Combined Method to Determine Shrimp Pond Cultivation Land

Linda Perdana Wanti

, Dwi Murni Handayani

, Abdul Rohman Supriyono

Ratih Hafsarah Maharrani

and Nur Wahyu Rahadi

Department of Informatics, Politeknik Negeri Cilacap, Jln. Dr. Soetomo No. 1 Cilacap Selatan, Cilacap, Indonesia

Department of Agro Industry, Politeknik Negeri Cilacap, Jln. Dr. Soetomo No. 1 Cilacap Selatan, Cilacap, Indonesia

Keywords: Decision Support System, Combined Method, Analytical Hierarchy Process, Topsis Method, Shrimp Pond

Land.

Abstract: Shrimp farming has begun to be cultivated in several parts of Indonesia and it is expected to attract some

investments. One of the factors that influence the success of a shrimp pond business is the location of shrimp

pond which must be based on several criteria. This study aims to provide alternative decisions about

appropriate and safe land used as shrimp ponds by taking into account several criteria such as soil texture,

soil pH, water pH, rainfall, beach bottom type, distance and coastline, labour, affordability, security and

marketing the shrimp pond harvest. The method used is a combination of decision-making methods, the

analytical hierarchy process and the technique for order preference by similarity to an ideal solution, while

the system development method is a user-centred design where the system created is tailored to user needs.

The results of this study are a decision support system that provides recommendations for the area that is

suitable for use as a shrimp pond with the highest weight value.

1 INTRODUCTION

The Ministry of Maritime Affairs and Fisheries

through the 2014-2019 strategic plan states the vision

and mission in increasing domestic fisheries

productivity to make Indonesia as a producer of

marine products and to prosper the community

through the improvement offishery products. Exports

of fishery products in 2014 reached USD 4.64 billion.

The achievement of the export value was dominated

by the export value of shrimp commodities which

reached USD 2.09 billion and was followed by the

tuna tongkol cakalang (TTC) commodity of USD

0.69 billion in 2014 (Peraturan Menteri Kelautan Dan

Perikanan Republik Indonesia, 2017).

The large market demand for fish products,

namely fish, shrimp, and seaweed and the higher

selling prices make this business increasingly attract

some people. This is the reason why the products of

the ponds to be one of the fishery commodities which

gives a big profit. It is also what makes the potential

https://orcid.org/0000-0002-6679-2560

https://orcid.org/0000-0003-0601-0026

https://orcid.org/0000-0002-9756-2792

https://orcid.org/0000-0002-4960-8944

of the business opportunities for aquaculture pond

products greater (Andriyanto et al., 2013). To meet

the growing market demand, it is necessary to

accelerate the production of sustainable aquaculture

ponds. The development of aquaculture ponds must

be able to utilize cultivation technology in a

sustainable manner by utilizing the potential of

coastal resources through the feasibility of existing

cultivation lands (Hidayat et al., 2014). Sustainable

aquaculture farming is an environmentally friendly

aquaculture activity that takes into account and

considers biophysical conditions in accordance with

the environmental support in the region (Kusuma, W

A; Prayitna, 2017).

Determination of the appropriate coastal areas

used as shrimp farming land must consider several

factors such as demographic, biological, social and

economic factors (Hasnawi, 2009). Analysis of these

factors is used to get the right area to open the shrimp

farms with maximum profit and minimize the impact

on the surrounding environment (Hakim, L; Supono;

Wanti, L., Handayani, D., Supriyono, A., Maharrani, R. and Rahadi, N.

Combined Method to Determine Shrimp Pond Cultivation Land.

DOI: 10.5220/0010940600003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 94-101

ISBN: 978-989-758-615-6; ISSN: 2975-8246

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Waluyo, S; Adiputra, 2018), (Prakesakwa et al.,

2019). In determining the appropriate area by taking

into account these factors, a decision support system

is needed by considering each region and selecting

the region with the highest value (Yesmaya et al.,

2018), (Yuwono et al., 2015).

