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Knowledge Engineering and Data Science (KEDS) pISSN 2597-4602 
Vol 2, No 1, Juni 2019, pp. 19–30 eISSN 2597-4637 
 
 
 

 
https://doi.org/10.17977/um018v2i12019p19-30   
©2019 Knowledge Engineering and Data Science | W : http://journal2.um.ac.id/index.php/keds | E : keds.journal@um.ac.id 
This is an open access article under the CC BY-SA license (https://creativecommons.org/licenses/by-sa/4.0/) 

Selection of Marine Security Policy  
using Fuzzy-AHP TOPSIS Hybrid Approach 

Hozairi a, 1, *, Buhari a, 2, Heru Lumaksono b, 3, Marcus Tukan c, 4 

a Informatics Eng. Study Program, Islamic University of Madura 
Jl.PP.Miftahul Ulum Bettet, Pamekasan 6931, Indonesia 

b Department of Shipbuilding Engineering, Shipbuilding Institute of Polytechnic Surabaya 
Jl. Teknik Kimia ITS, Surabaya 60111, Indonesia 
c Faculty of Engineering, University of Pattimura 

Jl. Ir M Putuhena, Ambon 97233, Indonesia 

1 dr.hozairi@gmail.com*; 2 buharinahrawi@gmail.com; 3 heruppns@gmail.com; 4 marcustucan@gmail.com 

* corresponding author 

 

 

I. Introduction 

Indonesia is an archipelago with an area of ocean exceeding mainland. Geographically, Indonesia 
is located between two continents and two oceans, and has a large wealth of natural resources. As an 
archipelagic country, Indonesia should also be called a maritime country. However, addressing 
Indonesia as a maritime country seems inappropriate because the development between the land and 
the sea is not balance [1][2]. 

For that reason, since 2014, Indonesia has focused on organising the maritime affairs for the 
nation's prosperity [3]. The Indonesian government through Presidential Regulation No. 178 of 2014, 
established a Marine Security Agency (Bakamla), which was previously named the Marine Security 
Coordination Agency (Bakorkamla) [4]. As consequence, the Indonesian Government is required to 
choose the right Indonesian maritime security policy so as to be able to realize the ideals of Indonesia 
as the World Maritime Axis [5][6]. Choosing an Indonesian maritime security policy is not easy 
because it has to consider many criteria. Therefore a Decision Support System (DSS) [7] is needed to 
recommend the most suitable maritime policies [8]. 

This study aims to select the Indonesian maritime security policy by considering many criteria in 
each decision alternative. The process of selecting Indonesia's marine security policy is not easy 
because it includes complex problems and cannot be solved by linear programming methods. This 
problem is a multi criteria problem, requires a DSS approach, namely Multi Criteria Decision Making 
(MCDM) to solve it. MCDM determines the best of many alternatives based on specific criteria. 
Criteria are usually available in the form of measurements, rules or standards, are used in decision 
making process [9][10][11]. 

ARTICLE INFO A B S T R A C T   

Article history: 
Received 3 December 2019 
Revised 24 January 2019 
Accepted 22 April 2019 
Published online 23 June 2019 
 

The research was focused on the integration of Fuzzy set theory with Analytic 
Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal 
Solution (TOPSIS) to choose the optimum maritime security policy to achieve 
Indonesia recognition as the world's maritime axis. The method used is AHP with 
fuzzy based enhancement. Here, the weight of each criterion is calculated to overcome 
the criticism of the scale of unbalanced rating, uncertainty, and inaccuracy in the 
pairwise of comparison process. The best recommendation for Indonesian maritime 
policies is multi task single agency which is greatly infuenced by several factors such 
as technology, regulations, infrastructure, economic, politic, and socio-culture.  The 
finding shows that the hybrid approach is able to produce the best recommendation 
for Indonesian maritime security policy.  

This is an open access article under the CC BY-SA license 
(https://creativecommons.org/licenses/by-sa/4.0/).

Keywords: 
Marine security policy 
Global maritime axis 
Fuzzy-AHP 
TOPSIS  



20 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 

Another major advantage of MCDM techniques is their ability to analyze quantitative and 
qualitative criteria simultaneously. Many techniques and methodologies are reported in the literature. 
Some popular approaches are Analytical Hierarchy Process (AHP) [12], Analytic Network Process 
(ANP), Technique for Preference by Ideal for Ideal Solution (TOPSIS) [13], Elimination and Choice 
Translation Reality (ELECTRE) [14][15], Preference Ranking for Organization Method for 
Enrichment Evaluation (PROMETHEE) [16], Decision making trial and evaluation laboratory 
(DEMATEL), and Vse Kriterijumska Optimizacija I Komprominsa Resenje (VIKOR). Each 
technique has its own strengths and weaknesses. Therefore, a hybrid technique could be a solution on 
improving the performance of these stand-alone approaches.  

