Implementation of Algorithm TOPSIS and ISO 9126 on the Selection

of Employee Acceptance

Basuki Hari Prasetyo, Mujito, Dian Anubhakti and Muhammad Idrus

Universitas Budi Luhur

Keywords: Employess, Recruitment, Application.

Abstract: Human resources in a company is one of the determinable of a company to deal with competition with other

companies. So in managing a human resource is required the right steps. A company should do a very strict

selection in employee acceptance. Employees must meet the criteria required by the company. Most of the

recruitment by the company is to post jobs on web site job seekers such as jobsdb.com, JobStreet and

Linkedln. So that recruitment will receive many work applications from the applicants. and check it one.

Thus ineffective and efficient and often errors in the calculation of the value of each prospective employee

as well as a subjective appraisal that resulted in the elected employee is not the best employee and not in

accordance with the assessment. In this research using TOPSIS algorithm so that the value of each

prospective employee will be calculated and carried out using the algorithm and displayed in the

application. To prove the feasibility of an application that has been created using ISO 9126. The result of

this research is a decision support system application that uses TOPSIS algorithm.

1 INTRODUCTION

Acceptance of the selection process of employees in

a company is very important. Because the

recruitment process is correct and in accordance

with the procedure will produce high quality human

resources. And vice versa if the recruitment is done

not in accordance with the procedure will produce

human resources that do not qualify. Recruitment

processes that occur in large companies in the region

of Indonesia are still using manual calculations and

do not utilize methods or algorithms. By utilizing

algorithms such as TOPSIS then the calculation of

each prospective employee's process will be very

fast and accurate. The rank process of each

prospective employee's value can also be displayed

by Graphik so that decision makers can quickly

decide on every prospective employee. Like

previous research Wahyuni, Elyza Gustri and

Ananto Tri Anggoro titled Support system for

Employee acceptance by TOPSIS method. In the

research using 5 criteria, namely IPK, TOEFL, TPA,

work experience and age and the test of application

results using interviews. Similarly, the previous

research Djamain dan De Christin is about the

system of supporting the recruitment decision of the

State electricity company using Simple Additive

Weighted algorithm. The criteria used are discipline,

obedience in carrying out duties, skills, Moral and

attitudes, work experience, cooperation and

innovation. The results of the research include the

value of each prospective employee and have not

used the test application that has been created.

Referring to the previous two studies then in this

research at the stage of testing software or

applications that have been made will be tested with

ISO 9126.

2 MAIN CONCEPT

2.1 Multi Attribute Decision Making

Multi Attribute Decision Making is a way or method

used in looking for a maximum alternative from

several alternatives with specified criteria. The goal

of the MADM is to determine the value of the

weights on each attribute, and continue with each

alternative. According to Kusumadewi some of the

features that exist in the MADM, namely:

a. Alternative, that is, prospective employees

who will be recruited to become employees.

140

Prasetyo, B., Mujito, ., Anubhakti, D. and Idrus, M.

Implementation of Algorithm TOPSIS and ISO 9126 on the Selection of Employee Acceptance.

DOI: 10.5220/0008930801400146

In Proceedings of the 1st International Conference on IT, Communication and Technology for Better Life (ICT4BL 2019), pages 140-146

ISBN: 978-989-758-429-9

Copyright

c

b. Attributes, or also called criteria. i.e. the size

that is the basis of judgment or determination

of something in decision making.

c. Weight of decision, the decision weight will

show the value of relative interest on each

criterion, W = (w1, w2, ..., wn).

d. Decision Matrik, a decision matrix or X, will

be M X N, and contains xij elements,

representing from an alternate Ai (i = 1, 2,...,

m) against the Cj criterion (j = 1, 2,..., N)..

On MADM The calculation process is done into

3 stages, first the preparation of the components of

the situation, then analyzes and processes the

synthesis of information. The decision matrix given

each alternative to each X attribute as follows:













 



  





 





 (1)

2.2 Technique for Order Preference by

Similarity to Ideal Solution

(TOPSIS)

TOPSIS method is one method that can help the

decision making process to solve the problem of

optimal decision practically. This is because the

concept is simple and understandable, efficient and

computing have the ability to measure the relative

performance of the decision alternatives in the form

of a simple mathematical.

2.2.1 Procedure TOPSIS

a. Computes the matrix the normalization

TOPSIS need rating the performance of each

prospective employee on any criteria or sub criteria

the normalization. normalization matrix formed

from Equation 1.













 









(2)

b. Computes the weigh matrix normalization

Equations (3) used to calculate weighted the matrix

normalization, then it must be determined in

advance the value of weights that represent absolute

preferences of decision makers. The value indicates

the level of preference weights of the relative

importance of each criterion or sub criteria in

equation (2)

W = {w1, w2, ..., wn} (2)





= 







(3)

c. Calculate the Ideal Solution Matrix of positive

and Negative Ideal Solution Matrix

The ideal solution is positive and negative ideal

solution can be determined based on the weighted

rating of normalization. Please note terms in

equations (4) and (5) in order to calculate the value

of an ideal solution by first determining whether the

of the advantage (benefit) or costs (cost).

