Indonesia has many E-Commerce companies that are in great demand by the people in Indonesia. COVID-19 has led to an increase in people's transactions using E-Commerce. E- Commerce, which has not been able to capture market share in Indonesia, is competing to increase the number of transactions. E-Commerce that already has regular customers will continue to maintain the quality and quantity of its transactions. E-Commerce customers also have their own preferences in choosing the E-Commerce company that will be used for transactions. The many criteria that are taken into account by customers sometimes confuse customers to be able to choose the most appropriate E-Commerce that best suits customer desires. Decision support systems can be used to help customers make their choices. The method used is SWARA-ARAS. There are 8 criteria and 6 alternatives used in this DSS. The methodology in this study uses the CRISP-DM Framework. Based on the 6 alternatives tested using SWARA-ARAS, Lazada (X4) became the favorite e-commerce in Indonesia with a value of 0.9193 followed by Tokopedia with a value of 0.9155 and Shoopee with a value of 0.9045. JD.ID became the last position with a value of 0.8753.

1. 69
Determination of Favorite E-Commerce in Indonesia in a Decision
Support System Using the SWARA-ARAS Method
Kadek Oky Sanjayaa,1
, Gede Surya Mahendrab,2,*
a
Program Studi Sistem Informasi, Fakultas Teknologi dan Sains, Universitas Hindu
Indonesia, Jl. Sangalangit, Kota Denpasar, Bali
b
Program Studi Teknik Informatika , STMIK STIKOM Indonesia, Jl. Tukad Pakerisan No.97,
Kota Denpasar, Bali.
1
kadekoki@unhi.ac.id; 2
gede.mahendra@stiki-indonesia.ac.id*
* Corresponding author
ABSTRACT (10PT)
Indonesia has many E-Commerce companies that are in great demand by the people in
Indonesia. COVID-19 has led to an increase in people's transactions using E-Commerce. E-
Commerce, which has not been able to capture market share in Indonesia, is competing to
increase the number of transactions. E-Commerce that already has regular customers will
continue to maintain the quality and quantity of its transactions. E-Commerce customers also
have their own preferences in choosing the E-Commerce company that will be used for
transactions. The many criteria that are taken into account by customers sometimes confuse
customers to be able to choose the most appropriate E-Commerce that best suits customer
desires. Decision support systems can be used to help customers make their choices. The
method used is SWARA-ARAS. There are 8 criteria and 6 alternatives used in this DSS. The
methodology in this study uses the CRISP-DM Framework. Based on the 6 alternatives tested
using SWARA-ARAS, Lazada (X4) became the favorite e-commerce in Indonesia with a value
of 0.9193 followed by Tokopedia with a value of 0.9155 and Shoopee with a value of 0.9045.
JD.ID became the last position with a value of 0.8753.
Keywords: SWARA, ARAS, E-Commerce
I. Introduction
Given that 96% of internet users in
Indonesia have used e-commerce, it is hoped
that e-commerce in Indonesia can develop
rapidly and become a leader in the Southeast
Asian market[1]. According to 2019 data, the
value of e-commerce transactions in
Indonesia is US $ 21 billion, and is estimated
to reach US $ 82 billion in transaction value
by 2025[2]. The current situation regarding
the impact of COVID-19 on the global e-
commerce industry shows that daily web
traffic has increased significantly by more
than 50%, which may be due to the social
and physical restrictions experienced by
consumers[3]. The government also
provides protection to consumers when
transacting through e-commerce through
preventive and repressive methods[4].
This situation allows e-commerce to
penetrate deeper and gain growth in the
Indonesian market. E-commerce is also
required to always maintain its own

2. 70
excellence in providing quality products and
services. Indonesia has a lot of e-commerce,
and Indonesia has the 6 largest e-commerce
companies consisting of Blibli, Bukalapak,
JD.id, Lazada, Shopee, and Tokopedia[5].
