Abstrack - Soybean (Glycine max (L.) Merrill var. Willis) is one of the crops and has become a staple in Indonesia. With the development of technology today soybean plants begin simulated by using a 3D shape with Groimp applications based XL System and to prove the growth simulation research using organic fertilizer and urea fertilizer at different treatment This study aimed to investigate the effect of fertilizing with liquid organic fertilizer on the productivity of soybean plants, know the time of fertilization that provides the best results and to know the interaction between fertilizer type and time of fertilization. The study was conducted with a structured design. Factors that first dose of fertilizer are: P1 (3 ml of organic fertilizer / 1 liter water / Evening), P2 (3 ml of organic fertilizer / 1 liter water / Morning), P3 (2 g urea / 1 liter water / Evening), P4 (2 g urea / 1 liter water / Morning). Parameters observed that plant height, stem length, number of branches and number of leaves. The data obtained were entered and calculated using ANFIS after the training process and the smallest error obtained from the plant where the election will be simulated in 3D. The results showed that fertilization with urea fertilizer can increase the productivity of soybean plants were compared using Liquid Organic Fertilizer. When fertilizing in the afternoon also causes soybean crop productivity higher than in the morning. Between time and type of fertilizer are to increase plant height interaction, many branches and many leaves of soybean. season and the environment affect the growth of plants and to research obtained herbs having etiolasi and after the transfer of the place after day to 28 to a place that is roomy in fact still not give an influence upon a plant which is supposed to the age of soybean already flowering at the age of to 35-40 day is not blossom, it is expected that plants season should indeed be planted in the season to the result is also maximum and environmental conditions must be considered.

1. Soybean (Glycine max (L.) Merrill var. Willis) growth
simulation the Urea Dose Variations On Giving And
Biological Fertilizer Formula Rhizobium Using ANFIS Based
XL System
Authors Angga Debby Frayudha (09650075)
Department Of Engineering Informatics
UIN Maulana Malik Ibrahim Malang
Malang 65144
mpyenkgmail.com
Mentors Dr. Suhartono, M.Kom
Department Of Engineering Informatics
UIN Maulana Malik Ibrahim Malang
Malang 65144
galipek@gmail.com
Abstrack - Soybean (Glycine max (L.) Merrill var.
Willis) is one of the crops and has become a staple in
Indonesia. With the development of technology today soybean
plants begin simulated by using a 3D shape with Groimp
applications based XL System and to prove the growth
simulation research using organic fertilizer and urea fertilizer
at different treatment This study aimed to investigate the effect
of fertilizing with liquid organic fertilizer on the productivity
of soybean plants, know the time of fertilization that provides
the best results and to know the interaction between fertilizer
type and time of fertilization. The study was conducted with a
structured design. Factors that first dose of fertilizer are: P1
(3 ml of organic fertilizer / 1 liter water / Evening), P2 (3 ml
of organic fertilizer / 1 liter water / Morning), P3 (2 g urea / 1
liter water / Evening), P4 (2 g urea / 1 liter water / Morning).
Parameters observed that plant height, stem length, number of
branches and number of leaves. The data obtained were
entered and calculated using ANFIS after the training process
and the smallest error obtained from the plant where the
election will be simulated in 3D. The results showed that
fertilization with urea fertilizer can increase the productivity
of soybean plants were compared using Liquid Organic
Fertilizer. When fertilizing in the afternoon also causes
soybean crop productivity higher than in the morning.
Between time and type of fertilizer are to increase plant height
interaction, many branches and many leaves of soybean.
season and the environment affect the growth of plants and to
research obtained herbs having etiolasi and after the transfer
of the place after day to 28 to a place that is roomy in fact still
not give an influence upon a plant which is supposed to the
age of soybean already flowering at the age of to 35-40 day is
not blossom, it is expected that plants season should indeed be
planted in the season to the result is also maximum and
environmental conditions must be considered.
Keyword : Glycine max (L.) Merrill var. Willis, 3D
Shape, Groimp, XL System, ANFIS
1. INTRODUCTION
Soybean or usual called soy beans is one of the plants
whose legumes being elementary substance much food of
eastern asia, such as soy sauce know, and the food. The
soybean plant is short steam( 30-100 cm ), shaped of
herbaceous plants, and woody. The stem of the soybean plant
is usually rigid and resistant to fall, except that is cultivated in
the rainy season or plant that lives in a Lacking light place (
adisarwanto, 2005; pitojo 2003 ). According to ( eric m.scuct
and a cur. Semwal, micikevicius, 2007: p, c.e.hughes,
j.m.moshell, 2007 ).
