TELKOM
NIKA
, Vol.14, No
.4, Dece
mbe
r
2016, pp. 14
46~145
3
ISSN: 1693-6
930,
accredited
A
by DIKTI, De
cree No: 58/DIK
T
I/Kep/2013
DOI
:
10.12928/TELKOMNIKA.v14i4.3505
1446
Re
cei
v
ed Fe
brua
ry 24, 20
16; Re
vised
Augus
t 18, 2
016; Accepte
d
Septem
ber 2, 2016
Water Quality Monitoring with Fuzzy Logic Control
Based on Graphical Programming
Mochammad
Hanna
ts
Ha
nafi Ichsan
*
1
, Wija
y
a
Kurnia
w
a
n
2
, Miftahul Huda
3
1,2,3
Faculty of
Comp
uter Sci
ence,
University of Brawijaya,
Jl. Veteran M
a
lang, East Java, Phone: +62 3
41 55
1
611/ Fax +62
341 565
420
*Co
rre
sp
ondi
ng autho
r, e-ma
il: hana
s.h
anafi@ub.a
c
.id
1
, wjayku
rni
a
@u
b.ac.id
2
,
hudafu
n
ky
@gmail.co
m
3
A
b
st
r
a
ct
W
a
ter qua
lity i
s
the most i
m
portant as
pect
to
ensur
e success in v
a
rious aspects of life, for
exa
m
p
l
e in th
e shri
mp p
onds.
On a shri
mp p
ond, w
a
ter co
n
d
itio
ns are very
vital beca
u
se i
t
has a very strict
thresho
l
d. Uns
t
able w
a
ter co
nditi
ons w
ill
affect grow
th
and
conditi
on
of shri
mps, eat p
a
ssion of shr
i
mps,
until th
eir
abi
lit
y to survive
gr
eatly affect the
surviva
l
of the
shri
mps. T
he
perce
ntag
e of f
a
rmers h
a
rvest
i
ng
shri
mps
if the
w
a
ter did
n
o
t
have
g
ood
co
n
d
itio
ns th
en t
h
e far
m
ers
w
ill
suffer sig
n
ifica
n
t loss
es if
yie
l
ds
w
e
re not
as
ex
pected,
be
ga
n
from t
he
a
m
o
u
n
t of s
h
ri
mp
th
at w
a
s re
duce
d
d
u
e
to
deat
h
or th
e
qu
ality
of
the shri
mp w
e
re ju
dge
d fro
m
the si
z
e
of th
e shri
mp. So the a
u
thors w
a
nted to
do res
earch
on
how
to
ma
inta
in th
e q
uality
of the
w
a
ter in
s
h
ri
mp
p
ond
so th
at the
w
a
ter qu
ality
i
s
mainta
in
ed.
T
o
overc
o
me t
h
is,
w
e
nee
d to
mo
nitor w
a
ter c
o
n
d
itio
ns b
a
se
d
o
n
the
l
e
vel
of
s
a
lin
ity a
n
d
turbi
d
ity of w
a
ter
in
ord
e
r to
stay i
n
goo
d co
nditi
on
. In this case, t
he res
earch
ers
used fu
zz
y
lo
gic to
mo
nitor t
he a
m
ou
nt of w
a
ter qual
ity a
n
d
w
a
ter volu
me. In this study only con
ducte
d w
a
ter qua
lity
mo
nitori
ng pr
o
c
ess but to do w
a
ter chang
es
to a
certain
con
d
iti
o
n still
co
nduct
e
d
ma
nua
lly. As
w
e
ll
as
the
pr
ogra
m
mi
ng
la
n
gua
ge
use
d
as
the
NI La
bVIEW
grap
hica
l pr
ogr
amming
w
i
th the ap
plic
atio
n fo
rm to se
e
mo
ni
toring
of w
a
ter qua
lity so th
at w
a
ter conditi
on
s
are w
e
ll pres
er
ved.
Ke
y
w
ords
: sal
i
nity, turbid
ity, labvi
e
w
,
fu
zz
y
l
ogic co
ntrol, gr
aph
ical pr
ogr
a
m
mi
ng
Copy
right
©
2016 Un
ive
r
sita
s Ah
mad
Dah
l
an
. All rig
h
t
s r
ese
rved
.