Decision support systems have been implemented

to provide recommendations for decisions on a

problem (L. et al Wanti, 2020), (Kholidasari et al.,

2019), (Azlan et al., 2020), (Linda Perdana Wanti et

al., 2020). Various methods are used such as

analytical hierarchy process in (Kar, 2015), (Balubaid

& Alamoudi, 2015), (Mubarok & Maldina, 2017),

(Mamat et al., 2019), simple additive weighting in

(Vafaei et al., 2018), technique for order preference

by similarity to ideal solution (TOPSIS) method in

(Rakhshan, 2017), (Sun et al., 2018), hybrid methods

of AHP and TOPSIS in (Reddy et al., 2019),

(Pramanik et al., 2017), (Wedagama, 2010).

Combined method in this research are used to

provide recommendations for areas that have the

potential to be used as aquaculture pond. The steps

in the AHP (Analytical Hierarchy Process) method

are used to determine the alternative chosen based

on the weights value of each criterion used because

in the AHP method there isa paired comparison

matrix used to test consistency and rank the

alternatives used (Balubaid & Alamoudi, 2015),

(Benmoussa et al., 2019). While the TOPSIS method

is used to alternative selection chosen based on

value of a negative ideal solution and value of a

positive ideal solution (Wedagama, 2010). The

criteria used in this study are soil texture symbolized

by C1, rainfall with C2, bottom type of beach with

C3, distance and coastline with C4, labour with C5,

affordability with C6, security with C7 and

marketing of shrimp farm yields with C8. The

alternatives selected were 6 areas in the Cilacap

area. The results of this study are recommendations

for areas that have the potential to be used as shrimp

farms with a weighting value of the intervention

which has the closest distance to the value of Di

and the farthest distance to the value of a Di

2 RESEARCH METHOD

The system development method used in this study is

user centred design, where all user needs are mapped

into the system design (Schnall et al., 2016), (Luna et

al., 2017), (Linda Perdana Wanti, Azroha, et al.,

2019). Figure 1 explains the research method in

which the system is made oriented to the user in all

stages of the process (Abd Rahman et al., 2020),

(Kautonen & Nieminen, 2018). It starts with defining

the system user context, defining all user

requirements, creating user interface design solutions

and evaluating the system with regard to user

feedback (Georgsson et al., 2019), (Linda Perdana

Wanti, Laksono, et al., 2019). With the evaluation,

the system improvement will be done in accordance

with the feedback from the user until all user needs

are defined and there are no more repairs to be done

(Liu et al., 2016). We strongly encourage authors to

use this document for the preparation of the camera-

ready. Please follow the instructions closely in order

to make the volume look as uniform as possible

(Moore and Lopes, 1999).

AHP method implementation uses 8 criteria, C1

to C8 namely soil texture,rainfall, beach bottom type,

distance and coastline, labour, affordability, safety

and marketing of shrimp pond harvests with 6

alternatives, A1 to A6, namely the Teluk Penyu

coastal area symbolized by A1, Binangun beach area

with A2, Selok beach area with A3, Menganti beach

area with A4, Widara Payung beach area with A5 and

Logending beach with A6.

The method used is AHP and TOPSIS, with the

aim of combining the decision-making steps available

in AHP and TOPSIS, as well as providing

recommendations for the most potential areas to be

Figure 1: Research Model Stages.

Combined Method to Determine Shrimp Pond Cultivation Land

used as shrimp farming land. Analytical hierarchy

process methods provide systematic solutions and

minimize the inconsistency or subjectivity of decision

makers in valuation (Chourabi et al., 2019), (Mamat

et al., 2019). In decision making, it is important to

know how good the consistency is because it is not

necessary to make decisions based on considerations

with low consistency (L P Wanti et al., 2020).

Therefore, it is necessary to check the consistency of

the hierarchy in the decision tree. If the value is more

than 10%, then the judgment assessment must be

corrected. However, if the consistency ratio (CR) is

less or equal to 0.1. Then the calculation results are

declared correct (Yu et al., 2020).

