre
a
si
ng v
e
ry
fast. Internet h
a
s als
o
b
een
u
s
ed by
multi-se
ctor busi
ness
e
s w
i
th mu
lti pro
f
ession. It mak
e
s infor
m
ation
the
intern
et usag
e
somethi
ng very
strat
egic, one
of w
h
ich infor
m
ation is
prod
uc
tivity internet b
andw
idth
usag
e.
T
herefore th
e
researc
h
are
n
eed
ed a s
i
mul
a
tion
and
i
m
pl
ementati
on
mo
del to b
e
a
b
le
to me
asure t
h
e
prod
uctivity
of the
i
n
ternet ba
ndw
idth usa
g
e
,
w
h
ic
h can
lat
e
r bec
o
m
e t
h
e
basis
of th
e
me
asur
e
m
ent
of
prod
uctivity. T
h
is pa
per d
e
s
c
ribes a
mode
l impl
e
m
ent
ati
on an
d si
mu
la
tion of pro
duct
i
vity me
asur
e
m
e
n
t
intern
et ba
nd
w
i
dth usag
e,
w
h
ich descr
ib
es al
l
poss
i
bl
e meas
ure
m
e
n
t valu
es o
b
tain
ed, a
nd is
a
contin
uati
on
of
prev
ious
res
earch, w
h
ic
h
i
s
the
bas
ic
c
once
p
t of
pro
ductivity
in
th
e us
e
of i
n
ter
net
ban
dw
idth an
d
how
to measu
r
e it, so that th
e me
as
ur
ing re
sults can be u
s
ed as a gu
id
e
in deter
mi
ne th
e
directi
on of po
li
cy and the pr
o
v
ision
of pr
od
u
c
tive Internet b
andw
idth
usag
e.
Ke
y
w
ords
: int
e
rnet
meas
ure
m
e
n
t, internet
pr
od
uctivity, productiv
i
ty me
a
s
ure
m
e
n
t
Copy
right
©
2015 Un
ive
r
sita
s Ah
mad
Dah
l
an
. All rig
h
t
s r
ese
rved
.
1. Introduc
tion
Internet ha
s been kno
w
n
to the public at la
rge, e
s
pe
cially sin
c
e the pop
ul
arity of
Face
boo
k
(F
B), peo
ple i
n
Ja
ka
rta
are
among
the l
a
rge
s
t inte
rnet
users-espe
ci
ally on t
w
itter-in
Asia a
nd a
r
e
numbe
r 4 i
n
t
he world
[1]. IT devel
opm
e
n
t has trigg
e
rs the
occu
rre
n
ce
of a maj
o
r
revolution, th
e informatio
n
revolution n
a
me. Internet
has be
en u
s
ed every
w
he
re and ha
s be
en
use
d
by anyo
ne, from the
elite to the lower
cla
s
ses.
Internet can
be acce
ssed
from a variet
y o
f
device
s
, fro
m
the p
e
rson
al
com
pute
r
at
home
or in th
e office
until t
he mo
bile i
s
a lapto
p
, I-Pa
d,
Smart Phone.
The Intern
et is ea
sy, chea
p and ma
ss, makin
g
the internet a very strategic
so
mething
that must
be
controlled, th
e problem
is
how to
me
asure
produ
ctive ou
r inte
rnet
usage, eith
er by
personal, especially by governme
nt inst
itutions.Internet will bri
ng
a broad impact (multi-sector)
in this life an
d the future;
it is the need
for a
frame
w
ork that
ca
n mea
s
ure th
e pro
d
u
c
tivity of
Internet ban
d
w
idth u
s
ag
e.
Based
on
th
e ab
ove b
a
ckgroun
d, this pap
er
de
scribes a m
odel
implem
entat
ion a
n
d
simulatio
n
of
prod
uctivity measurement
internet
b
a
n
d
width
u
s
ag
e
,
whi
c
h
de
scribes all
po
ssi
ble
measurement
values obta
i
ned, and is a continuat
i
on of previo
us re
se
arch.
The result of
previou
s
re
se
arch
i
s
the b
a
si
c con
c
ept of
pro
d
u
c
tivity in the u
s
e
of intern
et ba
ndwi
d
th an
d
how
to mea
s
u
r
e it.