In this study, an integrated model of fuzzy-AHP and TOPSIS was established to provide a phased 
methodology for selecting Indonesia's maritime security policy in accordance with Presidential 
Regulation No. 178 of 2014. This model was then applied in case studies to demonstrate its application 
in real-world pilot studies and prove its reliability. Fuzzy-AHP has good ability to resolve 
uncertainties and ambiguities in various MCDM situations [13][17][18][19]. On the other hand, 
TOPSIS is a model that is efficient in handling reasonable attributes and there is no limit to the number 
of criteria, sub-criteria or alternatives [20][21][22]. Thus, Fuzzy-AHP and TOPSIS integration should 
provide a good basis for the analysis of complex decision problems [23][24][25][26]. The easy 
programmed hybrid technique is expected to overcome the complex problem of maritime decision 
making. 

II. Methods 

This study aims to analyse the Indonesian marine security model by considering many criteria on 
each decision alternative. Based on the purpose, MCDM can be divided into 2 (two) models: Multi 
Atribute Decision Making (MADM), Multi Objective Decision Making (MODM). Often MADM and 
MODM are used to solve multi-attribute and multi-objective problems. MCDM method developed in 
this research is Fuzzy AHP and TOPSIS. In general, the stages of this research process can be seen in 
Figure 1. 

A. Fuzzy AHP 
The use of AHP in the Multi Criteria Decision Making (MCDM) problem is often criticized in 

light of the inadequacy of this AHP approach to overcome the uncertain factor experienced by the 
decision maker when it must provide a definite value in a pairwise comparison matrix. Therefore, to 
overcome the weakness of existing AHP then developed a method called fuzzy AHP. AHP fuzzy 
method is a combination of AHP method with fuzzy approach. 

 

Fig. 1. The framework of fuzzy based AHP-TOPSIS multi criteria decision analysis 



 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 21 

 

The fuzzy AHP method uses Triangular Fuzzy Number (TFN). TFN is used to describe linguistic 
variables with certainty. TFN is symbolized by 𝑀 = (𝑙, 𝑚, 𝑢) where 𝑙 is the lowest value, 𝑚 is the 
middle value, and 𝑢 is the top. The AHP and TFN ratings are used for purposes of the pairwise 
comparison matrix, as shown in Table 1. 

If we suppose there are 2 (two) Triangular Fuzzy Number (TFN) that is 𝑀1 = 𝑙1, 𝑚1, 𝑢1 and 𝑀2 = 
𝑙2, 𝑚2, 𝑢2, then TFN arithmetic operation is: 

𝑀 + 𝑀 = (𝑙 + 𝑙 , 𝑚 + 𝑚 , 𝑢 + 𝑢 ) (1) 

𝑀 𝑀 = (𝑙 𝑙 , 𝑚 𝑚 , 𝑢 𝑢 ) (2) 

𝑀 = (1 𝑢 ,
1

𝑚 ,
1

𝑙 )  (3) 

Fuzzy Analytic Hierarcy Process (FAHP) stage are: defining the fuzzy synthetic extend value, 
confidence level, the level of probability for a convex fuzzy numbers, and normalization of weighted 
vector 

1) Fuzzy synthetic extend value 

Define the fuzzy synthetic extent value for i-objects like the following equation: 

𝑆 =  ∑ 𝑀   ∑ ∑ 𝑀   (4) 

To get ∑ 𝑀  , then a fuzzy sum operation is performed from the value of 𝑚 in a pairwise matrix 

of comparison as can be seen in the following equation: 

∑ 𝑀 =  ∑ 𝑙 , ∑ 𝑚 , ∑ 𝑢  (5) 

To obtain equation (6): 

∑ ∑ 𝑀  (6) 

Then summed on 𝑀  as can be seen in Equation (7): 

∑ ∑ 𝑀 = ∑ 𝑙 , ∑ 𝑚 , ∑ 𝑢  (7) 

