Optimization of Weighted Product Methods for Choosing Internet
Providers
Niken Dwi Nirma, Linda Perdana Wanti
a
and Riyadi Purwanto
b
Department of Informatics, Politeknik Negeri Cilacap, Jl. Dr. Soetomo No.1, Cilacap, Central Java, Indonesia
Keywords: Weighted Product, Decision Support System, Extreme Programming, Internet Providers.
Abstract: The Internet is very much needed and important for most people in the digital era now. The problem that
arises is that there is competition among internet providers to give the best service in fulfilling the user needs
of internet access. This study optimizes Weighted Product (WP) decision-making method to choose the
internet provider with the best service so that users can determine the provider to be used as needed. The
criteria used to optimize the WP method are access speed, price, latency, validity period, provider credibility,
and the amount of quota in each package. Extreme Programming (XP) is the system development used to
build this decision support system. This study issues recommendations of internet provider that is produced
by optimizing the WP method with various criteria used that has the highest preference value, thus it becomes
a reference for the user to choose that provider to meet the needs of internet access.
1 INTRODUCTION
Every year internet card providers are competing to
improve their quality. It can be improving the speed
of access and latency or competing to attract the
consumers attention by providing low prices and
more internet quota (Marwa Sulehu, 2015). However,
the Kubangkangkung area is a village that has slow
internet access in all providers, thus requiring
alternative criteria and ranking calculations to get the
appropriate provider. The choice of internet providers
in Kubangkangkung area, which is a highland region,
encountered several problems, including the
affordability of the signal, the speed of access, and
competitive price. By considering several criteria that
will be used to solve problems regarding the selection
of internet providers in the Kubangkangkung area, a
decision support system is created by optimizing the
decision support method used, the WP method. The
WP method was chosen to find solutions for the
selection of internet providers because the method
can display alternatives with the highest preferences
weighted produced by multiplying the criteria
attribute rating weights for each alternative involved
that has been normalized in advance (Chourabi et al.,
2019). Another reason is because the calculation time
a
https://orcid.org/0000-0002-6679-2560
b
https://orcid.org/0000-0001-7066-2905
is more quickly completed, there are Cost and Benefit
variables, which are useful for determining the
weighting value for each criterion followed by an
alternative ranking of internet provider cards in order
to produce the best internet provider card. However,
there are the criteria that must be included in the
benefit group and ones that are included in the cost
group. The criteria used are the speed of access for
each existing client, the quota price for each internet
provider, the amount of quota per each internet
provider, signal strength or latency, the internet
validity period and finally by K1 to K6. While the
alternatives for those involved in this decision support
system are 5 alternatives representing internet
providers, namely Telkomsel, Smartfren, Indosat,
XL, and Tri symbolized by R1 through R5.
The decision support system for choosing an
internet provider has been carried out by several
researchers. Previous research was make a decision
support system for the selection of internet providers
in STMIK AKBA. The method used to find solutions
is the weighted product method. The problem faced
by internet users at STMIK AKBA is to replace the
internet provider that is now used by paying attention
to several things such as connection type,
maintenance, connection stability, access speed, price
1260
Nirma, N., Wanti, L. and Purwanto, R.
Optimization of Weighted Product Methods for Choosing Internet Providers.
DOI: 10.5220/0010963400003260
In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 1260-1266
ISBN: 978-989-758-615-6; ISSN: 2975-8246
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