I
mplementati
on a
nd
simul
a
tion a
r
e
pe
rforme
d in
this
resea
r
ch u
s
in
g the f
r
ame
w
ork
of previou
s
resea
r
ch tha
t
produ
ctivity measur
eme
n
t frame
w
ork without reg
a
rd to inte
rn
et
band
width
u
s
ag
e coeffici
ent variabl
e, the varia
b
le
value is ta
ken th
ro
ugh
the process of
Analytical Hie
r
archy Pro
c
e
ss
(AHP).
The results of
this research
, simulation
a
nd imple
m
ent
ation cond
uct
ed p
r
oof p
r
od
uctivity
of Internet ba
ndwi
d
th usag
e can b
e
mea
s
ured.
The importance of produ
ct
ivity
[2, 3] is
on a micro scal
e enterpri
ses, will increase the
comp
any'
s
profits and the
macro
scale
of a count
ry will incre
a
se
the GDP (G
ross Dome
stic
Produ
ct), whi
c
h mea
n
s in
crea
sing the
welfare of the community.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 13, No. 3, September 20
15 : 1069 – 10
78
1070
For a
com
p
re
hen
sive und
e
r
stan
ding
of the produ
ctivity of the internet ban
dwidt
h
usage,
of cou
r
se, m
u
st un
dersta
nd the m
eani
ng an
d defini
t
ion of the word "p
rod
u
cti
v
ity" in general a
s
the word
"produ
ctivity", a
nd the
mea
n
i
ng of "p
ro
du
ctivity" in the use of
i
n
ternet ba
nd
width, a
s
well
as the
meanin
g
of "
p
rod
u
ctivity internet
ba
n
d
w
idth
usage"
as a
sin
g
le
entity, it has
bee
n
descri
bed
in
previo
us
st
udie
s
[4-6], and the
ba
sis for th
e d
e
velopme
n
t
of model
s a
nd
simulat
i
o
n
s.
Produ
ctivity is the
ratio
of output
di
vi
ded by i
n
put ba
se
d o
n
e
c
on
omic theo
ry.
Mean
while,
a
c
cordi
ng to
Mali in 1
978
that pro
d
u
c
tivi
ty is
as follows [7, 8]. Produc
tivity internet
band
width u
s
age ca
n be measured by looking at th
e factors effectivene
ss an
d efficiency o
f
the
Internet ban
d
w
idth u
s
ag
e.
Variabl
e pro
ductivity internet ba
nd
wid
t
h
usa
ge at
the mome
n
t
there a
r
e
14 stu
d
y
variable
s
, de
scribe
d in Ta
ble 1. The followin
g
:
Table 1. Vari
able Pro
d
u
c
tivity Measure
m
ent Internet
Bandwidth
Usag
e
No
Variable Na
me
Descrip
tion
Variable T
y
p
e
Unit
1
P
Productivity
Quantitative
% Level Degr
ee
2 U
User
Compet
ency
Qualitative
Level
3
Up
User Price
Quantitative
Cost Factor Leve
l
4 G
Goal
Qualitative
Goal
Level
5 P
Place
Qualitative
Place
Level
6
T
Slot Time
Qualitative
Slot Time Level
7 T
Time
Duration
Quantitative
Hour
8 C
Contents
Qualitative
Content
Level
9
F
Fast Speed
Quantitative
Device Level
10
Fp
Fast Price
Quantitative
Cost Factor Leve
l
11 V
Volume
Quantitative
Mb
y
t
e
12 B
Band
w
i
dth
Quantitative
Mb
y
t
e
13
Bp
Band
w
i
dth Price
Quantitative
Cost Factor Leve
l
14 Rc
Reliability
Conne
ct
Q
uantitative
%Reliability
15 Bu
Band
w
i
dth
Utilit
y
Q
uantitative
%Utility
Variabl
e me
asu
r
em
ent o
f
current re
search
ha
s i
n
crea
sed
wh
en co
mpa
r
e
d
to the
previou
s
re
se
arch vari
able
s
[4, 5], whi
c
h add
s Va
ria
b
le Cost F
a
ct
or, which
con
s
ist
s
of a va
ri
able
Fp: Comp
ute
r
Co
st Fa
ctor, Bp: Bandwidth Co
st
Fact
or, Up: Use
r
Co
st Facto
r
. While a d
e
tail
ed
explanation
o
f
other variabl
es have b
een
describ
ed in
previou
s
stu
d
i
es [5].
