Selection between AHP and TOPSIS for Academic Information Systems

Decision Making Model

Jeffry Andhika Putra

, Tsabit Rakhman

and Muhammad Kunta Biddinika

Department of Informatics Engineering, Universitas Janabadra, Yogyakarta, Indonesia

Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Department of Civil Engineering, Universitas Janabadra, Yogyakarta, Indonesia

Keywords:

Academic Information System, Open Source, MADM, AHP, TOPSIS.

Abstract:

One way to develop a system of academic information using applications Free Open Source (FOS) that are

circulating. Academic information system developers need to determine the appropriate FOS used to develop

academic information system based on the criteria required and reliability of FOS. One way to help decision-

making can be used MADM models using AHP and TOPSIS. In this study, applying a comparative analysis

of the two methods, the method of AHP and TOPSIS with analytical testing calculations used to compare

the three-applications FOS Academic Information System, Campus Academic Information System (Siakad),

Academic Information Systems Integrated (Sikadu), as well as SISFOKOL to develop academic information

systems.

1 INTRODUCTION

Technological developments have an impact on uni-

versity academic services. At ﬁrst, the academic ser-

vice is only done by hand using paper media and sta-

tionery (display devices), with the growing scale of

academic service which means user can no longer

do, many universities use Microsoft Ofﬁce applica-

tions to perform academic services but each univer-

sity institution has its own characteristics so that the

necessary academic service system is unique aca-

demic information in accordance with the needs of

each university. To develop an academic informa-

tion system application do not require large funds

to the presence of Free Open Source (FOS). FOS

helping universities realize the management of infor-

mation technology-based academic services without

thinking about software procurement funds (Rousidis

and Christodoulou, 2019), FOS can be developed and

modiﬁed freely adapted to the needs.

It is necessary to pay more attention to deter-

mining FOS according to the needs of each univer-

sity and its reliability because each FOS has advan-

tages and disadvantages. In determining the most ap-

propriate FOS needs and can reliably use the tech-

nique of decision-making methods Multiple Attribute

Decision Making (MADM), including Simple Addi-

tive Weighting (SAW), Weight Product (WP), TOP-

SIS, and AHP methods (Kazimieras Zavadskas et al.,

2019), This study using AHP and TOPSIS later than

the methods are superior and relevant to the research

problems.

AHP is a multi-criterion that can perform

decision-making process with many criteria (Terzi,

2019). AHP superiority compared to other MADM

models are able to analyze simultaneously as well as

integrated between quantitative and qualitative crite-

ria (Distel, 2018). AHP can help facilitate decision-

making by many criteria. Research using AHP pre-

viously been done in the manufacture and analysis of

the rector election system by using AHP (Fitriastuti

et al., 2019), Other studies of AHP has done research

on the selection of open source digital library applica-

tions using AHP with three open source alternatives.

In addition to the AHP, TOPSIS can also perform

multi-criteria decision-making (Distel, 2018) to pro-

vide a solution by comparing each alternative with

the best and worst alternative (Fitriastuti et al., 2019).

TOPSIS widely used on the grounds concept is sim-

ple, easy to understand computation, efﬁcient, and

have the ability to measure the relative performance

of alternatives in the decision of a simple mathemati-

cal form (Distel, 2018). Research by TOPSIS method

was made for selecting suppliers in the pharmaceuti-

cal distribution industry method approach (Putra and

Sylvandinata, 2019).

Putra, J., Rakhman, T. and Biddinika, M.

Selection between AHP and TOPSIS for Academic Information Systems Decision Making Model.

DOI: 10.5220/0009878700860089

In Proceedings of the 2nd International Conference on Applied Science, Engineering and Social Sciences (ICASESS 2019), pages 86-89

ISBN: 978-989-758-452-7

The problem is how to ﬁnd a better model of AHP

or TOPSIS in decision making of academic informa-

tion systems at Department of Informatics Engineer-

ing Universitas Janabadra. So, the goal is to produce

the right solution in selecting the appropriate FOS and

more relevant to know the method used in this case

between the AHP and TOPSIS methods.

2 MANUSCRIPT PREPARATION

Following a decision support system theory MADM

models used in this study:

2.1 Analytical Hierarchy Process (AHP)

Analytical Hierarchy Process (AHP) is a functional

hierarchy to the main input of human perception.

Method was developed to search for a rank (priority)

of the various alternatives in solving a problem (Es-

colar et al., 2019). AHP in resolving problems with

some design principles, which must be understood,

such as (Maxwell, 2019); (1) Creating a hierarchy, (2)

Assessment criteria and alternatives, (3) Synthesis of

Priority (Setting priorities), and (4) Logical consis-

tency.