1. Introduc
tion
Wate
r is the
most imp
o
rta
n
t thing is in
the
aspe
cts o
f
life, such a
s
hou
sehol
ds,
farms,
pond
s, etc [1]
.
One of the vital water utili
zation
wa
s in
the sh
rimp p
o
nds.
Water q
uality in shri
mp
pond
s
ha
s an
impo
rtant rol
e
dete
r
min
e
t
he
su
ccess
of the
cro
p
[2].
Poor
wate
r q
uality co
nditio
n
s
will g
r
eatly aff
e
ct the
health
of sh
rimp
s
a
nd farm
ers th
reaten
ed
with
crop fail
ure
e
v
en if the p
o
n
d
water too
cl
e
an, shrim
p
s
will n
o
t g
r
o
w
well. Som
e
o
f
the fa
ctors t
hat affect
the
quality of
wa
ter
turbidity an
d
salinity p
ond
s. Several fa
ct
ors a
s
determinants of
water
quality
shrimp
s
(Van
n
a
mei
types fo
r exa
m
ple) is the
l
e
vel of
salinit
y of 19
-25
pp
t (pa
r
t p
e
r tho
u
sa
nd),
while
for th
e tu
rbid
ity
of 3-15 ppt. So the value of salinity and
water tu
rbidit
y should rem
a
in bala
n
ce
d.
Wate
r that h
a
s lo
w salinit
y was d
ang
e
r
ou
s
be
ca
use it will decr
ease the oxygen that
woul
d
cau
s
e
the thi
n
-cru
sted
sh
rimp
s, it usually
o
c
curs
duri
n
g
the
rainy
se
aso
n
d
u
e
to
the
increa
se i
n
water flo
w
du
e
to the rain t
hat ha
s a
hig
h
a
c
id
conte
n
t. While
salini
t
y was to
o hi
gh
will reduce t
heir
growth,
this
usually
occurs
duri
n
g the dry se
ason [3]. As shrinking
water
discha
rge
bu
t the am
ount
of cl
ay, org
anic [2] an
d
inorg
ani
c
co
mpoun
ds,
pl
ankto
n a
nd
o
t
her
mic
r
oorganisms
s
t
rongly s
u
s
p
ec
ted
as the c
a
use
of the water turbidity [4]. Turbidity of pond
water g
r
eatly
affect the
gro
w
th
sh
rimp
s,
becau
se if
th
e water is not
turbi
d
, the
su
nlight a
b
sorb
ed
in
th
e wa
te
r
w
o
u
l
d
be
to
o
mu
c
h
. T
h
is
w
i
ll c
a
u
s
e
th
e
sh
r
i
mp gr
ow
th
w
a
s
no
t op
tima
l, a
l
th
ou
g
h
th
e
shri
mp even t
houg
h plan
kt
on nee
d su
nli
ght for gro
w
th
[5].
Previou
s
re
search
condu
cted [6] o
n
the
use
of
NI
La
bVIEW to
de
sign
in
strum
e
ntation,
this re
se
arch
only con
d
u
c
ted for the in
st
rume
ntat
ion control
system
, but no algo
rithm / intelligent
sy
st
em
s in
t
h
is
st
udy
.
Whil
e f
u
rthe
r re
search co
ndu
cted
[7]
wa
s
f
u
zzy logi
c im
plementatio
n
in
LabVIEW, b
u
t
wa
s not i
m
p
l
emented
dire
ctly in
ha
rd
ware, b
u
t only
on
simulatio
n
for
DC moto
rs
only. Meanwhile, accordin
g to Xie [5]
very impor
ta
nt to maintain the quality of the water in
shri
mp pon
ds. The fuzzy lo
gic u
s
ed to control t
he in
strume
nts directly through the excha
nge o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Wate
r Qualit
y Monitorin
g
with Fuzzy L
o
g
i
c Co
ntrol…
(Moch
a
m
m
ad
Han
nats
Han
a
fi Ichsa
n
)
1447
data a
nd
co
mmand
s
bet
wee
n
inp
u
t a
nd out
put [8] as
wa
s
don
e well in
pre
v
ious
studie
s
to
monitor the irrigation
cha
n
nel.