and etc. The results obtained are the chosen internet
provider that will be used on the STMIK AKBA
campus, namely icon + which gets a preference
weight of 0.2650, among the three other internet
providers namely Indihome with 0.2354, Indosat with
a weight gained 0.2448 and Lintasarta with 0.2546
(Marwa Sulehu, 2015). Previous research was
implemented the analytical method hierarchy process
(AHP) to select internet providers using the criteria of
cost, speed, package, needs and quota. Decision
support system are made based on web with
alternatives involved in weighting of six namely
Indosat, Smartfren, Axis, XL, Tri, and Telkomsel.
This study produces an internet provider
recommendation that has been analysed using the
AHP method with rank 1 to 6 in sequence, namely
Telkomsel, Indosat, Tri, Smartfren, XL, Axis
(Prasetyo et al., 2013). Previous research was
analysed the selection of internet providers at PT.
Pool Cargo Services that requires internet access in
each of the company’s operational activities. PT. Pool
Cargo services require a decision support system that
provides recommendations for an internet provider to
support operational activities using several criteria
used. The criteria used include internet provider
credibility, customer satisfaction, security from
internet providers and fees for each internet provider.
The alternatives selected in this study were three
namely speedy Telkom, First media, and CBN.net.
The decision-making method used in research is
AHP. The results obtained from the research
conducted is a decision support system that produces
recommendations from internet providers chosen by
PT. Pool Services that is a speedy Telkom with a
weight of 45.3% higher than the other two
alternatives, each of which is First Media at 35.4%
and CBN.Net at 19.3% (Amin, 2015).
The difference between this research and previous
research is that alternatives involved in the decision
of the best internet provider can later be used in the
Kubangkangkung area. In addition to alternatives, the
criteria used to obtain the best decision are six
criteria. Optimization of the decision-making method
that is the weighted product method is also a renewal
of research conducted with previous studies. A
selected alternative can be used as a recommendation
for decision on previous calculations. By maximizing
the function of the decision support system itself, the
user can obtain a decision on a problem through
calculations using the WP method.
2 DECISION SUPPORT SYSTEM
A computer-based system that are used to provide a
recommended solution to the problem is called a
decision support system. Decision support systems
are also used with a variety of methods used to
recommend a solution(L. et al Wanti, 2020)(Mamat
et al., 2019). Solving previous problem decisions uses
conventional methods without taking into account the
decision-making criteria (L. P. Wanti et al., 2019)
(Dweiri et al., 2017). After the existence of a decision
support system, solutions are made with assistance of
a computer device containing a decision support
system while still taking into account the criteria and
utilizing the decision making method to obtain a
decision with the highest value or weight calculation
(L. P. Wanti et al., 2020)(Zasada et al., 2017).
Decision support system can be implemented in
various ways such as the selection of internet
providers.
2.1 Conceptual Model Decision
Support System
The conceptual model of a decision support system
consists of several main components including data
management used for processing decision support
systems that are taken and stored from a decision
support system database (Abd Rahman et al., 2020).
Then there is the management model that is used to
develop a decision support system (Alyaev et al.,
2019). Knowledge management to support the system
created is like additional knowledge issued by the
system when displaying the output of the developed
decision support system (Putra et al., 2018). And
lastly, there is a management interface that is used for
interaction between end-users and the decision
support system made (Irvanizam, 2017).
Figure.1 explains the conceptual model of decision
support systems with databases, users and other
computer-based systems. The figure also describes a
flexible decision support system when connected to
other computer-based systems that pay attention to
output for the user through the design of the user
interface and with the database which used both data
from the system environment and data from outside
the system (Alshibly, 2015) (Alyaev et al., 2019). The
data used is then put into the data management section
to be associated with the knowledge management and
model management that can be used to obtain
solutions (Chandra et al., 2019) (Putra et al., 2018).
Optimization of Weighted Product Methods for Choosing Internet Providers
1261

Figure 1: DSS Conceptual.
2.2 Weighted Product Method
The decision support system method used in the
selection of internet providers is weighted product.
The method is optimized to obtain the best internet
provider decision results with six criteria used to
determine the weight of each alternative
involved(Ahsan, 2019)(Taufik, 2019). The WP
method is easily adapted because of the simple step
of multiplying each attribute rating and linking the
attribute rating for each criterion used (Chourabi et
al., 2019). It is done by first ranking each attributes
by weighting the attribute criteria used and the
alternatives involved (Khairina et al., 2016). The
stages of the WP method are as follows:
 Specify alternative (Ri) i= 1,2,3..,n is object
namely provider.
 Specify criteria (Kj) j= 1,2,3,…,n is criteria to
choose provider.
 Calculate the criterion weight value (Wj) for
selecting provider. Weight value in the selection
of provider criteria values from the people in
Kubangkangkung.
1


(1)
 Calculate the value of Pi, Pi is the preference
value for alternatives (Ri).
  



(2)
with i=1,2,3,…n
 Qi that is the vector value used to determine Ri
ranking.
 