2. T-Frame
w
o
r
k
Measur
e
m
e
nt
Addition of F
p
, Bp, and
Up re
sulte
d
in
cha
nge
s i
n
the functio
n
of variabl
e in
put and
output fun
c
tio
n
s,
see
the
measurement
frame
w
o
r
k
i
n
Fig
u
re
1, t
he
so
-call
ed
T-Frame
w
o
r
k: (T-
Frame
w
o
r
k i
s
a F
r
ame
w
o
r
k to m
e
a
s
ure
pro
d
u
c
tivity internet
ban
d
w
idth u
s
a
ge
usin
g vari
abl
es
and indi
cato
rs whi
c
h h
a
ve been d
e
termi
ned, and it ha
s bee
n su
gge
sted in previo
us stu
d
ie
s [4]).
2.1. Measur
e
men
t
Formula
The pictu
r
e a
bove Figu
re 1
measu
r
em
en
t fr
amewo
r
k, obtaine
d the followin
g
funct
i
ons:
Ac
tual Produc
tivity
.
.
.
.
.
.
.
.
.
.
.
(
1
)
PA, Actual Produ
ctivity is a massive p
r
odu
ct
ivity value of the formulated m
a
thematical
cal
c
ulatio
ns d
e
scrib
ed ab
o
v
e function
s, having re
ga
rd
to units on e
a
ch vari
able.
Produ
ctivity Index P(%)=
100%
(
2
)
P%, Produ
ctivity Index in perce
nt, is t
he divisi
on
b
e
twee
n the
PA and the
value of
measuri
ng th
e value of maximum prod
u
c
tivi
ty measure P (max) mu
ltiplied by 100
%.
P(max) = M
a
ximum Value of Measu
r
in
g
(3)
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Sim
u
lation and Im
plem
enta
t
ion Model of Produ
ct
ivit
y Measurem
ent Internet… (Tj
ahjanto
)
1071
P(max) i
s
a
measure of t
he value
of
measuri
ng th
e maximum
prod
uctivity o
b
tained
whe
n
mea
s
u
r
ing all variabl
es are be
st position
ed opti
m
ally.
Figure 1. Measu
r
em
ent Framework
2.2.
Variable Mea
s
uremen
t Al
gorithms
Variabl
e me
asu
r
em
ent al
gorithm i
s
a method or
techni
que to
get the number of
measurement
s, eithe
r
a
u
to
matically fro
m
the
com
pute
r
s
y
s
t
e
m
, as
w
e
ll as
from th
e
da
ta
ta
b
l
e
that ha
s b
e
e
n
previou
s
ly
config
ure
d
, th
e ap
pro
p
ri
ate
provi
s
ion
s
of the m
e
a
s
ure
m
ent [6]. Fig
u
re
2 is a blo
ck di
agra
m
illustra
tes ho
w to ge
t the value of
a variable m
e
asu
r
e its ma
g
n
itude.
Figure 2. Input Variable
s
Measurement
s
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 13, No. 3, September 20
15 : 1069 – 10
78
1072
To find
the
n
u
mbe
r
of
me
asu
r
em
ent va
riable
s
autom
atically, from
the me
asu
r
e
d
obj
ect,
the necessa
ry technical measures, in
accord
an
ce
with Figure
2 also havi
ng reg
a
rd to the
system mo
de
l in Figure 3 i
s
the ba
sis
fo
r mea
s
ureme
n
t of the following
step
s:
1. Start the measure
m
ent
2. Preparatio
n of configu
r
a
t
ion setting
s
a)
Input Table T
y
pe of Industry
b)
Input Table User P
r
ofile
c)
Table Input Device Profile
d)
Input Table Category Cont
ents
e)
Input System Config
uratio
n
Data (Me
a
su
reme
nt Scale
)
3. Determi
ne
the measure
m
ent duration
t
1
-t
n
4. Read d
a
ta from proxy
5. Colle
ct data and IP Use
r
Content
6. Filter the data accordin
g
to the conten
t catego
ry
7. Put the measu
r
ing tabl
e
8. Save the configuration settings
9. Calculation
s
co
rrespon
di
ng mea
s
u
r
em
ent formula
10. Display the value of the measu
r
em
en
t
11. Save the measurement
data
12. End of measure
m
ent
Figure 3. Fun
c
tion Based
Matric
3.