Table 1: Random Index List Consistency.

Matrix Size Ri

1,2 0.00

3 0.58

4 0.90

5 1.12

6 1.24

7 1.32

8 1.41

9 1.45

10 1.49

11 1.51

12 1.48

13 1.56

The stages of the AHP method (Kar, 2015),

namely:

a. Determine the final goal of the decision to be

taken.

b. Develop criteria and alternatives used in the

decision making process.

c. Make a pairwise comparison matrix for each

element involved by selecting the weight of

each criterion oriented to the final goal.

d. Determine the value of the eigenvector vector

and its total using the results of the pairwise

comparison matrix. It starts with normalizing

each column j in matrix A:

∑

𝑖

𝑎(𝑖𝑗) = 1 (1)

e. Calculate the average value of each row i in

matrix A:

𝑊𝑖 = ∑

𝑖

𝑛 (𝑖𝑗) (2)

f. Evaluate each alternative used based on its

weighted value by checking the consistency of

the AHP process hierarchy (Mamat et al., 2019).

Calculate the consistency value of a weight

vector:

(𝐴)(𝑊

𝑇

) = (𝑛)(𝑊

𝑇

) (3)

g. Calculate the consistency index:

(4)

h. Calculate the consistency ratio:

(5)

The value positive ideal solution and value

negative ideal solution only exist in TOPSIS method

is used to select alternatives (Budhi & Wardoyo,

2017). The stages in the TOPSIS method (Sun et al.,

2018), namely:

a. For the first step is normalize the decision

matrix

b. For second step is normalize a weighted

decision matrix where an alternative

performance rating of Ai on each normalized Cj

is calculated using the formula:

(6)

c. For the third step is determine Di+ matrix and

Di-matrix that is determined based on the

normalized weight rating (yij), is calculated

using the formula:

𝑦𝑖𝑗 = (𝑤𝑖)(𝑟𝑖𝑗) (7)

With i=1,2,…,n

And j=1,2,…,m

𝐴

= 𝑦1

,𝑦2

,𝑦3

,…,𝑦𝑛

(8)

𝐴

−

= 𝑦1

−

,𝑦2

−

,𝑦3

−

,…,𝑦𝑛

−

(9)

Where

𝑦

= {max yij with j is profit attribute

{min yij with j is cost attribute

𝑦

−

= {min yij with j is profit attribute

{max yij with j is cost attribute

d. For the fourth step is calculate the distance

between the values of A1 until A6 with Di+

matrix and Di- matrix.

The distance between the alternative Ai and the

positive ideal solution is formulated as:

(10)

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

The distance for negative ideal solution is

formulated as:

(11)

e. The final step is determine the preference value

for each alternative (Vi), where a larger value

(Vi) indicates that alternative Ai is preferred, Vi

value is calculated using the formula:

(12)

Figure 2 shows the hierarchy of the AHP process

where the final goal of this decision support system is

the recommendation of a region that has the potential

to be a shrimp farm.

Figure 2: Hierarchy of AHP Process.

Figure 3 explains hybrid model to determining the

opening of a shrimp farm. Determination of the final

decision, namely the selection of shrimp farming

areas begins by determining the criteria and

alternatives used. Then give weight to each criterion

(Nasution & Bazin, 2018). Still using the AHP

method, the next step is to determine a pairwise

comparison matrix for each alternative involved. The

next step using the TOPSIS method is determining

alternative performance ratings on each criterion

followed by determining positive and negative ideal

solution matrix. After knowing positive and negative

ideal matrices, then still using the TOPSIS method,

the distance between the values of each alternatives

and the matrix is determined. For final step in

TOPSIS is to determine the preference value for each

area of shrimp farming. The alternative with the

preference value which has the closest distance to Di

and the farthest distance to Di

is chosen as a feasible

and potential area to be used as a shrimp farm.