∏




∏




(3)
with i=1,2,3,…n
3 RESEARCH METHOD
The research method used to develop optimizing of
WP methods for choosing internet providers is an
extreme programming method. The extreme
programming method is used because it is felt to be
the most appropriate situation and conditions at the
time of the development of this decision support
system. System development must be quickly done
by taking into account the needs of the system being
analyzed in a short time, with a minimum risk that
must be carried by the developer and equipped with
flexibility which means the system can later be
adapted and easy to implement (Roky & Meriouh,
2015) (Tolfo & Wazlawick, 2008). The object
oriented approach when developing an internet
provider selection decision support system is also one
of the reasons for choosing extreme programming
methods (Fojtik, 2011) (Pertiwi, 2018).
Figure.2 below is explains the stages of the XP
method. System development starts with selecting all
user needs to be implemented in the decision support
system. After all user definitions are detailed, they
break down and sort out each user’s needs in plans for
the tasks to be carried out and completed. At the
planning stage of this task, the design modules of the
internet provider selection decision support system
are made and be integrated with the database used and
the user interface in accordance with user needs at the
beginning of the XP method process (Azdy & Rini,
2018) (Schneider & Johnston, 2005). After the system
is finished, the resulting product is tested to determine
deficiencies and improvements that must be done and
adjusted for feedback from the test results
recommended by testers or users who test this
decision support system (Rahmi et al., 2016) (L. P.
Wanti et al., 2021). After repairs are made, the
product is in the final stage before being released and
evaluated in case there is an error in the future when
the product is implemented or when it is used
(Supriyatna, 2018) (Gumelar et al., 2017).
Another Computer
Based System
Knowledge
Management
Model
Management
Data
Management
User Interface
User
Internal
database
External
database
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
1262

Figure 2: Extreme Programming.
4 RESULTS AND ANALYSIS
4.1 Alternative
The following is a list of alternatives that have been
defined in this system which will later be used as
alternative options to consider problem solving:
Table 1: Alternative.
Code alternative Name alternative
R1 Telkomsel
R2 Smartfren
R3 Indosat
R4 Xl
R5 Tri
4.2 Criteria
There are six criteria in the provider selection
decision support system, symbolized by K1, K2, K3,
K4, K5, and K6. This criterion generate a weight
value to measure how important the value of each
criterion .The six criteria have the following values:
Table 2: Criteria.
Code criteria Name criteria Value Type
K1 Internet access speed 5
Benefit
K2 Internet Package Prices 5
Cost
K3 Large Quota 4
Benefit
K4 Latency 4
Cost
K5 Internet Term 4
Benefit
K6 Credibility of the Provider 4
Benefit
⨊ W
26
4.3 The Criteria Weight Value
Calculate the normalized weight (Wj).
Wj=

∑

(1)
W1=



=0.192
(2)
W2=



=0.192
(3)
W3=


=0.154
(4)
W4=


=0.154
(5)
W6=


=0.154
(6)
The result of the of calculating the normalized weight
values from the criteria table above by entering the
weight value into the formula such as the calculations
that have been done above, a result table is obtained
below:
Table 3: Normalized Weight Of Criteria.
Code criteria Criteria value Normalized
weight (Wj)
K1 0.192 0.192
K2 0.192 -0.192
K3 0.154 0.154
K4 0.154 -0.154
K5 0.154 0.154
K6 0.154 0.154
4.4 List of Alternative Values based on
Criteria (Y
)
Alternative involved in the decision process of
choosing internet provider are 5 alternatives
symbolized by R1, R2, R3, R4, and R5. R1 to R5
represents the providers that will be selected, namely
Telkomsel, Smartfren, Indosat, XL, and Tri. The
values for each alternative of each criterion can be
seen in the following table:
Table 4: Weight of Each Alternative for Each Criteria.
Alternatife
Criteria
K1
K2 K3
K4 K5 K6
R1
7.615 65000 10 98 30 31.7
R2
1.710 80000 28 61 28 21.7
R3
5.485 85000 27 75 30 20
R4
5.635 67000 15 47 30 25
R5
5.125 60000 51 76 30 1.6
Select user
requirement for
this
pr
oduct
Break down
requirements
Planning the task
Testing product
Release Product
Evaluate Product
Development and
integrasi product
Optimization of Weighted Product Methods for Choosing Internet Providers
1263