Then, to obtain the inverse of equation (7) can be done by using TFN arithmetic operation on 
Equation (3) which resulted in equation (8): 

∑ ∑ 𝑀 =  
∑

,
∑

,
∑

 (8) 

2) Confidence level  

If there are two fuzzy numbers M1 = (l1, m1, u1) and M2 = (l2, m2, u2), then the confidence level of 
M1 = (l1, m1, u1) ≥ M2 = (l2, m2, u2) can be defined as follows: 

𝑉(𝑀 ≥  𝑀 ) = sup 𝑚𝑖𝑛 𝜇𝑀 (𝑥), 𝜇𝑀 (𝑦)  (9) 

If M1 and M2 of the convex fuzzy number are obtained the following conditions: 

𝑉(𝑀 ≥  𝑀 ) = 1 𝑖𝑓𝑓 𝑚 ≥  𝑚  (10) 

𝑉(𝑀 ≥  𝑀 ) = ℎ𝑔𝑡 (𝑀  ∩  𝑀 ) =  𝜇 (𝑑) (11) 

Table 1. Triangular fuzzy scale of preference 

Linguistic Term AHP Scale Triangular Fuzzy Number (TFN) 

Absolute 9 (7, 9, 9) 

Very Strong 7 (5, 7, 9) 

Fairly Strong 5 (3, 5, 7) 

Weak 3 (1, 3, 5) 

Equal 1 (1, 1, 3) 
 



22 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 

The confidence level of the fuzzy number can be obtained by the equation: 

𝑉(𝑀 ≥  𝑀 ) =  

1 , 𝑖𝑓 𝑚 ≥ 𝑚
0 , 𝑖𝑓 𝑙 ≥ 𝜇

(  )

(  )  (  )
 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

 (12) 

Comparison of two fuzzy numbers can be seen in Figure 2 which shows that d is the highest 
intersection point coordinate between µM1 and µM2, to compare M1 = (l1, m1, u1) and M2 = (l2, m2, u2) 
requires value from 𝑉(𝑀 ≥  𝑀 ) and 𝑉(𝑀 ≥  𝑀 ). 

3) Level of probability 

The level of probability for a convex fuzzy numbers better than than k convex fuzzy numbers M1 
(i=1,2,3,...,k) can be defined as follows: 

𝑉(𝑀 ≥ 𝑀 , 𝑀 , . . . . . . , 𝑀 )  (13) 

= 𝑉 [(𝑀 ≥ 𝑀 ) and (𝑀 ≥ 𝑀 ) 𝑎𝑛𝑑 . . . . (𝑀 ≥ 𝑀 )]  

= 𝑚𝑖𝑛 𝑉(𝑀 ≥ 𝑀 ), 𝑖 = 1,2, . . . , 𝑘  

It is assumed that: 

𝑑’(𝐴 )  =  𝑚𝑖𝑛 𝑉(𝑆 ≥ 𝑆 )  (14) 

for k=1,2,...,n; k≠I, then the weight of the vector is defined as follows: 

 𝑊’ = 𝑑’(𝐴 ), 𝑑’(𝐴 ), . . . . , 𝑑’(𝐴 )  (15) 

4) Normalization of weighted vector 

Normalization of weighted vector in equation (15) becomes: 

𝑊 = 𝑑(𝐴 ), 𝑑(𝐴 ), . . . . , 𝑑(𝐴 )  (16) 

where W is not a fuzzy number. 

B. TOPSIS 
TOPSIS is one of the main MCDM techniques. This approach is based on the best alternative that 

has the closest distance from the positive ideal solution (PIS) and the farthest distance from the 
negative ideal solution (NIS). It has been widely applied in many fields of research related to the 
selection of various alternatives and risk analysis because of the rationality, logic and simplicity of the 
method. 

To solve multi criteria problems with the TOPSIS method there are several steps that must be 
completed, namely: 

Step 1 Normalise the decision matrix 

𝑟 =
∑

 , 𝑗 = 1,1, … . , 𝑗;  𝑖 = 1,2, … . , 𝑛 (17) 

 

Fig. 2. The framework of fuzzy based AHP-TOPSIS multi criteria decision analysis 



 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 23 

 

Step 2 Apply weight to the normalized decision matrix by multiplying the normalized matrix with 
the weights of the criteria: 

𝑣 = 𝑤 ∗ 𝑟 = 1,2, … . . , 𝑗; 𝑖 = 1,2, … . , 𝑛 (18) 