Behind the convenience provided, there are
still several negative factors, such as product
mismatches, delivery problems, security of
payment methods and customer service. Due
to intense competition, many consumer
considerations, it is difficult to choose
between the same products but the prices
offered are different, so that consumers are
still confused about choosing the right and
trusted e-commerce transaction. To solve this
problem, a decision support system (DSS)
can be used to provide advice in choosing the
right e-commerce. DSS itself is an effective
system that can assist users in making
complex decisions[6]. This system uses
decision rules, analysis models,
comprehensive databases, and decision
maker knowledge[7]–[10].
In this study, the method used was a
combination of SWARA-ARAS. SWARA
method, done by the weighting method, the
relative importance and the initial
prioritization of alternatives for each attribute
are determined by the opinion of the decision
maker, and then, the relative weight of each
attribute is determined[11]–[14]. The ARAS
method aims to select the best alternative
based on a number of attributes and the final
ranking of alternatives is made by
determining the utility degree of each
alternative[15]–[17]. In previous studies, the
combination of these methods in the DSS has
been applied well, decision makers can weigh
the criteria and greatly influence the results
of recommendations [18]–[20]. Regarding
the choice of e-commerce, several studies
have compared different alternative criteria
and methods, and achieved good results[21]–
[24].
Therefore, this study aims to be able to
perform calculations manually a
combination of the SWARA-ARAS
methods. The urgency of this research, if
not realized, could result in obstruction of
the development of the DSS method
which can only reach the calculation and
design stages manually, thus hindering
innovation in the DSS field. Based on the
background previously described, it is
necessary to realize a combination of the
SWARA-ARAS methods to determine the
best e-commerce using DSS.
II. Methodology
Fig. 1.CRISP-DM Model
The research method used in this
study follows the various stages of the
CRISP-DM model[25]–[27]. Data-related
problems such as data mining and DSS
can use the CRISP-DM method, which is
expected to analyze business problems
and current conditions, provide
appropriate data conversion to provide a
model that can evaluate effectiveness and
record the results obtained. CRISP-DM
solves this problem by defining a process
model related to data mining and DSS,
regardless of the problem department or
technology used.

3. 71
Business understanding is the stage
used to determine business goals, analyze
business conditions, and determine the
objectives of the DSS. At this stage a
thorough understanding is carried out based
on the results of the analysis of observations,
interviews, and supporting documents for the
objectives and results of the research. Several
options can be found when determining the
best e-commerce in Indonesia. Based on the
alternatives obtained, calculations are made
to determine the ranking. The best e-
commerce results can be the best
recommendations for consumers to make
digital transactions. On the other hand, e-
commerce that has not achieved the best
results can still improve its performance to
gain a better market share. When determining
the number and alternative criteria for the
best e-commerce candidates, refer to the
assessment in the 2019 Consumer Pulse eIQ
survey and get 6 alternatives namely Blibli,
Bukalapak, JD.id, Lazada Indonesia, Shopee
and Tokopedia. The decision makers used are
3 netizens who are actively using e-
commerce. The weights of the criteria were
obtained from the Decision Maker and were
calculated using SWARA, while the
evaluation of the alternative ranking used the
ARAS methods.
At Data understanding stage, it starts
with the process of data collection, data
analysis and evaluation of the quality of the
data used in the study. To be able to use the
SWARA-ARAS methods correctly,
appropriate criteria and alternative data are
needed. The criteria used in this study include
reputation, price & product, customer
service, delivery & payment, application &
UX and security & policy.
At Data preparation stage includes
selecting the data used and published to be
included in the DSS calculation. At this stage,
data cleaning is also carried out to repair,
remove or ignore noise in the data. At the
business understanding stage, the tools,
techniques or methods used in this study
have been selected. In this Modeling
stage, SWARA-ARAS methods were
chosen to determine the best e-commerce
in Indonesia. Before continuing the
research, you can do a test design with the
data to prove the method can be used.
Flowchart of method usage can be seen in
Figure 2.