3 RESULT AND ANALYSIS

This study uses a combination of TOPSIS and AHP

methods. The result of combination two methods is a

recommended area that is feasible and has the

potential to be used as shrimp farming land. Starting

by weighting the criteria shown in table 2 and table 3.

Table 2 is the result of weighting the criteria using the

analytical hierarchyprocessmethodwhichconsistsof 8

(eight) criteria used to select areas that could

potentially be used as shrimp farms. The priorities for

each criterion are explained, as follows:

a. Soil texture (C1) is less important than beach

bottom type (C3) and distance from coastline

(C4)

b. Rainfall (C2) is more important than soil

texture (C1)

Figure 3: Combined Method for Determination of Shrimp Pond Cultivation Land.

Combined Method to Determine Shrimp Pond Cultivation Land

c. The beach bottom type (C3) is as important as

the distance from the coastline (C4)

d. Rainfall (C2) is more important than the beach

bottom type (C3) and the distance from the

coastline (C4)

e. Labor (C5) is less important than affordability

(C6) and marketing (C8)

f. Security (C7) is more important than

workforce (C5)

g. Affordability (C6) is as important as

marketing (C8)

h. Security (C7) is more important than

affordability (C6) and marketing (C8)

i. Security (C7) is equally important as rainfall

(C2)

j. Rainfall (C2) is slightly more important than

labor (C5), affordability (C6), marketing (C8)

k. Security (C7) is slightly more important than

soil texture (C1), beach bottom type (C3),

distance from coastline (C4)

l. The beach bottom type (C3) and distance from

the coastline (C4) are as important as

affordability (C6) and marketing (C8)

m. Labor (C5) is less important than beach

bottom type (C3) and distance from coastline

(C4)

The criteria that have been weighted and

normalized with the final total weighted value per

criterion using the analytical hierarchy process

method then ranked. Ranking of A1 until A6 with the

TOPSIS method using normalized criteria and using

the analytical hierarchy process method. Table 3

shows the weighted normalized matrix using the

TOPSIS method, where the value of each alternative

per criterion is multiplied by the weight or value of

the eigen vector values calculated using the AHP

method.

Table 2: Pairwise Comparison Matrix of Criteria.

Criteria c1 c2 c3 c4 c5 c6 c7 c8

c1 1 0.2 3 3 1 3 0.2 3

c2 5 1 0.14 0.14 0.33 0.33 1 0.33

c3 0.33 7 1 1 0.33 1 0.33 1

c4 0.33 7 1 1 0.33 1 0.33 1

c5 1 3 3 3 1 0.330.2 0.33

c6 0.33 3 1 1 3 1 7 1

c7 5 1 3 3 5 0.1 1 0.14

c8 0.33 3 1 1 3 1 7 1

Table 3: Normalized Matrix with Final Weight Value.

Criteria c1 c2 c3 c4 c5 c6 c7 c8 Eigen Vector Value

c1 0.075 0.008 0.228 0.228 0.071 0.387 0.012 0.385 0.174

c2 0.375 0.040 0.011 0.011 0.024 0.043 0.059 0.042 0.075

c3 0.025 0.278 0.076 0.076 0.024 0.129 0.019 0.128 0.094

c4 0.025 0.278 0.076 0.076 0.024 0.129 0.019 0.128 0.094

c5 0.075 0.119 0.228 0.228 0.071 0.043 0.012 0.042 0.102

c6 0.025 0.119 0.076 0.076 0.214 0.129 0.410 0.128 0.147

c7 0.375 0.040 0.228 0.228 0.357 0.013 0.059 0.018 0.165

c8 0.025 0.119 0.076 0.076 0.214 0.129 0.410 0.128 0.147

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

Table 4: Normalized Weight Rating.