2.2 AHP Steps

There are several steps in the completion of the AHP

method, as follows (G

urb

uz, 2019):

1. Deﬁning the problem and determine the desired

solution, then hierarchical structuring.

2. Prioritization; (a) the contribution size of each ele-

ment to achieve the goal, (b) compiled by the rela-

tive level of interest of each element, (c) summing

columns, (d) creating a new matrix by means of

each element divided by the number of columns,

(e) summing lines, (f) creating a new matrix with

elements result the number of rows divided by the

total sum. The results of the ﬁnal division called

Eigen Vector.

3. Logical consistency Consistency means two

things: ﬁrst that thought or similar objects are

grouped according to homogeneity and relevance.

The second meaning is that the consistency of the

intensity of the relationships between ideas or ob-

jects based on a certain criterion to justify each

other logically; (a) create new matrix by multi-

plying the initial matrix with Eigen Vector, (b)

add up in rows, (c) for the sum with Eigen Vec-

tor, the division called Eigen Value, (d) Count the

ways by; (i) add up by Eigen Value, (ii) the sum

is divided by the order, then the result is called a

lambda max or t, (iii) calculate CI (Consistency

Index), (iv) calculate CR (Consistency Ratio)

4. Rin is the Random Index. Random Index (RIN),

also called Random Consistency (RC) (G

urb

uz,

2019) during the CR value does not exceed 10%,

or 0.10, the value given paired comparisons are

considered consistent.

5. Priority calculate Alternatives There are two types

of data on the alternative, namely the qualitative

as well as quantitative data types. The calculat-

ing a priority of these two data types are different,

the qualitative data is by comparing each alterna-

tive. Comparisons were by pairwise comparison

matrix similar to determining the priority crite-

ria in step number two above. Quantitative data

on the priority type depends on the type of crite-

ria (sub-criteria), namely, the cost (cost) and gain

(beneﬁt) ((Distel, 2018).

2.3 Stage Data Collection

Collecting data is through observation and literature

study. The observations were to collect data and infor-

mation, as well as exploring and comparing the sys-

tem, interviews were conducted with the management

of the department, a literature study is to understand

the application well as understand the concept of the

application of AHP and TOPSIS via the internet, pa-

pers, journals, and books that are relevant.

2.4 Preliminary Analysis Phase

Initial analyzes do an analysis of the criteria and

the application of methods in selecting the FOS for

the development of academic information systems

(Uskov et al., 2017) . There are two application meth-

ods in this case is the method of AHP and TOPSIS.

Then they will be compared to ﬁnd more relevant

method used to select the appropriate FOS.

2.5 Testing Phase

The testing phase is done by analyzing the compara-

tive analysis with conformity to calculate the degree

of conformity (Tki) on each method using the for-

mula: -

T k

DataFADM(100%)

(1)

Where Tki = Suitability, Xi = average scores of

data methods. Looking Xi using the formula:

Selection between AHP and TOPSIS for Academic Information Systems Decision Making Model

ΣDataAHPorTOPSI

(2)

Concordance rate is measured by the percentage

level at Table 1.

Table 1: Table Percentage level of concordance

The percentage

rate of confor-

mity

Category

31% - 45% Unsatisfactory / no

good

46% - 60% Unsatisfactory / poor

61% - 75% Quite satisfactory /

good enough

76% - 85% Satisfactory / good

86% -100% Very satisfactory /

good

3 RESULT AND DISCUSSION

The initial phase of the analysis is to describe the al-

ternative open source applications are required selec-

tion criteria to give weight to the criteria in order of

importance and needs. As an alternative application is

given as follows; (1) Campus Academic Information

System (Siakad), (2) Integrated Academic Informa-

tion System (Sikadu), (3) SISFOKOL.

Six criteria were used here; features, technology,

source code program, ﬂexibility and support the de-

veloper. Each criterion is determined whether the na-

ture of cost/beneﬁts, costs mean less value the good,

while the proﬁts instead. On the criteria here, all

categorized advantage. Furthermore, each criterion

is weighted based on the results of the third explo-

ration application, interview-related needs, and inter-

view some expert programmers, further testing using

AHP and TOPSIS.

3.1 Calculation Method of AHP

In the hierarchy there are six main criteria of Ease

(Kem), Features (Fit), Source Code (Sourc), Flexibil-

ity (Fleks), as well as support the Developer and the

Community (Duk).