Fig. 2.SWARA-ARAS Flowchart
The first step is to prepare
comparison data between the criteria
provided by the decision maker as a
resource and alternative data is Indonesian
e-commerce data based on the 2019 eIQ
Consumer Pulse survey. The SWARA
method starts with the initial prioritization
of attributes, calculates the coefficient,
determines the initial weight, relative
weight, thus determining the final ranking
of attributes. Furthermore, the ARAS
method is used to normalize alternative
data to produce normalized alternative
data. Criteria weight data results from the

4. 72
calculation of the SWARA method, and
alternative data normalized using the ARAS
methods are used for weighted normalization
calculations and calculating preference
values, the optimality function, as well as
producing ratings based on utility degree that
can determine the best e-commerce ranking.
This can be a reference for customers or input
as a refinement of e-commerce, which still
lacks in some aspects.
At evaluation stage, testing is carried
out based on the results of the DSS
recommendations and the performance of the
methods used. Calculations must be tested
manually, and the results obtained when
implemented in software have the same value
in order to have compatibility between the
two. Sensitivity testing is used to compare the
performance between the ARAS methods to
measure which method is more sensitive to
changes in weighting criteria, therefore the
more sensitive the better. At Deployment
stage, a deployment plan is carried out based
on previous assessments. If the test results
show good results, further implementation
can be planned. Apart from deployment
planning, a monitoring and maintenance plan
can also be planned to produce a final report
on the research results.
III. Result and Discussion
This research is based on questionnaire
data from users who are very familiar with e-
commerce, the questionnaire is transformed
using the SWARA method into weighting
criteria and e-commerce data as an
alternative. The number of Decision Makers
used to produce weighting criteria is 3
people, and the amount of e-commerce data
used is 6 companies. The calculation starts
using the SWARA method. There are 6
criteria, namely (C1) reputation, (C2) price &
product, (C3) customer service, (C4) delivery
& payment (C5) application & UX and
(C6) security & policies. The initial
prioritization of attributes from Decision
Maker 1. 2 and 3 are shown in Table 1.
Table 1. The Initial Prioritization of
Attributes from Decision Maker 1, 2 and
3
Criteri
a
C
1
C
2
C
3
C
4
C
5
C
6
DM1
4 4,
9
3 3,
3
2,
8
4,
5
DM2
3,
8
4,
5
3,
2
3,
9
3,
5
3,
4
DM3
3,
2
4,
7
3 3,
7
3 4
Furthermore, the calculation is
focused on the Decision Maker 1. The
calculation steps for other Decision
Makers are the same as the calculation for
the Decision Maker 1.
To change from the initial
prioritization of attributes to calculate
coefficients, you can sort the weights of
the criteria, from the largest to the
smallest. Then normalization is carried out
by dividing the value of each weight by
the maximum value for all weight values.
The coefficient value is done by adding a
value of 1 to each value of each criterion
in the Normalized Initial Prioritization of
Attributes except the largest. The sorted
and normalized initial prioritization of
attributes from DM 1 and the coefficient
value are shown in table 2.
Table 2. Sorted and Normalized
Initial Prioritization of Attributes from
Decision Maker 1 and the Coefficient
Value

5. 73
Criteria DM1
DM1
Norm.
DM1
Coef.
C2 4,9 1,000 1,000
C6 4,5 0,918 1,918
C1 4 0,816 1,816
C4 3,3 0,673 1,673
C3 3 0,612 1,612
C5 2,8 0,571 1,571
Max 4,9
The initial weight of an attribute for
each decision maker is calculated by dividing
the initial weight of the i − 1 attribute by the
coefficient value (k) of ith attribute in the
same decision maker, which is as follows for
the first attribute:
𝑞𝑞2 = 1
𝑞𝑞6 = 1,000
1,918
� = 0,521
𝑞𝑞1 = 0,521
1,816
� = 0,287
𝑞𝑞4 = 0,287
1,673
� = 0,171
𝑞𝑞3 = 0,171
1,612
� = 0,106
𝑞𝑞5 = 0,106
1,571
� = 0,068
After getting the initial weight value,
normalization is carried out by dividing the
entire initial weight by the number of initial
weights, to be able to calculate the relative
weight value. The initial weight and relative
weight of DM1 which have been sorted in the
initial conditions can be seen in table 3.