Manuring is the absolute to be used to obtain optimum
result of a plant, from that an assortment of research is done to
obtain fertilizing the best way to plants. On the study is done a
fertilizing treatment with pattern organised by fertilizing using
organic fertilizer and urea and distinguishing time of
fertilization between fertilizing the afternoon and the next
morning following draft research briefly treatment 1 using
organic fertilizer liquid 3 mls / litre done the afternoon,
treatment using liquid organic fertilizer 2 3 mls / litre done in
the morning, treatment using fertilizer urea 2gr 3 per liter done
the afternoon, treatment using fertilizer urea 2gr 4 per liter
done in the morning so as to obtain data the morphology of
plants that will be inputan in the course of the simulation.
In modeling the growth of plants who describes organic
element of a plant that is spatially dynamic and complex will
be very tricky approached with mathematical equation and
geometric conventional. Scientists now have broken with the
conclusion that the natural process of growth in plants in the
system of life and are complex biological characteristics,
which are affected by the environment has been able to
analysis and in the synthesis in the form of modeling artificial
life that same natural environment with the approach xl-
system. The purpose of this research, to model the form of the
size and the number of the structure of plants by using the
method anfis, and get a pattern of the rules that form a kind of
a plant such as the original. To produce a form of with this
method to do two steps, namely application of grammar to

2. produce a string contains the structure of topology of trees and
interprestasi of a string. To a first step done with the methods
rekursif, and for the second step, should be conducted by a
method of the iterative. The implementation of application is
using the software groimp to visualis the form of a plant.
Numeric analysis approach toward the system fuzzy first
drafted by tagaki and sugeno ( iyatami and harigawa, 2002 )
and after that a lot of the study associated with it. The system
that dna-based fuzzy ordinary expressed with knowledge
shaped “if-then” that provide benefit not need for analysis of
mathematical modeling. A system like this could process of
reasoning and human knowledge that is oriented toward the
aspect of qualitative. As we know, mathematical modeling a
kind of differential equations not proper to handle the system
that face the state of not erratic or undefined not good ( shing
and jang, 1993 ). At the other side neural network have an
advantage ease in classify an object based on a bunch of a
feature that suggestion system. With only enter a number of
features and then use the data, conduct training a system based
on neural network could distinguish between one object for
another ( widowers, etc. , 2001 ). This system also have excess
against conventional system of them:
1. Anfis capable of being and can do the acquisition of
knowledge under noise and uncertainty.
2. The representation of knowledge be flexible.
3. Tolerant of a mistake.
Considering excess anfis, and then in this paper anfis
implement a method to calculate error in inputan plants and
taken one data with plants which one has the smallest error
and used as a model the simulation. A system of inference
fuzzy used is a system of inference fuzzy model tagaki-
sugeno-kang ( tsk ) order one with consideration simplicity
and ease computation. A system of fuzzy inidigabungkan with
algorithms learning neural network.
1.1 The function of membership
According to kusuma dewi and purnomo understanding
function membership ( membership ) is a function of a curve
that show the mapping of dots data input into the value of its
membership ( degree membership ) having the interval
between 0 to 1. One of the ways that can be used to get a
membership through approach is to function. The functions
that are not used a whole, but only one of them. In this case
the function of membership used is a function membership
generalized bell.
1.1.1 The representation of linear
Linear, in representation the mapping of the input to
degrees membership is described as a straight line.
Figure 1.1 Linear Representation
With function membership
1.1.2 The Representation of a curve of a triangle
A curve of a triangle is basically a joint between two
lines ( linear ). According to susilo ( 2003 ).
Figure 1.2 Representation of a curve of a triangle
With function membership
1.1.3 The representation of a curve of a trapezoid
A curve trapezoid essentially as it curves triangular, it '
s just there are several points that have value membership 1.
Still according to susilo ( 2003 ).
Figure 1.3 The representation of a curve of a trapezoid
With function membership
1.1.4 The representation of a curve an -S

3. A curve growth and depreciation is a curve -S or sigmoid
relating to increase and decrease the surface is not linear. (
kusumadewi and purnomo, 2010 ).