Therefore
it i
s
n
e
cessa
r
y
a sy
stem that
ca
n mo
nitor
water conditi
ons in
real
time so that
the farme
r
/user can monit
o
r wate
r qu
ali
t
y at any
mo
ment and
ca
n repla
c
e o
r
add water so
that
water
quality
maintained.
The develo
p
ment of this
syste
m
will
be co
ndu
ct
ed in the form of
hard
w
a
r
e
an
d software m
onitorin
g
wat
e
r q
uality u
s
i
n
g fu
zzy l
ogi
c
control b
a
sed o
n
the
level of
salinity an
d
water tu
rbidity
pond.
Ha
rd
ware th
at w
ill b
e
u
s
ed wa
s sensor sali
nity
(salt), sen
s
o
r
s
turbidity (turbid), Ard
u
ino
Mega mi
croco
n
trolle
r a
nd LED i
ndi
cators
woul
d
implement
with
LabVIEW.
2. Rese
arch
Metho
d
This
se
ction
descri
b
e
s
the
method
s an
d
hard
w
a
r
e
re
sea
r
ch p
r
o
c
e
dure
s
. How t
o
use the
sen
s
o
r
ne
ed
s and de
sig
n
of fuzzy logi
c. Descri
pt
ion
of need
s an
d
their refe
re
n
c
e
s
so that the
flow explan
ation of the syst
em t
hat can b
e
use
d
in Fig
u
re 1.
Figure 1. Hardwa
re Blo
ck
Diag
ram
This
system
will be mad
e
in the form of
a pr
ototype
of a pool of water, wh
ere t
he wate
r
pool
s
will b
e
given
two
sensors, n
a
m
e
ly sali
nity
sensor an
d tu
rbidity
sen
s
o
r
. T
w
o
of th
ese
sen
s
o
r
s will
provide
an
o
u
tput voltage
on th
e mi
croco
n
trolle
r.
Microcontroll
e
r u
s
e
d
i
s
Arduin
o
Mega. After
Arduin
o
get
s
the data from
the sen
s
or
, t
hen h
e
will
transmit th
e da
ta to monito
ri
ng
appli
c
ation
s
that have be
e
n
creat
ed
usi
ng LabVIE
W. In su
ch ap
pl
ication
s
, the
data of sali
ni
ty
sen
s
o
r
s and
sen
s
o
r
turbidi
t
y will be p
r
o
c
e
s
sed u
s
in
g
fuzzy lo
gic.
So that the water q
uality d
a
ta
can
be m
onit
o
red
qu
ality. On the
appli
c
ation
of
dat
a obtain
e
d i
n
real
-time
so
that any
slig
ht
cha
nge in
wa
ter con
d
ition
s
, will be read i
n
the monitori
ng syste
m
.
2.1. Sensor
Salinity and t
u
rbidity
sen
s
ors u
s
ed
by t
he
research
e
r
s gen
erate
d
data su
ch
a
s
voltage
output
with a
rang
e of
0-5
V
. While th
e
requi
re
d dat
a
input fo
rm from the
se
nso
r
is conve
r
ted
in
the form
of Part Per Tho
u
s
an
d (ppt) to
salinity
a
nd Nep
helom
etri
c
Tu
rbidity Unit
(NTU) with
a
rang
e of 0
-
5
0ppt an
d 0
-
5
0ntu. To
overco
me th
i
s
p
r
oblem
re
sea
r
che
r
s ap
ply Analog to
Di
gital
Conve
r
si
on (ADC) [5, 9] as found in
Figure
2
.
Based o
n
Figure
2
, the data from
the salinity
sen
s
o
r
0ppt
1,7v as defa
u
lt and 3.7V as the
maximum val
ue 50
ppt after
conve
r
si
o
n
. While tu
rb
idity sen
s
or
prod
uces
0,3
v
as defa
ult 0ntu
and 1,3v maximum value 5
n
tu.