Step 3 Determine both PIS (maximum values) and NIS (minimum values) as: 

𝐴 = {𝑣 , 𝑣 , … … , 𝑣 } (19) 

𝐴 = {𝑣 , 𝑣 , … … , 𝑣 }  

Step 4 Calculate the distance of each alternative from PIS and NIS: 

𝐷 = ∑ (𝑣 − 𝑣 ) , 𝑗 = 1,2, … , 𝑗 (20) 

𝐷 = ∑ (𝑣 − 𝑣 ) , 𝑗 = 1,2, … , 𝑗  

Step 5 Calculate the closeness coefficient of each alternative (Ci) relative to its distance from PIS 
and NIS: 

𝐶 =  (21) 

Step 6 Compare the Ci values to determine the ranking of alternatives. 

This research begins by agreeing on the criteria that are considered in the selection of a suitable 
Indonesian maritime security policy which then later determine the alternatives that will be rated as 
Table 2. 

The proposed approach consists of three steps. In the first stage, compiling the policy criteria is 
taken into consideration to determine the structured decision hierarchy. Decision hierarchy should be 
approved by the policy-making team with several considerations of internal factors and external 
factors. The next process assess the criteria using Fuzzy AHP. As explained in section 2, linguistic 
values are used to determine the weight criteria. In the third stage, the Indonesian maritime security 
policy model is ranked using the TOPSIS procedure. Ranking is based on Ci values in the order of the 
high. The maximum Ci value are selected as the best policy model. The schematic diagram of the 
proposed model is showed in in Figure 3. 

III. Results and Discussions 

The integrated maritime security policy requires the involvement of many actors in decision 
making such as the state sector and the civil sector. The concept of the World Maritime Axis is the 
concept of Maritime Security itself with prominent characteristics and focuses on several aspects: 
national, economic, environmental, and human security. 

Table 2. Maritime security policy 

Code Description  

Security policy code 

C1 Politics 

C2 Economics 

C3 Social-culture 

C4 Technology 

C5 Infrastructure 

C6 Regulation 

Alternatif security policy code 

A1 Multy Agency Single Task 

A2 Multy Agency Multy Task 

A3 Single Agency Multy Task 

A4 Single Agency Single Task 
 



24 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 

Indonesia has twelve law enforcement agencies in the sea, of the twelve there are six institutions 
that have patrol boats as law enforcement tools at sea by conducting patrols at sea, namely: the Navy, 
Indonesian National Police, Ministry of Defense, Ministry of Maritime Affairs and Fisheries, Ministry 
of Transportation and Customs and Excise. There are six other marine law enforcement agencies that 
do not have patrol boats, namely: Ministry of Foreign Affairs, Ministry of Home Affairs, Ministry of 
Law and Human Rights, Attorney General's Office, Ministry of Finance and State Intelligence 
Agency. 

Therefore, the proposed method analyzes the situation and simplify the decision-making process. 
For this purpose, a team of ten: Indonesian Ocean Security Agency, Navy, Indonesian National Police, 
Ministry of Defense, Ministry of Maritime Affairs and Fisheries, Ministry of Transportation, Customs 
and Excise, and author were formed. The experiences and perspectives of these members are used 
throughout the entire study by following the described procedures. 

A. Identification of criteria 
Important criteria for comparison of the Indonesian marine security policy model are determined 

by a team of experts based on their background and experience. The team agreed that the problem has 
six important criteria as shown in Figure 3. 

The next step is creating a comparison between criteria to determine the most important criteria. 
The assessment process between criteria uses the Fuzzy AHP approach. The comparison scheme 
between criteria can be seen in Table 3. Decision hierarchy includes three levels: The overall objective 
"selection Indonesian maritime security policy" as the first level of the hierarchy, the criteria are on 
the second level and third level as the alternative.  

B. Weight criteria and alternatives 
The calculation of the fuzzy synthesis value leads to estimate the overall value of each desired 

criterion and alternative as presented in Table 3. Afterwards, the matrix elements in Table 3 is divided 
by the values in the row. The next stage sums the each line values, then divided it by the number of 
criteria to look for the Eigen Vector or the weight of each criterion. The calculation results can be seen 
in Table 4.  