4.5 The Value Vector (P)
Pi =
∏




,1,2,3,…
(1)
P1=(7.615
.
(65000
.
)(10
.
)(
98
.
)(30
.
) ( 31.7
.
)= 0.355
(2)
P2=(1.71
.
80000
.
)(28
.
)(
61
.
)(28
.
) ( 21.7
.
)=0.301
(3)
P3=(5.485
.
85000
.
)(27
.
)(
75
.
) (30
.
) ( 20
.
)= 0.358
(4)
P4=( 5.635
.
67000
.
)( 15
.
)(
47
.
) (30
.
) (25
.
)= 0.382
(5)
P5=( 5.125
.
60000
.
)( 51
.
)(
76
.
) (30
.
) (1.6
.
)= 0.282
(6)
  1.677
(7)
The preference value Pi for alternative R1 based on
Table 5 is 0.355, alternative R2 is 0.301, alternative
R3 is 0.358, alternative R4 is 0.382, and alternative
R5 is 0.284. The highest P1 value is obtained by
alternative R4 which represents internet provider
from XL with a preference weight of 0.358. The
second rank is R3 which represents Indosat with a
preference weight of 0.358, then Telkomsel which is
R3 with a weight of 0.355 fourth is R2 with a
preference weight of 0.301 which represents the
internet provider Smartfren, and lastly, there is a
provider from Tri which is an R5 with a weight of
0.282. Limitation of the criteria and the place taken
may be a reference for readers or developers to
develop this system by taking data in different places
in the future.
Table 5: Value of Pi.
Alternatife
Criteria
Pi
K1
K2 K3
K4 K5 K6
R1 1.478 0.119 1.425 0.494 1.688 1.702 0.355
R2 1.109 0.114 1.670 0.531 1.670 1.605 0.301
R3 1.387 0.113 1.660 0.515 1.688 1.585 0.358
R4 1.394 0.118 1.517 0.553 1.688 1.641 0.382
R5 1.369 0.121 1.831 0.514 1.688 1.075 0.282
⨊ Pi
1.677
Figure 3 below is the result of vector Pi
calculations. Pi value is obtained by multiplying all
the criteria weights for each alternative with positive
rank weights for the criteria included in the benefit
criteria group and negative rank weights for the
criteria included in the cost criteria group by using
two equation.
Figure 3: Preference Value of Each Alternative.
4.6 Ranking (Q)
The final stage in the weighted product method is to
calculate the value of Qi vector after previously
having obtained the Pi vector in figure 3 divided by
the number of Pi vectors which are then sorted by the
highest value (Chourabi et al., 2019).
Qi =

∑

(1)
Q1 =
.
.
= 0.211
(2)
Q2 =
.
.
= 0.179
(3)
Q3 =
.
.
= 0.213
(4)
Q4 =
.
.
= 0.228
(5)
Q5 =
.
.
= 0.168
(6)
Table 6 shows the results of the calculation of the
Qi vector value for each alternative R1 to R5 provider
which is involved in the internet provider selection
process against the criteria K1 to K6.
Table 6: Vector Qi.
Code alternative Qi Ranking
R1 0.211 3
R2 0.179 4
R3 0.213 2
R4 0.228 1
R5 0.168 5
0
1
2
3
R1 R2 R3 R4 R5
Criteria K1 Criteria K2
Criteria K3 Criteria K4
Criteria K5 Criteria K6
Pi
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
1264