2.2. Fuzzy
Logic Con
t
rol
To co
ntrol th
e quality of water
nee
de
d two
sen
s
o
r
data that ha
s be
en obtai
ned in th
e
form
Analo
g
to
Digital Co
nversi
on (ADC),
then
Me
mbershi
p
Fu
nction
and
Rule Evaluatio
n
of
Fuzzy Logi
c
will be d
e
si
g
ned in L
abVI
E
W. Fuzzy
l
ogic type
used by re
se
arche
r
s i
s
the
kind
Center of Area (CoA) [10]. This
function
will determine the condit
ion of the water
because water
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 4, Dece
mb
er 201
6 : 1446 – 145
3
1448
con
d
ition
s
a
very vital in
many a
s
pe
ct
s [11]
A little
wate
r condit
i
ons
wa
s
not
pre
c
i
s
e
will
be
harmful to living things in their environ
ment. T
heref
ore, Fu
zzy m
odel
s use
d
b
y
resea
r
che
r
s is
the Gau
ssia
n
model. The membe
r
ship functio
n
s u
s
e
d
con
s
iste
d o
f
seven mem
bership fun
c
ti
on
for each inpu
t data. Fuzzy
logic ca
n be
applied to
gi
ve a specifi
c
value co
rre
sp
ondin
g
[12, 13],
so in this a
ppl
ication p
r
ovid
ed a feat
ure t
o
edit the membershi
p
fun
c
tion
s.
Figure 2. ADC Salinity and Turbi
d
ity Sensors
Users can edit the membershi
p
function by filling the array
contai
ned in the left colum
n
in Figure 3. Mean
while, if you want to cha
nge t
he
model of Fu
zzy to which the user
can
pre
ss
the button switch "M
odel"
whi
c
h lo
cate
d at the t
op l
e
ft of the applicatio
n. "Model" is availa
bl
e
here fo
r mo
d
e
ls fro
m
MF
example g
a
u
ssi
an, trap
ezoid or t
r
ian
g
l
e
. In the left colum
n
can b
e
put
rest
rictio
ns in
to direct valu
e of membership fun
c
tion,
fuzzy logi
c function
ality is alrea
d
y provi
ded
by LabVIEW so ea
sy to implement.
Figure 3. Fuzzy Logi
c Me
mbershi
p
Fun
c
tion
2.3. Analy
s
is
Method
s
Once the de
sign of ha
rd
ware and int
e
lligent sy
ste
m
s de
sign
ed
, the next step is to
desi
gn
the sy
stem softwa
r
e.
System
int
e
rface p
r
ovid
ed several
fe
ature
s
, amo
n
g
othe
rs, the
first
is the
n
u
mb
er
of the
me
mbershi
p
fun
c
tion li
ke
in
Figure 4.
On
the m
e
mb
ership
nu
mbe
r
is
provide
d
3, 5
and 7, whi
c
h se
rves to
determi
ne
if the preci
s
ion
of the memb
ership which
is
own
ed by the water lo
cat
ed in the left column of t
h
e interfa
c
e.
The gre
a
ter the numbe
r of
membe
r
ship
function u
s
ed, it incre
a
s
ingly detail
s
the value of the mem
bership fu
nct
i
on
pro
c
e
s
sed by
fuzzy lo
gic.
Then th
e second fun
c
tion
i
s
the
re
sult o
f
sen
s
o
r
re
ad
ings
and
outp
u
t
fuzzy logi
c lo
cated o
n
the right colum
n
o
f
applicatio
ns.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Wate
r Qualit
y Monitorin
g
with Fuzzy L
o
g
i
c Co
ntrol…
(Moch
a
m
m
ad
Han
nats
Han
a
fi Ichsa
n
)
1449
Figure 4. Application of Po
nd Wate
r Qu
ality Control
Based
on Fig
u
re 4 the
bottom right u
s
e
d
to calib
rate the se
nsor. In
the pictu
r
e, th
ere a
r
e
4 circle
s, two
circle
s u
s
ed
to calib
rate th
e sali
nity of the sen
s
or
an
d then b
e
u
s
ed for
calib
rat
i
on
of turbidity sensors. So if performed i
n
a swimmi
n
g
pool di
spla
ceme
nt small
e
r or la
rg
er, i
t
is
possibl
e agai
n to re
calib
rat
e
. With this fu
nction
alit
y will provide
a lot of additional f
eature
s
fo
r th
e
system to wo
rk bette
r and
can b
e
imple
m
ented on th
e con
d
ition of
different pool
s.