Alternative

Criteria

c1 c2 c3 c4 c5 c6 c7 c8

a1 0.029 0.015 0.019 0.007 0.016 0.027 0.018 0.021

a2 0.044 0.008 0.019 0.015 0.024 0.027 0.037 0.032

a3 0.029 0.008 0.019 0.022 0.024 0.013 0.037 0.021

a4 0.029 0.008 0.019 0.015 0.016 0.040 0.018 0.021

a5 0.029 0.015 0.009 0.015 0.016 0.013 0.037 0.032

a6 0.015 0.023 0.009 0.022 0.008 0.027 0.018 0.021

The next step is to determine Di

matrix and Di

matrix using equations (8) and (9). After determining

it, for next step is determine the distance of 6 (six)

alternative areas of shrimp farms with a positive and

negative ideal solution matrix. Determination the

distance of six alternative is carried out with the

normalized matrix using equations (10) and (11).

Preference values indicate alternatives that have the

closest distance to Di

and the furthest distance to Di

. From the appraisal value, it obtained an alternative

ranking of a suitable area and has the potential to

become shrimp farming land. From the ranking of

preference values obtained A2, namely Binangun

beach area with a value of 0.0473, the highest among

the other alternatives. This means that the Binangun

coastal area, based on calculations using the AHP

method and the TOPSIS method, is a feasible area

and has the potential to be used as a shrimp farm. The

value of the positive ideal solution and the value of

the negative ideal solution along with the distance of

each alternative and the preference value are shown

in Figure 4 in the form of the following diagram. The

value Di

shows the distance of alternative values

with positive ideal solution values, Di

shows the

Figure 4: Final Results of Shrimp Pond Farming Land

Ranking.

distance of alternative value with negative ideal

solution values, then V shows the preference values

of each alternative from A1 to A6.

4 CONCLUSIONS

Areas that are feasible and potentially used as shrimp

ponds have been successfully determined using a

decision support system with a combination of two

methods namely the analytical hierarchy process

method and the TOPSIS method. The final results

show that an alternative with A2 code, namely the

Binangun beach area, was selected with the highest

preference value of 0.0473.

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

Andriyanto, F., Efani, A., & Riniwati, H. (2013). Analisis

Faktor-faktor Produksi Usaha Pembesaran Udang

Vanname (Litopenaeus vannamei) di Kecamatan

Pacitan Kabupaten Lamongan Jawa Timur. ECSOFIM

(Economic and Social of Fisheries and Marine), 1(1),

82–96.

Azlan, A., Yusof, Y., & Mohamad Mohsin, M. F. (2020).

Univariate Financial Time Series Prediction using

Clonal Selection Algorithm. International Journal on

Advanced Science, Engineering and Information

Technology, 10(1), 151–156. https://doi.org/10.18517/

IJASEIT.10.1.10235

Balubaid, M., & Alamoudi, R. (2015). Application of the

Analytical Hierarchy Process (AHP) to Multi-Criteria

Analysis for Contractor Selection. American Journal of

Combined Method to Determine Shrimp Pond Cultivation Land

Industrial and Business Management, 05(09), 581–589.

https://doi.org/10.4236/ajibm.2015.59058

Benmoussa, K., Laaziri, M., Khoulji, S., Kerkeb, M. L., &

El Yamami, A. (2019). AHP-based Approach for

Evaluating Ergonomic Criteria. Procedia

Manufacturing, 32, 856–863. https://doi.org/10.1016/

j.promfg.2019.02.294

Budhi, M. A., & Wardoyo, R. (2017). Group Decision

Support System Determination Of Best Employee

Using Topsis And Borda. IJCCS (Indonesian Journal

of Computing and Cybernetics Systems), 11(2), 165.

https://doi.org/10.22146/ijccs.22773

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

Georgsson, M., Staggers, N., Årsand, E., & Kushniruk, A.

(2019). Employing a user-centered cognitive

walkthrough to evaluate a mHealth diabetes self-

management application: A case study and beginning

method validation. Journal of Biomedical Informatics,

91, 103110. https://doi.org/10.1016/j.jbi.2019.103110

Hakim, L; Supono; Waluyo, S; Adiputra, Y. T. (2018).

Performa Budidaya Udang Vaname (Litopenaeus

Vannamei) Semi Intensif di Desa Purworejo

Kecamatan Pasir Sakti Kabupaten Lampung Timur.