Measur
e
men
t
Applica
t
ion
Architectur
e
Figure 4 illustrates the l
o
cation or posit
i
on
measurement applicati
ons on a
computer
netwo
rk,
a
se
rver
can
be
in one
box, o
r
ca
n al
so
be i
n
two
se
rver
box, and
pla
c
ed bet
wee
n
t
h
e
firewall a
nd the hub switch. Also, that
can
cre
a
te
a comp
uter-ba
s
ed applia
nce
that is installed
insid
e
mea
s
u
r
eme
n
t appli
c
ation
s
p
r
od
uctivity in
tern
et band
width
usag
e, whi
c
h has it
s own IP
numbe
r [6].
4.
Measur
e
men
t
Applica
t
ion
Architectur
e
4.1.
Variable Qua
litativ
e Le
v
e
l
w
i
th
AHP
Measurement
of all variabl
es, the
r
e a
r
e
6 types
of q
u
a
litative varia
b
les th
at nee
d to be
quantitative conversion
by
usin
g the A
H
P metho
d
(Analytical
Hi
era
r
chy Pro
c
ess), this
me
thod
use
d
in
Mea
s
uri
ng Info
rm
ation Se
cu
rity Awar
ene
ss of Indo
ne
sia
n
Smartp
hon
e Use
r
s [9]. The
followin
g
Tabl
e 2 descri
b
e
s
the results d
egre
e
of levelling.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Sim
u
lation and Im
plem
enta
t
ion Model of Produ
ct
ivit
y Measurem
ent Internet… (Tj
ahjanto
)
1073
Figure 4. Application Me
as
urem
ent in the Network
Table 2. Leve
l
Variable
with AHP
Variabl
e
Ty
p
e
Variable
Sy
mb
ol
Variable
Name
Variable
C
ont
en
t
1 2
3
4
5
6
7
8
9
V1 Qualitative
U
User
Level
Professional
Lecturer
U
Student
Student
Toddle
r
9
7
5
3
1
0.3600
0.3600
0.2800
0.2000
0.1200
0.0400
V2 Qualitative
T
Time
Slot
Productive
Ti
me
Rest
Ti
me
Free
Ti
me
9
7
5
3
1
0.2800
0.4286
0.3333
0.2381
V4 Qualitative
G
Goal
Business
Work
Stud
y
Game
9
7
5
3
1
0.2000
0.3750
0.2917
0.2083
0.1250
V5 Qualitative
C
Content
Business
Educatio
n Ne
w
s
Game
9
7
5
3
1
0.1200
0.3750
0.2917
0.2083
0.1250
V6 Qualitative
P
Place
Office
Campus
School
Home
9
7
5
3
1
0.0400
0.3750
0.2917
0.2083
0.1250
V11 Qualitative
A
Application
Bro
w
ser
Email
Chatting
FTP
P2P
9
7
5
3
1
-
-
-
-
-
V12 Qualitative
Ti
Industr
y
Office
Educatio
n Factor
y
Foundatio
n
9
7
5
3
1
-
-
-
-
-
Applicatio
n variabl
e (V11,
A) do not levelli
ng process, these va
riabl
es a
r
e
ignored
becau
se all i
n
ternet a
ppli
c
ation
s
can
be ru
n th
rou
gh
a web browser, while a
variabl
e
of
type
Industry (V1
2
, Ti) levelling pro
c
e
ss is not done
well, as indu
stry type variable will only be
sele
cted o
ne
cou
r
se , nam
ely when
sele
cting mea
s
u
r
ement unive
rse.
In Table 2 col
u
mn thre
e, AHP cal
c
ulatio
n re
sults sho
w
ed that the variable
s
U:
User ha
s
the high
est v
a
lue of
0.360
0 co
mpa
r
ed
to the oth
e
r v
a
riabl
es are
su
ccessi
vely
var
i
able T: Time
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Vol. 13, No. 3, September 20
15 : 1069 – 10
78
1074
Allotment = 0
.
2800, G: De
stinati
on
= 0.200, C: Cont
ent = 0.1200,
and P: Points = 0.04
00, which
mean
s that the influen
ce of
the variable
U:
User g
r
eat
est co
mpa
r
ed
to other varia
b
les.
In Table
2 Li
n
e
V1 i
s
Use
r
Level, of the
re
sult
s of A
H
P cal
c
ulatio
n
shows P
r
ofe
s
sional
s have
level value = 0.3600
comp
ared T
e
a
c
he
rs value
= 0.2
800, Student = 0.