2.4. ADC Sensor Calibra
tion
Each
se
nsor
calib
ration
ADC, to
en
su
re w
h
e
t
h
e
r
th
e d
a
t
a
ob
ta
in
ed
in
acc
o
r
d
anc
e
w
i
th
a
desi
gn that h
a
s be
en creat
ed. Based in
Figure
5
sho
w
s
t
h
e
A
D
C calib
rat
i
on sa
linit
y
sen
s
or prod
uces an output
voltag
e
1.5v
default. For
salinity se
nsor re
qui
re
s a
long time,
b
e
ca
use of the influen
c
e o
f
the water t
hat
soa
k
e
d
the foot sen
s
o
r
while the p
o
o
l so la
r
ge t
hat it cau
s
e
the readi
ng
s less tha
n
the
maximum.
Figure 5. Salinity Sensor
Calibratio
n
ADC
From Ta
ble 1
,
shows the result
s of ADC in
acco
rd
a
n
ce
with the
desi
gn of the
system,
but the time
for the o
u
tpu
t
of the se
nsor
stabl
e l
o
n
g
eno
ugh
sal
i
nity. To test
seven
sa
mpl
e
s
requi
re
s 323
se
con
d
s o
r
less tha
n
five
minutes. Mea
n
whil
e, the turbidity sen
s
o
r
calibration tend
s
to be mo
re
a
ppro
p
ri
ate th
an the
salinit
y sen
s
o
r
, althoug
h the d
e
f
ault con
d
itio
n se
nsor
abl
e
to
cha
nge
d ea
si
ly.
There a
r
e in
Figure 6
sho
w
s th
e results of the tu
rbi
d
ity sen
s
o
r
calibratio
n
ADC p
r
od
u
c
e
output voltag
e 0,18v defau
lt.
Table
2
, the tes
t
results
of the turbidity s
e
ns
or
that
prod
uce different ADC values in
accordan
ce
with the
ri
se
in the
output
voltage fr
om
the sen
s
o
r
. While
the sta
b
le
time of
t
he
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 4, Dece
mb
er 201
6 : 1446 – 145
3
1450
turbidity sensor indi
cate
s t
he total nu
m
ber
of
only 7
8
se
co
nd
s o
r
one mi
nute
by seven te
st
sampl
e
s.
Table 1. ADC Salinity Sensor Cali
bration
Results
No.
V Default (v)
Sensor Voltage (v)
ADC Salt (ppt)
Stable Period (t)
1 1,5
1,51
0,35
41
2 1,5
2,12
15,78
45
3 1,5
2,23
18,48
47
4 1,5
2,97
36,85
51
5 1,5
3,11
40,41
45
6 1,5
3,27
44,45
50
7 1,5
3,33
45,92
44
Total 323
Figure 6. ADC Turbidity Sensor Calib
ra
tion
Table 2. Re
sults of ADC
Calibratio
n
Turbidity Senso
r
No
V Default (v)
Sensor Voltage (v)
ADC Salt (NT
U
)
Stable Period (t)
1 0,18
0,18
0
11
2 0,18
0,21
1,53
12
3 0,18
0,31
6,68
11
4 0,18
0,53
17,70
10
5 0,18
0,70
26,03
13
6 0,18
0,88
35,61
10
7 0,18
1,03
41,60
11
Total 78
3. Result a
n
d Analy
s
is
Based
on the
results of th
e desi
gn to i
m
pleme
n
tatio
n
, testing for
sen
s
o
r
calibration ha
s
been do
ne b
u
t have not been teste
d
to perform te
st
ing of fuzzy logic that ha
s been de
sign
ed.
The p
u
rpo
s
e
of this te
st i
s
to asse
ss the
level of
su
cc
es
s,
f
a
ilur
e
,
a
nd d
e
f
i
cien
cie
s
of
t
h
e
sy
st
e
m
that has be
en
desig
ned.