Table 3. Initial Weight and Relative
Weight from Decision Maker 1
Criteria
Initial
Weight
Relative
Weight
C1 0,287 0,133
C2 1,000 0,464
C3 0,106 0,049
C4 0,171 0,080
C5 0,068 0,031
C6 0,521 0,242
With these steps, they are also
carried out on DM2 and DM3, so that they
get the relative weight of each decision
makers. Table 4 shows the results of the
relative weight of each decision maker
along with their geometric mean to be
used in a compromise for weighting the
criteria in ARAS. The pie chart for the
relative weight under normalized
geometric mean conditions can be seen in
Figure 3.
Table 4. Relative Weight of Each
Decision Maker and Normalized
Geometric Mean
Criteri
a
Relative Weight Geo
Mean
(Norm
)
DM
1
DM
2
DM
3
C1 0,13
3
0,13
6
0,08
2
0,1218
9
C2 0,46
4
0,46
8
0,45
5
0,4943
5
C3 0,04
9
0,02
5
0,05
0
0,0424
9
C4 0,08
0
0,25
1
0,13
7
0,1497
0
C5 0,03
1
0,07
6
0,03
0
0,0447
3
C6 0,24
2
0,04
4
0,24
6
0,1468
4

6. 74
Fig. 3.Relative Weight Using SWARA
(Normalized Geometric Mean)
After getting the weighted criteria
results, continue using the ARAS method to
calculate the preference value. When using
the ARAS method to calculate, starting from
the normalized decision matrix, the weighted
normalized decision matrix, the optimality
function, the ultility degree and the final
ranking of alternatives. The e-commerce data
used includes Blibli (X1), Bukalapak (X2),
JD.ID (X3), Lazada (X4), Shopee (X5) and
Tokopedia (X6). Based on predetermined e-
commerce data, the results are shown in
Table 5 below. To get the value for X0, if the
criterion is a benefit condition, then X0 is the
maximum value from the criteria column,
and if the criterion is a cost condition, then
X0 is the minimum value from the criteria
column. The sum row only sums the
alternative values in the criteria column
without including X0.
Table 5. Alternative data and X0
using ARAS
Cri
-
teri
a
C
1
C2 C3
C
4
C5 C6
X0
14
,8
170,
1
23,
3
71
,4
32,
4
22,
7
X1
14
,8
168,
5
18,
4
61
,7
19,
8
15
X2 13
163,
7
23,
3
46
,4
32,
4
21,
4
X3
12
,9
170,
1
8,3
71
,4
22,
8
13,
7
X4
13
,7
166,
5
15,
8
62
,3
19,
5
22,
2
X5
10
,9
168,
2
18,
7
58
,3
23
22,
4
X6
14
,3
167,
2
21,
4
42
,9
31,
6
22,
7
SU
M
79
,6
100
4,2
105
,9
34
3
149
,1
117
,4
The normalized decision matrix is
calculated using the following steps.
Exemplified in line X1 where the other
alternative uses the same steps as X1,
including X0. The results of the
normalized decision matrix of all
alternatives can be seen in table 6.