200
0, Student = 0.1
2
0
0
and Tod
d
le
rs = 0.0400, which me
an
s that profe
s
sio
nals a
r
e mo
re prod
uctive
whe
n
com
p
a
r
ed
with
stude
nts. Levelling Al
so i
s
happ
en
for the val
ue
of the n
e
xt line V2, V3, V4,
V5, and
V6 i
n
Table 2. That’
s
Shows in th
e conte
n
t of the variabl
e.
4.2.
Effec
t
iv
e Fa
ctor (Output
Factor)
As
con
c
lud
e
d
in the
previo
us
study [1], t
hat
produ
ctivity is the
effectiveness of th
e task
divided by the efficien
cy of resou
r
ce u
s
e and mult
ipli
ed by the qu
ality of the proce
s
s, whi
c
h
can
be ab
breviat
ed effectiven
ess divided
efficien
cy
mu
ltiplied by the quality fact
or. He
re
are
the
results of the
simulatio
n
e
ffectiveness
con
s
i
s
ting
of
variable
U: User, G: Go
als, P: Place
,
T:
Time, C: Con
t
ent, F: Specification, B: Band
wi
dth an
d
A: Application, in the table below.
Table 3. Effective Facto
r
(Output Fa
ctor)
Col No
1 2
3 4
5
6
7
8
9
Unit
Level
Level
Level
Slot
G
H
z
MBPS
Level
Desc. User
Goal
Place
Time
Content
Spec.
Band
w
i
dth
Application
Var.
U
G
P
T C F
B
A
Ou
tpu
t
Xo1
Xo2
Xo3
Xo4
Xo5
Xo6
Xo7
Xo8
Yo
X1
X2 X3 X4
X5
X6
X7
X8
Y
Max
9 9
9 9
9
4
8
1
1889568
7 7
7 7
7
3
4
1
201684
5 5
5 5
5
2.4
2
1
15000
3 3
3 3
3
2
1
1
486
2 2
2 2
2
1.2
0.5
1
19.2
Min
1 1
1 1
1
0.8
0.25
1
0.2
Ʃ
U
Ʃ
G
Ʃ
p
Ʃ
T
Ʃ
C
Ʃ
F
Ʃ
B
Ʃ
A
Ʃ
O
27.00
27.00
27.00
27.00
27.00
13.40
15.75
6.00
2106757.40
The figure in
Table 3 a
bov
e sho
w
s the
maxi
mum an
d minimum v
a
lue
s
of the output of
all varia
b
le fa
ctors th
at infl
uen
ce it.
Whil
e the
num
bers in
colum
n
8
Application v
a
riabl
es a
r
e
all
= 1, beca
u
se
the variable A: Application is neglig
i
b
le, since alm
o
st all Internet
applicatio
ns
can
be run o
n
the
browse
r ap
pl
ication.
4.3.
Efficiency
Factor (Input Factor)
Efficiency fa
ctor or i
nput
consi
s
ts
of a vari
abl
e V: Volume, D:
Dura
tion, Fp: Spe
c
ificatio
n
Co
st, Up: User Co
st, and
Ap: Applicatio
n Co
st.
The table b
e
l
ow is the
re
sult of simul
a
tion
Efficien
cy Facto
r
of
produ
ctivity, of th
e
maximum val
ue to the minimum value t
hat will ha
ppen, except
column 15
Application Cost,
all
value = 1 for
Applicatio
n Cost igno
red b
y
the
same re
aso
n
as that
of the output factor.
Table 4. Efficiency Fa
ctor
(Input Fa
ctor)
Col
No
10
11
12 13
14 15
16
Unit
10MB
Hour
Factor
Factor
Factor
Factor
Desc.
Volume
Duration
Spec. Cost
Band
w
i
dth Cost
User Cost
Application Cost
Var.