3.1. Pond Water Q
u
alit
y
Con
t
rol
In this test
scen
ario, te
sting was
co
nd
uct
ed
by co
mpari
ng the
results of
wa
ter quality
throug
h LED i
ndicator
software an
d ha
rdwa
re after
water conditio
n
s
dee
med to
be re
pla
c
ed
wa
s
con
d
itioned i
n
accordan
ce
with the cont
rol output
of the system. Error obtai
ned f
r
om the sche
me
is not g
r
eat
o
n
ly 0.62 only
can
be
see
n
i
n
Table
3.
Error results o
b
tained from te
sting the
se
n
s
or
inputs to the
membe
r
ship functio
n
that has be
en mad
e
.
Table 3. Error Output Mode
l Fuzzy
No PPT NTU
m0
m1
m2
e
1
5,8
9,2
70,1 70,1 70,1
0
2
11,7 13,8 39,8
39,8
39,8
0
3
16,8 18,9 40,7
40,7
40,7
0
4
21,8 25,2 70,1
70,1
70,1
0
5
28,1 31,5 81,7
81,9
84,8
3,1
Average
0,62
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Wate
r Qualit
y Monitorin
g
with Fuzzy L
o
g
i
c Co
ntrol…
(Moch
a
m
m
ad
Han
nats
Han
a
fi Ichsa
n
)
1451
The n
e
xt sta
ge of the te
st
scena
rio
wa
s to
d
e
termi
n
e
the sen
s
o
r
data.
Sen
s
or data
a
r
e
given at
ran
dom o
r
ran
d
o
m until
the
LED in
di
cato
r light
s
up i
n
the th
ree
condition
s "Ba
d
",
"Enough", an
d "Good" to see the re
sult
s of the monito
ring of ea
ch
of these co
n
d
itions. The t
o
tal
volume of water that must be
replaced will appear at
the out
p
ut of the monitoring syst
em.
Becau
s
e thi
s
study only monitorin
g
, wat
e
r ch
ang
e pe
rforme
d man
ually.
Based
on th
e
test re
sult
s i
n
Tabl
e 4, th
e wh
ole exp
e
r
iment p
r
od
uces g
ood
wate
r qu
ality,
althoug
h the
percenta
g
e
o
f
the in
put o
r
volume
of
wa
ter e
a
ch a
dif
f
erent m
e
mb
ership. In
test
s,
the perce
ntag
e or volume o
f
water affect
s the l
ength
o
f
time on testing. The worse the quality of
the water, th
e
long
er du
rati
on
requi
re
d t
o
chan
ge
th
e
wate
r
until th
e water of g
o
od q
u
ality. Here
is p
hotog
rap
h
i
c evide
n
ce o
f
poor qu
ality wate
r te
st
st
art from
wate
r in
critical
co
ndition u
n
til the
water in g
ood
conditio
n
for shri
mp po
nd.
Table 4. Water Quality Te
st Re
sults
No p0
p1
a0
volume
h0
h1
a1
s
1
4,7
46,6
Bad
96 3,9 41,4 2,7
Good
357
2
34,8
24,2
Enough
39 1,6 37,3 5,9
Good
60
3
39,7
3,0
Good
24 1,0 40,9 2,0
Good
48
Time Total (s)
465
The a
c
tual
water
con
d
ition
s
can
be
see
n
Figu
re
7, while in th
e p
r
ogra
m
inte
rfa
c
e, the
measurement
re
sults a
r
e
containe
d in
F
i
gure
8.
in
th
e imag
e
sho
w
s that the
water conditi
ons
"Bad". At the
top ri
ght
colu
mn
sho
w
s th
e nu
mbe
r
of
liters of
wate
r t
hat mu
st
b
e
repla
c
ed
in
order
to be good o
r
incomin
g
wat
e
r co
ndition
s
"Good" or at t
he ce
nter of the image.
Figure 7. Wat
e
r Co
ndition
Before contro
lled
Figure 8. Wat
e
r Co
ndition I
n
Moni
tori
ng
Systems Bef
o
re controlled
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 4, Dece
mb
er 201
6 : 1446 – 145
3
1452
The initial co
ndition of water in Fig
u
re
8, the water
con
d
ition
s
be
fore controll
e
d
"Bad",
the color loo
k
s very inte
n
s
e
wate
r, thu
s
fall i
n
to
the
cate
gory
of
"bad". In a
d
d
i
tion to the
L
E
D
indicator li
ght
s u
p
o
n
the
l
e
ft side
of th
e ap
plication,
whi
c
h
mea
n
s
p
oor water co
ndition
s
a
n
d
sho
u
ld b
e
re
placed u
n
til the
water qu
a
lity to be
go
o
d
. Figu
re 8
is a representa
t
ion of the
da
ta
pro
c
e
ssi
ng sy
stem taken from the wate
r in Fi
gure 7 is
see
n
that the water ve
ry turbid.