𝑟𝑟∗
𝑖𝑖𝑖𝑖 =
𝑟𝑟𝑖𝑖𝑖𝑖
∑ 𝑟𝑟𝑖𝑖𝑖𝑖
𝑚𝑚
𝑖𝑖=0
𝑋𝑋∗
11 =
14,8
79,6
= 0,1859
𝑋𝑋∗
12 =
168,5
1004,2
= 0,1678
𝑋𝑋∗
13 =
18,4
105,9
= 0,1737
𝑋𝑋∗
14 =
61,7
343
= 0,1799
𝑋𝑋∗
15 =
19,8
149,1
= 0,1328
𝑋𝑋∗
16 =
15
117,4
= 0,1278
C1
12,19%
C2
49,43%
C3
4,25%
C4
14,97%
C5
4,47%
C6
14,68%
RELATIVE WEIGHT
(NORMALIZED GEOMETRIC MEAN)

7. 75
Table 6. Normalized Decision Matrix
Using ARAS
Cri
-
teri
a
C1 C2 C3 C4 C5 C6
X0
0,1
86
0,1
69
0,2
20
0,2
08
0,2
17
0,1
93
X1
0,1
86
0,1
68
0,1
74
0,1
80
0,1
33
0,1
28
X2
0,1
63
0,1
63
0,2
20
0,1
35
0,2
17
0,1
82
X3
0,1
62
0,1
69
0,0
78
0,2
08
0,1
53
0,1
17
X4
0,1
72
0,1
66
0,1
49
0,1
82
0,1
31
0,1
89
X5
0,1
37
0,1
67
0,1
77
0,1
70
0,1
54
0,1
91
X6
0,1
80
0,1
67
0,2
02
0,1
25
0,2
12
0,1
93
After getting the normalized decision
matrix, proceed to calculate the weighted
normalized decision matrix, by multiplying
the relative weight from SWARA with the
normalized decision matrix from ARAS.
Exemplified in line X1 where the other
alternative uses the same steps as X1. The
results of the weighted normalized decision
matrix of all alternatives can be seen in table
7.
𝑟𝑟𝚤𝚤𝚤𝚤
� = 𝑟𝑟∗
𝑖𝑖𝑖𝑖 × 𝑤𝑤𝑗𝑗
𝑋𝑋11
� = 0,1859 × 0,1219 = 0,0227
𝑋𝑋12
� = 0,1678 × 0,4943 = 0,0829
𝑋𝑋13
� = 0,1737 × 0,0425 = 0,0074
𝑋𝑋14
� = 0,1799 × 0,1497 = 0,0269
𝑋𝑋15
� = 0,1328 × 0,0447 = 0,0059
𝑋𝑋16
� = 0,1278 × 0,1468 = 0,0188
Table 7. Weighted Normalized
Decision Matrix Using SWARA-ARAS
Cr
i-
ter
ia
C1 C2 C3 C4 C5 C6
X0
0,0
23
0,0
84
0,0
09
0,0
31
0,0
10
0,0
28
X1
0,0
23
0,0
83
0,0
07
0,0
27
0,0
06
0,0
19
X2
0,0
20
0,0
81
0,0
09
0,0
20
0,0
10
0,0
27
X3
0,0
20
0,0
84
0,0
03
0,0
31
0,0
07
0,0
17
X4
0,0
21
0,0
82
0,0
06
0,0
27
0,0
06
0,0
28
X5
0,0
17
0,0
83
0,0
08
0,0
25
0,0
07
0,0
28
X6
0,0
22
0,0
82
0,0
09
0,0
19
0,0
09
0,0
28
After getting the weighted normalized
decision matrix, proceed to calculate the
optimality function, by adding up the
values of all rows from the weighted
normalized decision matrix. The optimal
value is called V0 which is the result of
utility degree on alternative X0.
𝑆𝑆𝑖𝑖 = � 𝑟𝑟𝚤𝚤𝚤𝚤
�
𝑛𝑛
𝑗𝑗=1
𝑉𝑉0 = 0,0227 + 0,0837 + 0,0093 +
0,0312 + 0,0097 + 0,0284 =
0,1850
𝑆𝑆𝑋𝑋1 = 0,0227 + 0,0829 + 0,0074 +
0,0269 + 0,0059 + 0,0188 =
0,1646
𝑆𝑆𝑋𝑋20,0199 + 0,0806 + 0,0093 +
0,0203 + 0,0097 + 0,0268 =
0,1666

8. 76
𝑆𝑆𝑋𝑋3 = 0,0198 + 0,0837 + 0,0033 +
0,0312 + 0,0068 + 0,0171 = 0,1620
𝑆𝑆𝑋𝑋4 = 0,0210 + 0,0820 + 0,0063 +
0,0272 + 0,0059 + 0,0278 = 0,1701
𝑆𝑆𝑋𝑋5 = 0,0167 + 0,0828 + 0,0075 +
0,0254 + 0,0069 + 0,0280 = 0,1674
𝑆𝑆𝑋𝑋6 = 0,0219 + 0,0823 + 0,0086 +
0,0187 + 0,0095 + 0,0284 = 0,1694
To calculate the utility degree, it is done
by dividing the optimality function value of
each alternative by the value of V0.