V t
(t0
-t
n)
Fp
Bp
Up
A
p
Inpu
t
Xi1
Xi2
Xi3 Xi4
Xi5 Xi6
Yi
X1 X2
X3
X4
X5
X6
Y
Max
10 4
10
10
5
1
20000
7.5
3
8 8
4 1
5760
5.0
2
6.5
4
3
1
780
2.5
1
4.5
2
2
1
45
1.5
0.5
3.5
1
1
1
2.625
Min
0.5 0.25
3
0.5
0.5
1
0.09375
Ʃ
V
Ʃ
t0-tn
Ʃ
Fp
Ʃ
Bp
Ʃ
Up
Ʃ
Ap
Ʃ
I
270.00
10.75
37.50
27.50
15.50
6.00
26587.72
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TELKOM
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930
Sim
u
lation and Im
plem
enta
t
ion Model of Produ
ct
ivit
y Measurem
ent Internet… (Tj
ahjanto
)
1075
4.4. Qualit
y
Factor
The q
uality fa
ctor
produ
ctivity measu
r
em
ent co
n
s
ist
s
of variabl
es that facto
r
Co
nne
ction
Relia
bility ind
e
x availability of conn
ectio
n
s
and
ban
d
w
idth
Utility is the in
dex of
band
width
usage
5. The followi
ng tabl
e describes the
sim
u
lation
of
all t
he possibilities that
will be obtained
in
the
measurement
.
Table 5. Qual
ity Factor
Colom Numbe
r
17
18
19
Unit
Index Index
Index
Description
Reliability
Conne
ction
Band
w
i
dth Utilit
y
Variable
Rc Bu
Quali
t
y
Xq1
Xq2
Yq
X1 X2
Y
Max 1
1
1
0.9
0.9
0.81
0.8
0.8
0.64
0.7
0.7
0.49
0.6
0.6
0.36
Min 0.5
0.5
0.25
Ʃ
Rc
Ʃ
bu
Ʃ
Q
4.50
4.50
3.55
The Tabl
e 5 above al
so e
x
plains that the qualit
y of prod
uctivity is determin
ed
by factors
Reliability and Bandwidth Connec
tion Utility, visible when
both
factors are decli
ning,
dri
v
e
quality also d
e
crea
sed. Th
at chan
ge in the
quality factor occu
rs in
a linear fa
shi
on.
4.5.
Maximal and
Minimal Pro
ductiv
i
t
y
Score
The follo
wing
table describ
ed the maxim
u
m and mini
mum mea
s
u
r
ement of pro
ductivity
measurement
result
s of the
internet ban
d
w
idth u
s
ag
e.
Table 6. Prod
uctivity
Colom Numbe
r
20
21
22
Unit
%
Index
PxQ
P
Variable
P
P1
P2
Y%
Y1 Y2
Max 100.00
94.48
94.48
30.02
28.36
35.01
13.03
12.31
19.23
5.60
5.29
10.80
2.79
2.63
7.31
Min 0.56
0.53
2.13
Ʃ
P
Ʃ
P1
Ʃ
P2
152.00
143.61
168.97
5.
Analy
s
is of Measur
e
men
t
5.1.
Input An
aly
s
is on Produc
tiv
i
t
y
The g
r
a
ph in
Figu
re 5
do
es
not in
clud
e the va
riabl
e form
ed by
the A: Appli
c
ation
,
becau
se any appli
c
ation can be exec
uted by the bro
w
ser an
d the sca
le of the highe
st value
of
all variabl
es i
s
10. P
= Produ
ctivity Actual value
m
a
y declin
e an
d
risin
g
to the
highe
st level,
is
influen
ced
b
y
variable
s
input. Thi
s
sho
w
s the
cha
nge
of the inp
u
t va
riable
s
affect
ing
prod
uctivity.
Figure 5 also
explains that
t
he produ
ctivity will decrea
s
e sig
n
ifica
n
tly when there
is little
cha
nge in th
e entire vari
a
b
le input (e
speci
a
lly vari
a
b
le U, G, P,
T, C). Variabl
e F: Specification
doe
s not hav
e to incre
a
se to get maximum pro
d
u
c
tivity value.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
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TELKOM
NIKA
Vol. 13, No. 3, September 20
15 : 1069 – 10
78
1076
Figure 5. Input Grap
h Ana
l
ysis on Prod
uctivity
5.2.
Outpu
t
An
al
y
s
is on Productiv
i
t
y
Figure 6 po
rtray the gra
p
h
chan
ge
s in t
he value of p
r
odu
ctivity depend
s on the
output
variable. G
r
a
ph forme
d
wi
thout variabl
e Ap: Application Co
st, beca
u
se usin
g
only a browser
appli
c
ation, a
ll appli
c
ation
s
ca
n be
ru
n i
n
ternet. G
r
a
p
h
ic
scaled
to
scale the
hig
hest valu
e of
all
va
r
i
a
b
l
es
is
10
.