 

Fig. 3. Hierarchy of selection of Indonesian maritime security policies 

Table 3. The pairwise comparison matrix for criteria 

Criteria 
  C1     C2     C3     C4     C5     C6   

L M U L M U L M U L M U L M U L M U 

C1 1.00 1.00 3.00 1.00 0.33 0.20 3.00 5.00 7.00 0.20 0.14 0.11 1.00 0.33 0.20 0.33 0.20 0.14 

C2 1.00 3.00 5.00 1.00 1.00 3.00 3.00 5.00 7.00 0.33 0.20 0.14 1.00 1.00 3.00 1.00 0.33 0.20 

C3 0.33 0.20 0.14 0.33 0.20 0.14 1.00 1.00 3.00 0.20 0.14 0.11 0.33 0.20 0.14 0.20 0.14 0.11 

C4 5.00 7.00 9.00 3.00 5.00 7.00 5.00 7.00 9.00 1.00 1.00 3.00 5.00 7.00 9.00 1.00 3.00 5.00 

C5 1.00 3.00 5.00 1.00 1.00 0.33 3.00 5.00 7.00 0.20 0.14 0.11 1.00 1.00 3.00 1.00 0.33 0.20 

C6 3.00 5.00 7.00 1.00 3.00 5.00 5.00 7.00 9.00 1.00 0.33 0.20 1.00 3.00 5.00 1.00 1.00 3.00 
 



 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 25 

 

After the value of the number of rows and columns is obtained, the value of fuzzy synthesis of 
each criterion (SKi) with i=1, 2, …4 is calculated as follow 

SKi = (Number of rows (L, M, U) * Invers (L, M, U)) 

SK1 = ((6.53, 7.01, 10.65) * Invers (0.01, 0.01, 0.02))   = (0.05, 0.09, 0.19) 

SK2 = ((7.33, 10.53, 18.34) * Invers (0.01, 0.01,0.02))  = (0.06, 0.13, 0.33) 

SK3 = ((2.40, 1.89, 3.65) * Invers (0.01, 0.01, 0.02))  = (0.02, 0.02, 0.33) 

SK4 = ((20.00, 30.00, 42.00) * Invers (0.01, 0.01, 0.02))  = (0.17, 0.38, 0.76) 

SK5 = ((7.20, 10.48, 15.64) * Invers (0.01, 0.01, 0.02))  = (0.06, 0.13, 0.28) 

SK6 = ((3.73, 2.15, 1.77) * Invers (0.01, 0.01, 0.02))  = (0.04, 0.03, 0.03) 

The calculation of fuzzy synthesis values can be summarised in Table 5. 

Determination of vector value (V) and ordinate value of defuzification uses a fuzzy approach. It is 
the minimum implication (min) fuzzy function. Afterwards, we will get the ordinate value of 
defuzification (d') which is the minimum d value. Based on Table 5, the values of vectors and ordinate 
defective values of each criterion are obtained. For instance, the first criteria is Politic (K1), the value 
of vector is: 

(VK1) ≥ (VK2, VK3, VK4, VK5, VK6) 

Because of the value m1 ≥ m2 and u2 ≥ l1 then VK1 ≥ VK2: 

VK1 ≥ VK2 = 0.86 

VK1 ≥ VK3 = 1 

VK1 ≥ VK4 = 0.67 

VK1 ≥ VK5 = 0.83 

VK1 ≥ VK6 = 0.73 

Afterwards, the next stage obtained value d’(VK1). Once finished, the calculation is repeated for 
the other criteria. 

d’(VK1) = min (0.86, 1, 0.67, 0.83, 0.73) 

d’(VK1) = 0.67 

Table 4. Results calculation of the number of rows. number of columns and inverse values 

TFN 
(M) 

Number of Rows Number of 
columns 

Inverse 

C1 C2 C3 C4 C5 C6 

L 6.53 7.33 2.4 20 7.2 12 55.47 0.01 

M 7.01 10.53 1.89 30 10.48 19.33 79.24 0.01 

U 10.65 18.34 3.65 42 15.64 29.20 119.49 0.02 
 

Table 5. Results of fuzzy synthesis calculations (Si) 

Criteria 
Si 

L M U 

C1 0.05 0.09 0.19 

C2 0.06 0.13 0.33 

C3 0.02 0.02 0.07 

C4 0.17 0.38 0.76 

C5 0.06 0.13 0.28 

C6 0.10 0.24 0.53 
 

 



26 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 

Based on the ordinate values K1, K2, K3, K4, K5 and K6, then the value of the vector weight can be 
determined as follow: 

Number of Vectors= (0.67 + 0.71 + 0.62 + 1 + 0.71 + 0.81) = 3.52 

As a result, the value of the vector weight in the first criterion is obtained. 