Figure 1.3 The representation of a curve an -S
With function membership
1.1.4 The function of membership Generalized bell ( Gbell
)
Function gbell disifati of a parameter {a,b,c}.
Figure 1.3 The function of membership Generalized bell
1.2 Architecture anfis
Figure 1.5 Architecture anfis
A layer of 2. Serves to awaken degrees membership
With X1 and X2 is input for a knot ke-i. The output of each
neuron in the form of degrees membership given by function
membership input; namely : μ_A1 (x2), μ_B1 (x1), μ_A2 (x2)
aor μ_B2 (x2). Use of a generalized membership bell ( gbell ).
With {ai, bi dan ci} Is the parameter of the function of
membership or called as the parameters premise that is usually
value bi = 1. (Sri Kusumadewi and Sri Hartati, 2006).
A layer of 2. Each neuron in the second in the form of
neurons fixed whose output is the result of the first layer.
Usually used operators AND. Every node represent α the
predicate of the rules of-i. This was serves to awaken firing-
strength by multiplying any input signal. ( sri kusuma dewi
and sri hartati, 2006 ).
A layer of 3 every neurons in the third layer in the form of
node fixed that is the result of calculating the ratio of a (w)
predicate, From a rule to–i against the sum of a whole a
predicate. A function of this layer to normalizes firing
strength. ( sri kusumadewi and sri hartati, 2006 ).
A layer of 4 each neuron in the lining of the fourth is node
adaptive against an output. With wi is normalised firing
strength in the third layer and { of pi, qi and indonesian } is
parameters on these neurons. Parameters at the layer was
called by the name consequent a parameter. ( sri kusumadewi
and sri hartati, 2006 ).
A layer of 5 counting the output signal anfis with add up
all signals in.
1.3 An algorithm learn hybrid
ANFIS in learning a hybrid, ex-coworker means of an
algorithm namely or incorporating the methods least-square
estimator ( LSE) and error backpropagation (EBP).
Table 1.1
1.4 Least Square Estimator
If the value of output of the parameters of a premise
remain so the whole thing can be expressed by a combination
of parameters linear consequent.
.
1.5 A model of propagation of error
On blok diagram picture 2.12 described about sistematika
a groove back of a system anfis. In this process was conducted
an algorithm EBP ( error backpropagation ) where in any layer
done calculations error to perform updates parameters ANFIS.

4. Figure 1.6 A model of propagation of error
a) Error in the 5-layer
Tissue adaptive here 2.12, such as a drawing who have
only 1 neurons in the lining of output ( neurons ), 13 and
propagation error towards on the 5th can be formulated
b) Error in the 4-layer
Propagation error in the 4th, which is toward the namely
neurons 11 and 12 may be formulated neurons
c) Error in the 3-layer
Propagation error in the 3rd, which is toward the namely
neurons 9 and 10 may be formulated neurons
d) Error in the 2-layer
Propagation error in the second, which is toward the
namely neurons 7 and 8 may be formulated neurons
e) Error in the 1-Layer
Propagation error in the 1st, which is toward the namely
neurons or 6
After he got the parameters of the new selanjutnya error
we use to seek for information error against the parameters of
a (a11 and a12 for A1 and A2 , a21 and a22 for B1 and B2), and
c(c11 and cc12 for A2, c12 and c22 for B1 and B2)
After a calculation and is found a change in value of a
parameter aij and cij (delta aij and delta cij )
So that the aij and cij the new thing is that
And is found in value to menload new data
1.6 GroIMP
GroIMP (Growth Grammar-related Interactive Modelling
Platform). As his name, GroIMP is software used as
modeling-3D having some of the features of them: . In a
scene, interactive co-edit rich set of objects 3D, easily
understandable, for a layman lots of options such as color and
texture etc.
.
2. SOLUTION
2.1 Data Analysis
The environmental condition and climate in january to
march, which were rainy season have a problem that is
causing the growth of soybean plant having etiolasi and to be
supported by the environment as it did not favor the growth
because it was not on a broad place. Start at the age of 1-26
days and after the transfer of on the day to 28 to a place that is
roomy any plant still show symptoms etiolasi. Also affect,
light of the sun the intensity of light mean solar january of
230.61 cal/cm2
/day and least 217.82 cal/cm2
/day.