Figure 6. Output Gra
ph An
alysis o
n
Pro
ductivity
In the Fig
u
re
6 sho
w
s the
output
cha
n
g
e
s
affect
p
r
od
uctivity. if observed, that
th
e value
of pro
d
u
c
tivity will de
crea
se
signifi
cant
ly when
there is little
cha
nge in
the o
u
tput varia
b
l
e
s,
esp
e
ci
ally the variable
s
V, FP and BP, while t
he
va
ri
able D:
Du
rat
i
on
and Up: User Co
st
Fa
ctor,
doe
s not nee
d to be high to get maximu
m prod
uctivity value.
5.3.
Factor An
aly
s
is Outpu
t
a
nd Input Fac
t
ors on Prod
uctiv
i
t
y
Figure 7 portray chan
ge
s in the value of
P:
Productivity varies from mini
mum to
maximum in
line with
chang
es in t
he varia
b
le
s that influen
ce. From th
e gra
phi
c b
e
low
prod
uctivity value chan
ge i
s
not linea
r wi
th chan
ge
s in
variable
s
tha
t
affect it.
This
can b
e
seen in Fig
u
re
7 that are no
t r
equired variable F: Spe
c
i
f
ication, D: Duration
,
and
Up:
Use
r
Co
st F
a
cto
r
to obtain
max
i
mum p
r
o
duct
i
vity value. Even vari
able
B: Bandwi
d
th
is
also n
o
nee
d
to be at the
maximum value to obtai
n
high produ
cti
v
ity
values. It is said that
only
the maximu
m ban
dwi
d
th
doe
s
not g
u
a
rante
e
the
value of th
e
prod
uctivity o
f
the u
s
e
of
the
internet will be a maximum
.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Sim
u
lation and Im
plem
enta
t
ion Model of Produ
ct
ivit
y Measurem
ent Internet… (Tj
ahjanto
)
1077
Figure 7. Output Gra
ph An
alysis o
n
Pro
ductivity
5.4.
Value Analy
s
is Producti
v
i
t
y
From Fi
gure
8 graph, the
more
it appears that the val
ue of
P =
productivity will decrease
with the decrease of the value of ot
h
e
r
v
a
r
i
a
b
l
e
s
(
U
,
G
,
p
,
T
,
C
,
F
,
B
,
V
,
D
,
F
p
,
B
p
,
U
p
)
,
s
o
a
l
s
o
on the contrary, the value
of P = P
r
oductivity will
ri
se in line
with the in
crease in the variables
that influence
it.
Ho
wever i
m
p
a
irme
nt P = P
r
odu
ctivity is not as li
nea
r
variable
s
oth
e
r (U, G, p, T,
C, F, B,
V, D, Fp, Bp, Up), ten
d
to g
o
strai
ght do
wn
w
hen th
e
other va
riable
s
are
ju
st starting down. Th
is
sho
w
s that th
e produ
ctivity of Intern
et b
and
widt
h u
s
a
ge is influe
nced by alm
o
st
all supp
ortin
g
variable
s
,
no
t by a
ce
rtai
n vari
able,
a
s
d
e
scribe
d
i
n
the
chart
star ima
g
e
an
alysis ab
ove
is
Figure 5, Figure 6 an
d Fig
u
re 7.
Figure 8. Gra
ph Analysi
s
Proc
ess of Pro
ductivity Decli
n
e
6. Conclu
sion
6.1. Conclu
sion
From
the sim
u
lation re
sult
s with
the
m
easure
m
ent obje
c
t
unive
rsal mea
s
u
r
in
g
ge
neral
(re
gardle
ss of
the type of in
dustry to be
meas
ured), the followi
ng concl
u
si
on
s were obtai
ned:
1.
Measuri
ng th
e prod
uctivity of Inter
net band
width
usa
ge can b
e
done, in
whi
c
h
qualitative va
riable
s
scale
d
by AHP, a
nd can
be
co
mbined
with
quantitative variabl
es,
so t
hat
the mea
s
u
r
in
g re
sult
s obt
ained fo
r all
possibl
e com
positio
n me
a
s
uri
ng valu
e,
whi
c
h
will be
the
basi
s
of the a
c
tual mea
s
u
r
ement, sho
w
n in Table
s
2-6.