The
con
d
ition
of the
wate
r
after is re
pla
c
ed
o
r
sup
p
le
mented with other wate
r condition
s
in Figu
re
9, t
hen te
sting
the
system. T
he
water
con
d
itions after
controlle
d
well
, the color lo
oks
on the dark water be
come
s clearer than t
he wate
r co
n
d
ition in Figure 8. In addition to monitorin
g
the appli
c
atio
n, sho
w
in
creased
salt a
nd lo
wer
ppt
NT
U turbi
d
ity which indi
cates that
wat
e
r
con
d
ition
s
are better tha
n
ever. Black
LED indi
ca
to
r lights u
p
on
the appli
c
atio
n, whi
c
h me
a
n
s
good
water
condition
s hav
e cha
nge
d like in Figure 10
.
Figure 9. Wat
e
r Co
ndition
after Cont
roll
ed
Figure 10. Water Indicator
In Monitorin
g
Systems After Cont
rolle
d
The
upp
er l
e
ft colum
n
in
Figure 1
0
, bl
ack
colo
re
d i
ndicators m
o
ve to the
in
dicato
r
"Good"
whi
c
h
mean
s i
ndi
cates th
at the
water qu
ality
is g
ood. At th
e top
ce
nter
of the ima
g
e
also
sho
w
s the
co
ndition
of the
water that
mu
st be
repla
c
ed
sma
ller
th
an
in F
i
g
u
r
e
8
.
T
h
is
s
h
ows
th
a
t
the system h
a
s be
en ru
n in accordan
ce
with what wa
s expe
cted e
a
rlie
r.
4. Conclusio
n
This
syste
m
has mad
e
a
g
ood
com
m
uni
cation
bet
we
en the
se
nsor, microcontrol
lers an
d
LabVie
w working i
n
a
c
co
rdan
ce
with
the wi
sh
e
s
.
LabVIEW p
r
ovides fe
atures fu
zzy lo
g
i
c
desi
gne
rs fai
r
ly easy to ap
plied by the u
s
er. But fewe
r pro
b
lem
s
wi
th sen
s
o
r
be
cause the wat
e
r
input doe
s no
t directly read
fast, it takes
a long ti
me to read with ap
prop
riate water quality. Thi
s
relate
s to
the
pro
c
e
s
s of mi
xing water int
a
ke
when
giv
en a
pa
rticula
r
in
put q
uality wate
r
and
wait
for the wate
r and the water feedba
ck fro
m
the system
.
For futu
re
re
search
co
uld
b
e
ad
ded to
thi
s
sy
stem i
n
t
h
e
control
system. Mixing
water is
not performe
d
manually b
u
t using a mo
tor that can
g
e
t in the flow
of water acco
rding to nee
d
so
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Wate
r Qualit
y Monitorin
g
with Fuzzy L
o
g
i
c Co
ntrol…
(Moch
a
m
m
ad
Han
nats
Han
a
fi Ichsa
n
)
1453
that no manu
al pro
c
e
s
s. as well
as
wat
e
r mi
xing p
r
o
c
e
ss
can
be
done m
o
re
q
u
ickly if adde
d to
accele
rate th
e p
r
o
c
ess of
mixing water.
In ad
diti
on, f
a
ctors that af
fect the
wate
r q
uality of th
e
pond th
ere
a
r
e two, name
l
y salinity an
d turbidity,
so there
are two in
put flow of wate
r to th
e
cont
r
o
l sy
st
e
m
.
Referen
ces
[1]
Bhan
dari V, Ab
rol P. F
i
eld Mo
nito
ri
ng of T
r
eated Industria
l W
a
ste W
a
ter.
IJECE.
2013; 3:
629-6
34.