The following is the final graph of determining the
best alternative:
Figure 4: Grafik Value Vector Qi
5 CONCLUSIONS
Optimization of the weighted product method has
resulted in a solution to the problem of selecting
internet provider recommended for use in the
Kubangkangkung area with a preference value vector
Qi of 0.228. XL was chosen by using predetermined
criteria and adjusted to the geographical situation and
condition of the Kubangkangkung area. Among the
second to fifth consecutive are Indosat with a
preference value vector Qi of 0.213,Telkomsel with a
preference value vector Qi of 0.211, Smartfren with a
preference value vector Qi of 0.179 and in the
distended position there is Tri provider with a
preference value vector Qi of 0.168. This research has
produced a decision support system for the selection
of internet providers with the highest results of
Telkomsel.
REFERENCES
Abd Rahman, M. S., Mohamad, E., & Abdul Rahman, A.
A. (2020). Enhancement of overall equipment
effectiveness (OEE) data by using simulation as
decision making tools for line balancing. Indonesian
Journal of Electrical Engineering and Computer
Science, 18(2), 1040–1047. https://doi.org/10.11591/
ijeecs.v18.i2.pp1040-1047
Ahsan, M. et al. (2019). Selecting multiple intelligences on
children with weighted product , analytical hierarchy
process , simple additive weighting and TOPSIS
Selecting multiple intelligences on children with
weighted product , analytical hierarchy process , simple
additive we. Journal of Physics, 1–7. https://doi.org/
10.1088/1742-6596/1402/7/077033
Alshibly, H. H. (2015). Investigating Decision Support
System ( DSS ) Success : A Partial Least Squares
Structural Equation Modeling Approach. Journal of
Business Studies Quarterly, 6(4), 56–77.
Alyaev, S., Suter, E., Bratvold, R. B., Hong, A., Luo, X., &
Fossum, K. (2019). A decision support system for
multi-target geosteering. Journal of Petroleum Science
and Engineering, 183(July), 106381. https://doi.org/
10.1016/j.petrol.2019.106381
Amin, R. (2015). Metode Analytical Hierarchy Process
Dalam Sistem Pendukung Keputusan Pemilihan
Internet Service Provider. Jurnal Teknik Komputer, 1(1,
ISSN 2442/2436), 66–71. http://ejournal.bsi.ac.id/
ejurnal/index.php/jtk/article/view/237/203
Azdy, R. A., & Rini, A. (2018). Penerapan Extreme
Programming dalam Membangun Aplikasi Pengaduan
Layanan Pelanggan (PaLaPa) pada Perguruan Tinggi.
Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(2),
197. https://doi.org/10.25126/jtiik.201852658
Chandra, Y. U., Karya, S., & Hendrawaty, M. (2019).
Decision Support Systems for Customer to Buy
Products with an Integration of Reviews and Comments
from Marketplace E-Commerce Sites in Indonesia : A
Proposed Model. 9(4), 1171–1176.
Chourabi, Z., Khedher, F., Babay, A., & Cheikhrouhou, M.
(2019). Multi-criteria decision making in workforce
choice using AHP, WSM and WPM. Journal of the
Textile Institute, 110(7), 1092–1101. https://doi.org/
10.1080/00405000.2018.1541434
Dweiri, F., Kumar, S., Ahmed, S., & Jain, V. (2017).
Corrigendum to “ Designing an integrate d AHP base d
decision support system for supplier selection in
automotive industry ” Expert Systems. Expert Systems
With Applications, 72, 467–468. https://doi.org/10.10
16/j.eswa.2016.12.025
Fojtik, R. (2011). Extreme programming in development of
specific software. Procedia Computer Science, 3, 1464–
1468. https://doi.org/10.1016/j.procs.2011.01.032
Gumelar, T., Astuti, R., & Sunarni, A. T. (2017). Sistem
Penjualan Online Dengan Metode Extreme
Programming. Jurnal Telematika, 9(2), 87–90.
Irvanizam, I. (2017). Multiple attribute decision making
with simple additive weighting approach for selecting
the scholarship recipients at Syiah Kuala university.
Proceedings - 2017 International Conference on
Electrical Engineering and Informatics: Advancing
Knowledge, Research, and Technology for Humanity,
ICELTICs 2017, 2018-Janua(October 2017), 245–250.
https://doi.org/10.1109/ICELTICS.2017.8253272
Khairina, D. M., Ivando, D., & Maharani, S. (2016).
Implementasi Metode Weighted Product Untuk Aplikasi
Pemilihan Smartphone Android. 8(1), 1–8.
Mamat, A. R., Mohamed, M. A., Azhar, A. F., Saany, S. I.
A., Rawi, N. A., Amin, M. M., Kadir, M. F. A., & Nor,
M. A. M. (2019). Modelling decision support system
for selection maahad tafiz center using analytical
hierarchal analysis. Indonesian Journal of Electrical
0
0,05
0,1
0,15
0,2
0,25
Qi
Qi
Optimization of Weighted Product Methods for Choosing Internet Providers
1265