Step 1. Develop a pairwise comparison matrix us-

ing the concept of Saaty intensity scale, as shown in

Table 2.

Table 2: Pairwise Comparison Matrix

Crit

eria

Kem Fit Tek sou

reﬂ

kerc

hief

Kem 1 3 3 1 4 3

Fit 0.33 1 1 0.33 3 1

Tek 0.33 1 1 0.33 3 1

sourc 1 3 3 1 4 3

reﬂex 0.25 0.33 0.33 0.25 1 0.33

kerch

ief

0.33 1 1 0.33 3 1

total 3.24 9.33 9.33 3.24 18 9.33

Step 2. Normalization of each column (A’) and

calculating the average of each row (W).

a) Normalization of each column (A’), each entry

matrix divided by the total number of columns.

b) Calculating the average of each row (W). The av-

erage of each row of the matrix entries and the

results are expressed as a priority vector.

Step 3. Calculate the consistency index (CI):

count (A) (W

)

CR = 0.0162 / 1.24 = 0.0131

CR < 0.1, so consistent.

CR = 0.0162 / 1.24 = 0.0131 CR < 0.1, so consis-

tent.

Step 5. Ranking of the calculation based on the

weight of each criterion, do multiplication weighting

each criterion to the weights of the level of interest

among the criteria. The results can be seen in Table 3.

Table 3: Ranking of results tables

Result Rank

SISFOKOL .2495 3

Sikadu .3262 2

Siakad .3514 1

3.2 Calculation Method of TOPSIS

Step 1. Determine the importance scale of each cri-

terion (C1), features (C2), technology (C3), program

source code (C4), ﬂexibility (C5), developer and com-

munity support (C6), rated on a scale up to ﬁve and

decision makers give preference to weight on the

same scale. The results can be seen in Table 4.

Table 4: Scale the importance of each criterion

Alternative Criteria

1 2 3 4 5 6

Siakad

Sikadu

SISFOKOL

ICASESS 2019 - International Conference on Applied Science, Engineering and Social Science

Step 2. Normalize matrix (R) decision.

Step 3. Calculation of weighted normalized ma-

trix (Y), that is by multiplying the normalized matrix

(R), by weighting preference (W)

Step 4. Determine the positive ideal solution (A+)

and the ideal negative solution (A-)

Step 5. Determine the distance between the

weighted values of each alternative to the ideal pos-

itive solution (Si+) and the ideal negative (Si-) solu-

tion.

Step 6. Calculate the proximity of each alternative

to the ideal solution Analysis of AHP calculations and

TOPSIS.

Criteria ranking is determined based on rules that

have the highest weight value are in the ﬁrst priority

to be chosen and occupy the ﬁrst rank. Sequential

ranking starts from the criteria having the largest to

the smallest weight value. The results of ranking with

the AHP and TOPSIS methods can be seen in the fol-

lowing Table 5:

Table 5: AHP and TOPSIS ranking

Alternative Weight value AHP

rank-

ing

TOPSIS

rank-

ing

AHP TOPSIS

Siakad 0,3514 0,7228 1 1

Sikadu 0,3262 0,3741 2 4

SISFOKOL 0,2495 0,5728 3 2

Based on the table above, an analysis is con-

ducted to ﬁnd out the relevant methods for the prob-

lem by calculating the level of suitability (Tki) of each

method. To ﬁnd out the results of the level of con-

formity (Tki), the ﬁrst step is to ﬁnd out the average

value in each method. calculated using the following

formula:

AHP

1, 1005

= 0, 275125

TOPSIS

2, 217

= 0, 53175 (3)

4 CONCLUSIONS

Based on the results of a comparison analysis between

the level of conformity (Tki) of AHP method and

TOPSIS, both methods are in a very satisfying range

in assisting decision making in the MADM model but

for cases that use qualitative data and multicriteria

AHP method is more suitable to use than TOPSIS.

The ranking results using the AHP and TOPSIS meth-

ods are the same in rank 1 category, but different in the

next ranking. Siakad can be taken as FOS to develop

academic information systems. The AHP method has

a higher level of suitability than the TOPSIS method,

so the use of the AHP method is more relevant to the

problem and can be used as one of the decisionmaking

models for the MADM application that best meets the

criteria. This research still has deﬁciencies in terms

of determining the weight of criteria and determining

the level of importance because it is still based on the

perceptions of decision makers obtained from inter-

views and some experts in their ﬁelds are not based

on processing the results of the questionnaire.

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Selection between AHP and TOPSIS for Academic Information Systems Decision Making Model