[2]
Bo
yd EC, T
u
cker CS. Pon
d
Aquac
ultur
e
W
a
ter Qual
it
y
M
ana
geme
n
t. Ne
w
York: Spri
n
ger Scie
nce
.
199
8.
[3]
Zimmerman
RJ, Minello T
J
, Zamo
ra G. Sel
e
ction
of Ve
ge
tated H
abitat
b
y
Br
o
w
n
Shrim
p
, Pen
aeu
s
Aztecus, In Galveston Bay
Salt Marsh.
Fishery Bulletin
. 196
4; 82(2).
[4]
DeZuane J. Drinking
Water Qualit
y. Seco
nd
Editio
n.
Danv
e
r
s: Cleara
n
ce
Center. 19
96.
[5]
Xi
e J, Sun
XH
, Pan JY, Z
h
a
o
Y. Ph
ysicoc
hemic
al
Pro
p
e
r
ties And B
a
ctericid
al Activ
i
ti
es Of Acidic
Electrol
yz
ed
W
a
ter Used Or Stored At
Different T
e
mperatur
es On Shrimp.
F
ood Res
earc
h
Internatio
na
l.
2013; 47(
2): 331
-336.
[6]
Cha
o
J, W
u
-bi
n
X, Bi
ng
L. Desig
n
of In
stru
ment Co
ntrol S
y
stem Bas
ed
o
n
La
bVIEW
.
TELKOMNIKA
Indon
esi
an Jou
r
nal of Electric
al Eng
i
ne
eri
ng.
2013; 1
1
(6): 3
427-
343
2.
[7]
T
hepsatorn P,
Numsomra
n A,
T
i
psu
w
a
n
porn
V, T
eanthong
T
.
DC Motor S
pee
d C
ontrol
u
s
ing F
u
zz
y
Log
ic Base
d o
n
LabVIEW
.
In SICE-ICAS, Internati
ona
l Joi
n
t Confere
n
ce. 2
006.
[8]
Z
heng
jun
Q, Xi
ao
xi
ng T
,
Jieh
ui S, Yi
da
n B. I
rrigatio
n
Decis
i
on-Maki
ng
S
y
s
t
em Base
d o
n
T
he F
u
zz
y
-
Contro
l T
heor
y and Virtua
l Ins
t
rument.
ACSES.
2013; 8.
[9]
Luo J, Ault JS
, Larkin MF
, Barbi
e
r
y
LR. Sa
linit
y Me
asure
m
ents from Po
p-Up Arch
ival
T
r
ansmitting
(PAT
)
T
ags and T
heir Ap
pli
c
ation t
o
Geol
ogic
a
l Estimati
on for Atl
antic
T
a
rpon.
Marine Ecology
Progress Ser
i
e
s
.
2008; 36
7: 101-1
09.
[10]
Duq
ue W
O, Hu
guet NF
, Dom
i
ngo J
L
, Schu
h
m
acher M.
Ass
e
ssin
g
W
a
ter
Qualit
y i
n
Riv
er
s
w
i
t
h
F
u
zz
y
Inference S
y
st
ems.
Environ
m
ental Intern
atio
nal.
20
06; 32(
6
)
: 733-74
2.
[11]
Glenn
on R. Groun
d
w
at
er Pu
mpin
g and T
he F
a
te of
America F
r
esh W
a
ters. W
a
shingto
n
DC: Islan
d
Press. 2002.
[12]
Ichsan MH
H, Yuda
nin
g
t
y
as
E, Muslim MA
. Op
timal Sol
u
tion P
a
th F
i
n
d
in
g Usi
ng F
u
zz
y
-
D
ijkstr
a
H
y
brid Al
gor
ith
m
.
EECCIS.
2013; 6(2): 15
5-1
60.
[13]
Pra
y
itno
A, In
dra
w
at
i V, Uto
m
o G. T
r
ajector
y
T
r
acking
o
f
AR.Dron
e
Q
uadr
otor Us
ing
F
u
zz
y L
ogic
Contro
ller.
T
E
L
K
OMNIKA T
e
leco
mmu
n
icati
o
n Co
mp
uting E
l
ectron
ics and
Contro
l.
201
4; 12(4): 81
9-
828.
Evaluation Warning : The document was created with Spire.PDF for Python.