VK1 = 0.67/3.52 = 0.19 

The calculation result of other criteria is also presented in Table 6. 

Finally, the total value of the vector weights in each criterion is summed  

W = (VK1 + VK2 + VK3 + VK4 + VK5 + VK6) 

Thus,  

W = (0.19 + 0.20 + 0.18 + 0.28 + 0.20 + 0.23) = 1.28. 

Normalize the value of the vector weight for the first criterion is  

WK1 = 0.19/1.28 = 0.15 

Third column of Table 6 shows the calculation result of the other criteria.  

The results of the calculation of criteria weights using the Fuzzy AHP method obtained the 
following values of weighting: Politics = 0.15; Economy = 0.16; Social and Cultural = 0.14; 
Technology = 0.22; Infrastructure = 0.16; and Policy = 0.18. Here, the most influential factor is 
technology. 

The next stage spreads questionnaires to several respondents (Ministry of Maritime Affairs and 
Fisheries, Indonesian Navy, Ministry of Defense, Ministry of Transportation, Police and Academics) 
who understand and have the authority of Indonesian maritime security policies. The questionnaire is 

Table 6. The calculation result of weight of each criterion 

Criteria D’(VK) VK WK 

1 0.67  0.19  0.15 

2 0.71  0.2 0.16 

3 0.62  0.18 0.14 

4 1  0.28 0.22 

5 0.71  0.2 0.16 

6 0.81 0.23 0.18 

Total 3.52 1.28  
 

Table 7. Questionnaire value 

Variables Code Value 

Very Poor VP 1 

Poor P 2 

Fair F 3 

Good G 4 

Very Good VG 5 
 

Table 8. Recapitulation of questionnaire results between criteria and decision alternative 

Alternative C1 C2 C3 C4 C5 C6 

A1 4.00 3.00 4.00 4.00 4.00 4.00 

A2 3.00 2.00 3.00 2.00 4.00 2.00 

A3 5.00 4.00 4.00 5.00 4.00 5.00 

A4 2.00 5.00 3.00 4.00 4.00 3.00 

Average 3.50 3.50 3.50 3.75 4.00 3.50 
 



 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 27 

 

used to assess the consistency of each alternative as detailed in Table 7. About 100 people fill the 
questionnaire. The result of the questionnaire recapitulation is shown in Table 8. 

After obtained the comparative value of criteria and alternatives, the next step is looking for the 
value of squares and the roots of the assessment results using TOPSIS. Table 9 captures the calculation 
result. The process is followed by the multiplication of every element in Table 8 with its root in Table 
9 to get a TOPSIS normalization matrix in Table 10.  

The next step is to get a weighted normalization matrix by multiplying the TOPSIS normalization 
matrix with the weighted matrix of criteria (Table 6). The result is a weighted normalization matrix 
of TOPSIS with the Fuzzy-AHP can be seen in Table 11. 

Table 9. Result of calculation Value of squares and roots 

Criteria C1 C2 C3 C4 C5 C6 

Square 54.00 54.00 50.00 61.00 64.00 54.00 

Root 7.35 7.35 7.07 7.81 8.00 7.35 
 

Table 10. Normalization Matrix 

Alternative C1 C2 C3 C4 C5 C6 

A1 0.54 0.41 0.57 0.51 0.50 0.54 

A2 0.41 0.27 0.42 0.26 0.50 0.27 

A3 0.68 0.54 0.57 0.64 0.50 0.68 

A4 0.27 0.68 0.42 0.51 0.50 0.41 
 

Table 11. The weighted normalization matrix 

Alternative C1 C2 C3 C4 C5 C6 

A1 0.081 0.064 0.078 0.071 0.078 0.098 

A2 0.061 0.043 0.059 0.035 0.078 0.049 

A3 0.101 0.085 0.078 0.088 0.078 0.123 

A4 0.040 0.106 0.059 0.071 0.078 0.074 
 

Table 12. The value of alternative distance of positive and negative solution 

Value C1 C2 C3 C4 C5 C6 

Maximum 0.101 0.106 0.078 0.088 0.078 0.123 

Minimum 0.040 0.043 0.059 0.035 0.078 0.049 
 

Table 13. Quadratic value of alternatives 

Square Value Benefit Cost 

A1 0.003 0.006 

A2 0.014 0.000 

A3 0.000 0.014 

A4 0.007 0.006 
 

Table 14. Root values on alternatives 

Root Value Benefit Cost 

A1 0.056 0.078 

A2 0.120 0.020 

A3 0.021 0.119 

A4 0.082 0.077 
 



28 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 

The next step is finding the value of positive solution and negative solution and determining the 
distance between values, as showed in Table 12. The next stage, determines the square and root values 
of positive ideal values (PIS) and negative ideal values (NIS) which is presented in Table 13 and Table 
14 respectively.  