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ISSN: 16
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TELKOM
NIKA
Vol. 13, No. 3, September 20
15 : 1069 – 10
78
1078
2.
The m
a
ximu
m input fa
ct
ors do
not
gua
rante
e
that the ma
ximum value
of
productivity woul
d be anyway, so the maximu
m output factor
does
not guarantee will
get
maximum pro
ductivity values, sh
own in Figure
7. The
analysi
s
of input and outp
u
t factors.
3.
To obtai
n ma
ximum produ
ctivity require
d
output
and
input
con
d
itions are o
p
timal,
con
s
id
er Fig
u
r
e 7. The an
a
l
ysis of input-output facto
r
.
4.
That the value of the bandwidth is n
o
t t
he main factor for p
r
od
uctivity produ
ctive
Internet ban
d
w
idth u
s
ag
e.
6.2.
Follo
w
-
up I
m
plementa
tion and Simulation
From the ab
o
v
e con
c
lu
sion
, that the imp
l
ementation a
nd simul
a
tion
have descri
b
ed the
possibilities of measuring values
obtai
ned. And the resu
lts of
this si
mulation will be
the
basi
s of
further re
sea
r
ch,
namely:
1.
The further research
will perform
direct m
e
asurem
ents
on one
or more
orga
nization
s, can
the
com
pany o
r
th
e
campu
s
acade
mic
org
ani
zat
i
ons, i
n
a
c
co
rdan
ce
with th
e
target unive
rse to be measured.
2.
The furthe
r rese
arch will
perfo
rm a co
mparative analysis bet
we
en the re
sult
s of
measuri
ng the simulatio
n
with real me
asu
r
em
ent
re
sults, so it is expected th
at the analysis
results
can b
e
use
d
to improve the qu
ality fact
or produ
ctivity me
asu
r
em
ent Internet ban
dwi
d
t
h
usa
ge.
Referen
ces
[1]
Prami
y
ati T
,
Supri
ana
I, Pur
w
a
r
ia
nti A.
De
termi
nin
g
T
r
ust Scope
Attrib
utes Usi
n
g
Go
odn
ess of F
i
t
T
e
st: A Survey
.
T
E
LKOMNIKA T
e
leco
mmu
n
i
catio
n
Co
mp
uting El
ectronics
and C
ontrol.
2
015; 13(
2)
.
[2] Saari
S.
Pro
d
u
ctivity T
heory
and M
easur
e
m
e
n
t in B
u
sin
e
ss.
Europ
e
a
n
Productiv
i
t
y
Confer
ence.
F
i
nla
nd.
20
06.
[3]
Strassmann
P
A
. Defin
i
n
g
a
nd M
eas
urin
g
Informatio
n
Productiv
i
t
y
.
Ne
w
Ca
na
an,
USA: T
h
e
Information Ec
onom
ics Press
.
2004.
[4]
T
j
ahjanto, Be
nhar
d Sito
ha
n
g
, Sud
a
rso K
ader
i W
i
r
y
on
o
.
F
r
ame
w
ork
Peng
ukur
an
Produktiv
i
tas
Peng
gu
naa
n B
and
w
i
dth Inter
net. SNAKOM, Bandu
ng. 20
1
2
.
[5]
T
j
ahjanto, Be
n
hard S
i
toha
ng,
Sudars
o
Ka
der
i W
i
r
y
ono. Var
i
abe
l Prod
uktivi
tas Peng
gu
naa
n Ban
d
w
i
dth
Internet (Korel
asi de
ng
an vari
abe
l IT
Resour
ces pad
a COBIT
F
r
ame
w
ork).
KNS&I, Bali. 2013.
[6]
T
j
ahjanto, Be
n
hard S
i
toh
ang,
Sudars
o
Ka
der
i W
i
r
y
ono. A S
y
stem D
e
si
gn
of Productiv
i
t
y
Measur
emen
t
Internet Ban
d
w
i
dth Usa
ge. R-ICT
,
Bandun
g. 201
3.
[7]
J Sumanth DJ.
Productivit
y
E
ngi
neer
in
g and
Manag
eme
n
t. McGra
w
-
Hil
l. 1984.
[8]
Gaspersz V. Mana
jeme
n Prod
uktivita
s T
o
tal.
Jakarta: Gramedi
a. 200
0.
[9]
Puspita K
enc
ana S
a
ri, Ca
ndi
w
a
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