𝑘𝑘𝑖𝑖 =
𝑆𝑆𝑖𝑖
𝑉𝑉0
𝑘𝑘𝑋𝑋1 =
0,1646
0,1850
= 0,8898
𝑘𝑘𝑋𝑋2 =
0,1666
0,1850
= 0,9003
𝑘𝑘𝑋𝑋3 =
0,1620
0,1850
= 0,8753
𝑘𝑘𝑋𝑋4 =
0,1701
0,1850
= 0,9193
𝑘𝑘𝑋𝑋5 =
0,1674
0,1850
= 0,9045
𝑘𝑘𝑋𝑋6 =
0,1694
0,1850
= 0,9155
In the final ranking, the utility degree
values are arranged in descending order, and
the alternative with the highest utility degree
value is selected as the best alternative. The
ranking for the selection of the best e-
commerce in Indonesia can be seen in the
table 8. The graph for ranking e-commerce
can be seen in the figure 4.
Table 8. The Ranking for the
Selection of the Best E-Commerce in
Indonesia Using SWARA-ARAS
Alternative
Utility
Degree
Ranking
Lazada (X4) 0,9193 1st
rank
Tokopedia
(X6)
0,9155 2nd
rank
Shoopee (X5) 0,9045 3rd
rank
Bukalapak
(X2)
0,9003 4th
rank
Blibli (X1) 0,8898 5th
rank
JD.ID (X3) 0,8753 6th
rank
Fig. 4.Selection of the Best E-Commerce
in Indonesia Using SWARA-ARAS
Based on calculations using SWARA-
ARAS, the favorite e-commerce in
Indonesia is Lazada (X4). Decision
makers focus on price and product criteria
reaching 49.43% which is almost half
compared to other criteria considerations,
and after that, delivery & payment criteria
reaching 14.97% and security & policies
criteria reaching 14.68%. In the Lazada
(X4) alternative seen in the normalized
decision matrix, it has a fairly good and
balanced value on criteria 2, 4 and 6, so
0,9155
0,9045
0,9193
0,8753
0,9003
0,8898
0,80 0,82 0,84 0,86 0,88 0,90 0,92 0,94
Selection of the Best E-
Commerce in Indonesia Using
SWARA-ARAS
Blibli Bukalapak JD.ID
Lazada Shoopee Tokopedia

9. 77
that the utility degree calculation is the best.
The next favorite e-commerce, followed by
Tokopedia and Shoopee, while the least
featured from the tested data is JD.ID.
Ratings may change when the decision maker
gives a different rating.
IV. Conclusion
Based on previous research, it shows that
the SWARA-ARAS method can be used to
determine favorite e-commerce in Indonesia.
Based on the weighting of the three decision
makers on the 6 predetermined criteria, they
tend to choose the price & product criteria as
the main choice. Based on the 6 alternatives
tested using SWARA-ARAS, Lazada (X4)
became the favorite e-commerce in Indonesia
with a value of 0.9193 followed by
Tokopedia with a value of 0.9155 and
Shoopee with a value of 0.9045. JD.ID
became the last position with a value of
0.8753. The position of this favorite e-
commerce in Indonesia is strongly influenced
by the decision of the decision maker. If the
decision maker gives a different assessment
of each criterion, the position of this favorite
e-commerce in Indonesia will also change.
Acknowledgment
Thank you to the Editorial Team of the
International Journal of Interreligious and
Intercultural Studies, Universitas Hindu
Indonesia for giving the author the
opportunity to publish this proceeding article.
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