After obtaining the square and root values of PIS and NIS. Then the last step in the TOPSIS 
calculation is to find the preference value for each given alternative. If a larger Vi value indicates that 
the alternative Ai is preferred. The preference values of each alternative are obtained as follows: 

The preference value of Multy Agency Single Task Model 

𝑉 =  
.

. .
= 0.583  

The preference value of Multy Agency Multy Task Model 

𝑉 =  
.

. .
= 0.144  

The preference value of Single Agency Multy Task Model 

𝑉 =  
.

. .
= 0.483  

The preference value of Single Agency Single Task Model 

𝑉 =  
.

. .
= 0.848  

Having obtained the priority value on each alternative, then carried out the process of 
normalization of the decision value, shown in Table 15.  

Based on the analysis of the interests of some concepts of marine security, it turns out the most 
suitable concept to be implemented in the State of Indonesia. First is the concept of Single Agency 
Multy Task, where all policies in handling law enforcement at sea are in one institution. Second is the 
Multy Agency Single Task, where there are more than one institution that interacts together to achieve 
or to solve similar problems. The third is Single Agency Single Task, where there is one institution 
that regulates and implements the regulations. This alternative is not possible to be applied because 
Indonesia already has many institutions with maritime authority of security. Fourth is Multy Agency 
Multy Task, where there are many institutions that have the duty and authority of oversight and marine 
security. The last concept has many weaknesses, especially the inter-agency sectoral ego. 

Table 15. Priority value of alternative selection of Indonesian marine security concept 

Alternative Priority Value Normalize Priority Values 

A1 0.583 0.283 

A2 0.144 0.070 

A3 0.848 0.412 

A4 0.483 0.235 
 

 

Fig. 4. Results of priority model of marine security Indonesia 

0,283

0,070

0,412

0,235

0,000 0,050 0,100 0,150 0,200 0,250 0,300 0,350 0,400 0,450

A1

A2

A3

A4

Priority Value Marine Security Concept using Fuzzy-AHP TOPSIS 

M
ar

in
e 

Se
cu

ri
ty

 C
on

ce
pt



 Hozairi et al. / Knowledge Engineering and Data Science 2019, 2 (1): 19–30 29 

 

Figure 4 illustrates that the concept of "Single Agency Multy Tasks" has greatest contribution on 
addressing marine security and security enforcement issues in Indonesia. In contrast, other concepts 
may difficult to implement and may cause some problems such as overlapping authority, conflicts 
between law enforcement officers, and the absence of command and control units at the sea. The 
implementation of the Single Agency Multy Tasks system in Indonesia, can be done by optimizing 
the the synergy between authority, strength and ability of stakeholders.  

IV. Conclusions 

In this paper, using Fuzzy AHP and TOPSIS, an integrated two-step model was established to 
evaluate in choosing the optimal maritime security policy to realize Indonesia as the world's maritime 
axis. Evaluation is based on several criteria, namely: politics, economy, social and culture, technology, 
infrastructure and policy. The Fuzzy-AHP found that the best criteria that greatly affect the 
improvement of marine security is technology, is followed by regulations, infrastructure, economic, 
politic, and socio-culture. The order of priority decisions of Indonesian maritime security policies 
based on TOPSIS are Multy Task Single Agency (0.412), Single Task Multy Agency (0.283), Single 
Task Single Agency (0.235), and Multy Multy Agency Task (0.070). The best recommendation for 
Indonesia marine security policy is "Multy Task Single Agency". The concept is believed to make a 
major contribution in overcoming various problems in the enforcement of security and safety laws at 
Indonesian sea. The concept requires a single institution to give one command, related to maritime 
security policy. 

Acknowledgment 

This research is supported by the Ministry of Research, Technology and Higher Education of the 
Republic of Indonesia through the National Strategic Research Grant. 

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