Jurnal Rekayasa Dan Teknologi Budidaya Perairan,

6(2), 691–698.

Hasnawi, U. M. A. R. (2009). Penentuan Lokasi

Pengembangan Budidaya Tambak Udang

Berkelanjutan Dengan Aplikasi Sistem Informasi

Geografis di Kabupaten Lampung Selatan. Journal

Riset

Akuakultur, 4(3), 407–423.

Hidayat, A., Sudarsono, B., & Sasmito, B. (2014). Analisis

Kesesuaian Lahan Tambak Menggunakan Sistem

Informasi Geografis. Jurnal Geodesi Undip, 3(April),

28–43.

Peraturan Menteri Kelautan Dan Perikanan Republik

Indonesia, (2017). https://doi.org/10.1017/CBO97811

07415324.004

Kar, A. K. (2015). A hybrid group decision support system

for supplier selection using analytic hierarchy process,

fuzzy set theory and neural network. Journal of

Computational Science, 6, 23–33.

https://doi.org/10.1016/j.jocs.2014.11.002

Kautonen, H., & Nieminen, M. (2018). Conceptualising

benefits of user-centred design for digital library

services. LIBER Quarterly, 28(1), 1–34.

https://doi.org/10.18352/lq.10231

Kholidasari, I., Setiawati, L., & Tartila. (2019). The

implementation of forecasting method by incorporating

human judgment. International Journal on Advanced

Science, Engineering and Information Technology,

9(6), 1982–1988. https://doi.org/10.18517/ijaseit.9.6.

10640

Kusuma, W A; Prayitna, S. B. A. R. W. (2017). Kajian

Kesesuaian Lahan Tambak Udang Vaname

(litopenaeus vannamei) di Kecamatan Cijulang dan

Parigi, Pangandaran, Jawa Barat dengan Penerapan

Aplikasi Sistem Informasi Geografis. Journal of

AquacultureManagementandTechnology, 4(4),95–

100.

Liu, Y. C., Chen, C. H., Lee, C. W., Lin, Y. S., Chen, H. Y.,

Yeh, J. Y., & Chiu, S. Y. H. (2016). Design and

usability evaluation of user-centered and visual-based

aids for dietary food measurement on mobile devices in

a randomized controlled trial. Journal of Biomedical

Informatics, 64, 122–130.

https://doi.org/10.1016/j.jbi.2016.10.001

Luna, D. R., Rizzato Lede, D. A., Otero, C. M., Risk, M.

R., & Fernán, G. B. de Q. (2017). User-centered design

improves the usability of drug-drug interaction alerts:

Experimental comparison of interfaces. Journal of

Biomedical Informatics, 66, 204–213.

https://doi.org/10.1016/j.jbi.2017.01.009

Moore, R., & Lopes, J. (1999). Paper templates. In

TEMPLATE’06, 1st International Conference on

Template Production (pp. 319-340).

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

Engineering and Computer Science, 13(1), 35–40.

https://doi.org/10.11591/ijeecs.v13.i1.pp35-40

Mubarok, N., & Maldina, E. Y. (2017). Strategi Pemasaran

Islami Dalam Meningkatkan Penjualan Pada Butik

Calista. I-Economic, 3(1), 80–81.

Nasution, F. B., & Bazin, N. E. N. (2018). E-government

maturity model to support systemdynamicsinpublic

policymaking. International Conference on Electrical

Engineering, Computer Science and Informatics

(EECSI), 2018-Octob, 464–471. https://doi.org/10.11

09/EECSI.2018.8752921

Prakesakwa, A., Suryani, A., & Gustriansyah, R. (2019).

Sistem Pendukung Keputusan untuk Subsidi Biaya

Perbaikan Kerusakan Kontainer Menggunakan Naive

Bayes. Jurnal Teknologi Dan Sistem Komputer, 7(3),

98. https://doi.org/10.14710/jtsiskom.7.3.2019.98-102

Pramanik, D., Haldar, A., Mondal, S. C., Naskar, S. K., &

Ray, A. (2017). Resilient supplier selection using AHP-

TOPSIS-QFD under a fuzzy environment.