The state of climate the weather is not optimum them
shows the condition for the growth of the soybean plant is. In
general the condition of a plant at the age of 35 up being
essentially growth vegetative soybean subjected to the process
of flowering but not subjected to it
A combination of organic fertilizer and fertilizing time
afternoonin not so affect the growth of crops, the results of the
most visible manure is fertilizing of urea or inorganic by
fertilizing time afternoon, affect the diameter of the stem tall
plant, many branches and many leaves. Under this is
fertilizing treatment by using organic fertilizer basin the
afternoon can be seen in a table 2.1
Table 2.1 The data from the soybean plant is the age of 60
days

5. After obtained the result of the observation of data the
morphology of plants next done the process of data processing
by ANFIS
2.2 Data Processing
In this case consisting of two X1 X2 input and output, and one
Y where X1 is a long rod, X2 is a lot of leaf and Y are high in
plant. Then obtained a rule model Sugeno::
And obtained average weighted
After that the data processed and first sought the value of
ai,bi and ci Using this equation helpdown
S=
After obtained the result obtained menggunakann equation
above the value of ai, bi and el then calculated using tissue
ANFIS ( adaptive neuro fuzzy inference system ) ANFIS
picture of tissue anfis can be seen under this
Figure 2.1 Arsitecture ANFIS
(1) Layer 1
Serves to awaken degrees membership by an equation
below in Table 2.2 the result
Table 2.2 the result Layer 1
(2) Layer 2
Each neuron in the second in the form of neurons fixed
whose output is the result of the first layer. Usually used
operators AND. This coating was serves to awaken firing-
strength by multiplying any input signal. ( sri kusuma dewi
and sri hartati, 2006 ).
Table 2.3 the result Layer 2
(3) Layer 3
A function of this layer to normalizes firing strength. (Sri
Kusumadewi and Sri Hartati, 2006).
Table 2.4 the result Layer 3
(4) Layer 4

6. A function of this layer is for in this research to gain value
{pi, qi dan ri} parameters at the layer was called by the name
consequent parameter (Sri kusumadewi and Sri Hartati, 2006).
Table 2.5 the result Layer 4
(5) Layer 5
Counting the output signal anfis with add up all signals in.
Table 2.6 the result Layer 5
Counting the output signal anfis with add up all signals in.
Figure 2.2 arsitecture EBP
a) error in a 5-layer
Tissue adaptive here as the picture 4.2, who have only 1
neurons in the lining of output ( neurons 13 )
Table 2.7 the result error 5-layer
b) error in a 4-layer
See back images picture 4.2. Propagation error in the 4th,
which is toward the namely neurons neurons 11 and 12
c) error in a 3-layer
See back images picture 4.2. Propagation error in the 3rd,
which is toward the namely neurons neurons 9 and 10
Table 2.8 the result error 4 and 3-layer
d) error in a 2-layer
See back images picture 4.2. Propagation error in the second,
which is toward the namely neurons neurons 7 and 8
Table 2.9 the result error 2-layer
e) error in a 1-layer
See back images picture 4.2. Propagation error in the 1st,
which is toward the namely neurons 6, a neuron 5, a neuron
and a neuron 3, 4

7. Table 2.10 the result error 1-layer
Next the value of error is we use to mengupdate, the
parameters of ai bi and all new.
Table 2.11 the result Update ai and bi
After he got the value of new then the process of
selanjutnya adalah mengupdate the value of tissue anfis new
obtained the result that output a new one.
The difference between anfis before in updates and ANFIS
after in ANFIS update on the value aij and cij this new into
error. And value of error was taken from the smallest data
keberapa to know the value of which will be simulated.
Table 2.12 the result Update new and old
2.3 The Manufacture
Making simulation program program was done twice. The
first part is the process of making program calculation anfis
based on the data obtained from research. The second part,
namely the process of making visualization output anfis by
simulation the growth of the soybean plant is.
2.4 The results of the program
The results of the simulation program in the form of a
display of the simulation 3D accompanied with a caption high
in plant, how many branches, number of leaves, time, and day
and charts at every their nets growth. Here is a picture of the
result of the simulation program:
Figure 2.3 Soybean In GroIMP
Figure 2.4 Soybean Growth Chart
3. SUMMARY
From the observation the soybean plant is obtained plant
with traits etiolasi at the age of 10 days until 28 days and
performed the transfer of a place that is a roomy but the result
is a plant still remains a symptom of etiolasi because of leaves
and branches many but and luxuriance but a small rod and at
the age of plants must be flowering but still not flowering.