Engineering and Computer Science, 13(1), 35–40.
https://doi.org/10.11591/ijeecs.v13.i1.pp35-40
Marwa Sulehu. (2015). Sistem Pendukung Keputusan
Pemilihan Layanan Internet Service Provider
Menggunakan Metode Weighted Product ( Studi kasus :
STMIK AKBA ) Marwa Sulehu STMIK AKBA.
Indonesian Journal on Networking and Security, 4(4,),
55–60.
Pertiwi, D. H. (2018). Metode extreme programming (xp)
pada website sistem informasi franchise lkp
palcomtech. Jurnal Mikrotik, 8(1), 86–98.
Prasetyo, B., Laksito, W., & Siswanti, S. (2013). Sistem
Pendukung Keputusan Pemilihan Paket Internet
Operator Telekomunikasi Dengan Metode Ahp
(Analytical Hierarchy Process). Jurnal Teknologi
Informasi Dan Komunikasi (TIKomSiN), 1(2), 7–12.
https://doi.org/10.30646/tikomsin.v1i2.125
Putra, J. A., Galwargan, A. M., & Adiwijaya, N. O. (2018).
Decision support system scheme using forward
chaining and simple multi attribute rating technique for
best quality cocoa beans selection. International
Conference on Electrical Engineering, Computer
Science and Informatics (EECSI), 2018-Octob, 122–
127. https://doi.org/10.1109/EECSI.2018.8752849
Rahmi, R., Sari, R., & Suhatman, R. (2016). Pendekatan
Metodologi Extreme Programming pada Aplikasi E-
Commerce (Studi Kasus Sistem Informasi Penjualan
Alat-alat Telekomunikasi). Jurnal Komputer Terapan,
2(2), 83–92.
Roky, H., & Meriouh, Y. Al. (2015). Evaluation by Users
of an Industrial Information System (XPPS) Based on
the DeLone and McLean Model for IS Success.
Procedia Economics and Finance, 26(0), 903–913.
https://doi.org/10.1016/s2212-5671(15)00903-x
Schneider, J. G., & Johnston, L. (2005). eXtreme
Programming - Helpful or harmful in educating
undergraduates? Journal of Systems and Software, 74(2
SPEC. ISS.), 121–132. https://doi.org/10.1016/j.jss.
2003.09.025
Supriyatna, A. (2018). Metode Extreme Programming Pada
Pembangunan Web Aplikasi Seleksi Peserta Pelatihan
Kerja. Jurnal Teknik Informatika, 11(1), 1–18.
https://doi.org/10.15408/jti.v11i1.6628
Taufik, et al. (2019). Decision support system design for
determining brown sugar quality with weighted product
method Decision support system design for
determining brown sugar quality with weighted product
method.
Journal of Physics, 1–8. https://doi.org/
10.1088/1742-6596/1280/2/022019
Tolfo, C., & Wazlawick, R. S. (2008). The influence of
organizational culture on the adoption of extreme
programming. Journal of Systems and Software,
81(11), 1955–1967. https://doi.org/10.1016/j.jss.2008.
01.014
Wanti, L. et al. (2020). A support system for accepting
student assistance using analytical hierarchy process
and simple additive weighting A support system for
accepting student assistance using analytical hierarchy
process and simple additive weighting. Journal of
Physics. https://doi.org/10.1088/1742-6596/1430/1/01
2034
Wanti, L. P., Ikhtiagung, G. N., & Pangestu, I. A. (2021).
Implementasi Extreme programming Pada Website
Marketplace Lapak Petani Online. 12(01), 50–58.
https://doi.org/10.35970/infotekmesin.v12i1.427
Wanti, L. P., Laksono, K. Y., & Purwanto, R. (2019).
Implementasi Metode User Centered Design Pada
Sistem Pendukung Keputusan Peramalan Penjualan
Ikan Hias. Jurnal ICT : Information Communication &
Technology, 18(1), 26–33. https://doi.org/10.36054/
jict-ikmi.v18i1.39
Wanti, L. P., Maharrani, R. H., Wachid, N., & Prasetya, A.
(2020). Optimation economic order quantity method for
a support system reorder point stock. International
Journal of Electrical and Computer Engineering,
10(5), 4992–5000. https://doi.org/10.11591/ijece.v10
i5.pp4992-5000
Zasada, I., Piorr, A., Novo, P., Villanueva, A. J., &
Valánszki, I. (2017). What do we know about decision
support systems for landscape and environmental
management? A review and expert survey within EU
research projects. Environmental Modelling and
Software, 98, 63–74. https://doi.org/10.1016/j.envsoft.
2017.09.012
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
1266