International Journal of Management Science and

Engineering Management, 12(1), 45–54.

https://doi.org/10.1080/17509653.2015.1101719

Rakhshan, S. A. (2017). Efficiency ranking of decision

making units in data envelopment analysis by using

TOPSIS-DEA method. Journal of the Operational

Research Society, 68(8), 906–918. https://doi.org/10.10

57/s41274-017-0237-0

Reddy, A. S., Kumar, P. R., & Raj, P. A. (2019). Entropy-

based fuzzy TOPSIS framework for selection of a

sustainable building material. International Journal of

Construction Management, 0(0), 1–12.

https://doi.org/10.1080/15623599.2019.1683695

Schnall, R., Rojas, M., Bakken, S., Brown, W., Carballo-

Dieguez, A., Carry, M., Gelaude, D., Mosley, J. P., &

Travers, J. (2016). A user-centered model for designing

consumer mobile health (mHealth) applications (apps).

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

100

Journal of Biomedical Informatics, 60, 243–251.

https://doi.org/10.1016/j.jbi.2016.02.002

Sun, W., Li, D., & Liu, P. (2018). A decision-making

method for Sponge City design based on grey

correlation degree and TOPSIS method. Journal of

Interdisciplinary Mathematics, 21(4), 1031–1042.

https://doi.org/10.1080/09720502.2018.1456826

Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M.

(2018). Selection of normalization technique for

weighted average multi-criteria decision making. IFIP

Advances in Information and Communication

Technology, 521, 43–52. https://doi.org/10.1007/978-

3-319-78574-5_4

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, Somantri, O., Abda’U, P. D., Faiz, M. N.,

Maharrani, R. H., Prasetya, N. W. A., Susanto, A.,

Purwaningrum, S., & Romadoni, A. (2020). A support

systemfor acceptingstudent assistanceusing analytical

hierarchy process and simple additive weighting.

Journal of Physics: Conference Series.

https://doi.org/10.1088/1742-6596/1430/1/012034

Wanti, Linda Perdana, Azroha, I. N., & Faiz, M. N. (2019).

Implementasi User Centered Design Pada Sistem Pakar

Diagnosis Gangguan Perkembangan Motorik Kasar

Pada Anak Usia Dini. Media Aplikom, 11(1), 1–10.

Wanti, Linda Perdana, 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, Linda Perdana, 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.v10i5.pp4992-5000

Wedagama, D. (2010). Determining Regencial Road

Handling Priority Using Fuzzy Analytic Hierarchy

Process (FAHP) and TOPSIS Method (Case Study:

Badung Regency - Bali). Jurnal Teknik Sipil ITB, 17(2),

143–152.

Yesmaya, V., Ronald, A., & Hidajat, M. (2018). Property

exhibition decision support system based on web

application. Telkomnika (Telecommunication

Computing Electronics and Control), 16(2), 766– 770.

https://doi.org/10.12928/TELKOMNIKA.v16i2.760 1

Yu, E., Wang, Y., Fu, Y., Chen, D., & Xie, M. (2020). AHP

integrated TOPSIS and VIKOR methods with

Pythagorean fuzzy sets to prioritize risks in self-driving

vehicles. Optics Communications, 126175.

https://doi.org/10.1016/j.asoc.2020.106948

Yuwono, T., Setiawan, N. A., Nugroho, H. A., Persada, A.

G., Prasojo, I., Dewi, S. K., & Rahmadi, R. (2015).

Decision support systemfor heart diseasediagnosing

using K-NN algorithm. International Conference on

Electrical Engineering, Computer Science and

Informatics (EECSI), 2(August), 160–164.

https://doi.org/10.11591/eecsi.2.544

Combined Method to Determine Shrimp Pond Cultivation Land

101