After conducted research by lux meters obtained the intensity
of light mean solar january 230.61 cal/cm2/day and lowest
217.82 cal/cm2/day. The state of climate them shows weather
conditions allow plants exposed to etiolasi and an environment
that don ' t support. After it was obtained the result at the
provision of fertilizer inorganic species of urea 2 gr / litre be

8. an increase in high in plant faster than using organic fertilizer
liquid 3 cc / litre, many branches and a lot of leaf also more
visible his influence when using organic fertilizer urea than
fertilizer. The time of fertilization also affect growth, whether
or not the soybean plant is good seen from the result of
fertilization in the afternoon better than early morning hours
against high in plant, many branches, many leaves and
diameter of the stem. Long irradiating also affect whether or
not the soybean plant is good. In the manufacture of growth, a
simulation this soybean program can be concluded that in
general program simulation by using the method ANFIS
(Adaptive Neuro Fuzzy Inference System) Could describe the
pattern of growth and the development of the soybean plant
varieties Wilis percentage accuracy with an average of higher
plants and number of leaves and the number of branches the
first experiment as much as 7,3284 % And on experiment to 2
as much as 7,329354651 %.
4. BIBLIOGRAPHY
O. Kniemeyer, G.Buck-Sorlin, and W. Kurth. A graph
grammar approach to Artificial Life. Artificial Life. 10 (4).
(2004). 413-431
W. Kurth: Introduction to rule-based programming, L-
systems and XL.
W. Kurth: Basic examples in XL (part 1).
W. Kurth: Basic examples in XL (part 2).
M. Henke: A closer look at some examples from the
grogra.de gallery.
Kusumadewi, Sri dan Hartati, Sri. 2006. Neuro-Fuzzy,
Integrasi Sistem Fuzzy dan Jaringan Syaraf.
Yogyakarta:Graha Ilmu.
Shing, J; Jang, R.; 1993; ANFIS : Adap tive-Network-
Based Fuzzy.
Jang, JSR;Sun, CT; dan Mizutani, E. 1997;Neuro-Fuzzy
and soft Computing, London: Prentice-Hall.
Chuldi, M. Prasetya. 2012. Simulasi pertumbuhan tanaman
krisan pada pemberian dosis pupuk urea dan penyiraman
menggunakan ANFIS berbasis xl system. Jurusan Teknik
Informatika Fakultas Sains dan Teknologi Universitas
Maulana Malik Ibrahim Malang.
Risnawati. 2010. Pengaruh pemberian pupuk urea dan
beberapa formula pupuk hayati rhizobium terhadap
pertumbuhan dan hasil kedelai di tnah masam ultisol. Jurusan
Biologi Fakultas Sains dan Teknologi Universitas Maulana
Malik Ibrahim Malang.
Hidayat, O. D. 1985. Morfologi Tanaman Kedelai. Hal 73-
86. Dalam S. Somaatmadja et al. (Eds.). Puslitbangtan. Bogor.
Sumarno dan Harnoto. 1983. Kedelai dan cara bercocok
tanamnya. Pusat Penelitian dan Pengembangan Tanaman
Pangan. Buletin Teknik 6:53 hal.
Lingga dan Marsono. 2009. Petunjuk Penggunaan Pupuk.
Jakarta: Penebar Swadaya.
Suriadikarta, Didi Ardi., Simanungkalit, R.D.M.
(2006).Pupuk Organik dan Pupuk Hayati. Jawa Barat:Balai
Besar Penelitian dan Pengembangan Sumberdaya Lahan
Pertanian. Hal 2. ISBN 978-979-9474-57-5.
Parnata, Ayub.S. (2004). Pupuk Organik Cair. Jakarta:PT
Agromedia Pustaka. Hal 15-18.
[1] Fariza Arna, Helen Afrida, dan Rasyid Annisa. 2007.
Performansi Neuro Fuzzy Untuk Peramalan Data Time
Series. Politeknik Elektronika Negeri Surabaya, Institut
Teknologi Sepuluh Nopember (ITS) Surabaya.