A+ = ( y1+ , y2+,…… yn+) (4)

A- = ( y1- , y2-,…… yn-) (5)

d. Determines the distance between the value of

each Alternative with a positive Ideal Solution

Matrix and matrix Ideal Solution Negative

Determine the distance between the alternative Ai

with a positive ideal solution, which is described in

equation (6).





















 











2 ; i= 1,2.m (6)

Determine the distance between the alternative

Ai with a negative ideal solution, which is described

in equation (7)



















 











 (7)

e. e. Calculate the value of Preferences for each

Alternative

The value of preferences (Vi) for each alternative

are formulated in the equation (8) as follows:

























(8)

2.2.2 ISO 9126

To conduct application testing the ISO 9126 is very

suitable for use, ISO 9126 is an international

standard in testing software. ISO 9126 defines the

quality of the software products, models, quality

characteristics, and related metrics used to evaluate

and determine the quality of a software product.

According to Al-Qutaish The testing aspects adopted

are as follows:

a. Functionality. That is the ability of a software

to serve the needs of users according to its

functions.

b. Reliability. Is a permissible software is

capable of working in optimal conditions even

in certain conditions.

Implementation of Algorithm TOPSIS and ISO 9126 on the Selection of Employee Acceptance

141

c. Usability. Is a simple permissible software is

easy to grasp and understand as well as easy to

pull and not dull when...

d. Efficiency Is the permissible software can

provide maximum performance to the

resources used.

Figure 1: ISO 9126

Table 1. ISO 9126 Capability Aspects

Criteria

Sub Criteria

Description

Functionalit

y

Suitability

Able to perform tasks

according to the

objectives set

Accuracy

Able to produce

accurate and detail

output according to

the needs

Security

Able to face someone

who is not given

access to enter the

system

Interoperability

Able to interact

between operating

system platforms

Compliance

Able to meet the

needs in accordance

with prevailing

standards and

regulations

Reliability

Maturity

Able to avoid failures

of software errors

Fault tolerance

Able to maintain

application

performance in the

event of a software

error

Recoverability

The application is

able to improve

performance after a

system failure

including connection

to network and data

Usability

Understandabil

Easy to understand

ity

applications

Learnability

applications already

to learn

Operability

Easy to operate

applications

Attractiveness

Application is able to

attract users to use it

Efficiency

Time behavior

Able to provide fast

response for data

processing according

to function

Resource

behavior

Able to manage the

resources owned

when performing

predefined functions

3 DISCUSSION AND RESULT

In this study the overall criteria using the attribute

table benefit, and for the assessment of use Range

from 1 – 5, use 6 Criteria and 5 prospective

employees

Table 2: Table of Criteria

No

Code

Criteria Name

Default

Value

1

C01

Physical Appearance

4

2

C02

intelligence

5

3

C03

Communication

4

C04

Work Motivation

4

5

C05

Education

5

6

C06

Work Experience

5

ICT4BL 2019 - International Conference on IT, Communication and Technology for Better Life

142

Description:

5 = Very Good

4 = Good

3 = Enough

2 = Poor

1 = Very Poor

3.1 Calculation Method TOPSIS

a. After Alternative following the selection

process and the assessment then obtained data

are shown in table 3:

Table 3: Table of Assessment

No

Alternative

Name

Criteria Name

C01

C02

C03

C04

C05

C06

1

Indah

5

3

4

5

2

Rudiyanto

5

4

5

4

3

Kurniawan

5

3

4

A. Riyan

4

3

4

5

Charles

5

3

Devideer

10,770

9,165

10,344

9,539

9,055

The value of the divider is derived from the

value of the root of criteria each alternatif then

square and calculate. Example (SQRT ((5^2) + (5^2)

+ (5^2) + (4^2) + (5^2)) = 10,770.

b. Specifies matrix decision normalization, are

shown in table 4.

Table 4: Table of Normalization

No

Alternative

Name

Criteria Name

C01

C02

C03

C04

C05

C06

1

Indah

0,464

0,327

0,387

0,524

0,577

0,552

2

Rudiyanto

0,464

0,436

0,483

0,419

0,462

0,442

3

Kurniawan

0,464

0,546

0,483

0,314

0,346

0,442

4

A. Riyan

0,3741

0,327

0,387

0,419

0,462

0,442

5

Charles

0,464

0,546

0,483

0,524

0,346

0,331

Then each alternate value divided by the value of

the divisor. Sample: 5 / 10,770 = 0,464.

c. Calculate the decision matrix weighted

normalization, are shown in table 5. As for the

weighting used is W = (4, 5, 4, 4, 5, 5). So the

retrieved results i.e.

Table 5: Table of Weights Normalization

No

Alternative

Name

Criteria Name

C01

C02

C03

C04

C05

C06

1

Indah

1,857

1,637

1,547

2,097

2,887

2,761

2

Rudiyanto

1,857

2,182

1,933

1,677

2,309

2,209

3

Kurniawan

1,857

2,728

1,933

1,258

1,732

2,209

4

A. Riyan

1,486

1,637

1,547

1,677

2,309

2,209

5

Charles

1,857

2,728

1,933

2,097

1,732

1,656

Then the value of the matrix is multiplied by the

value of the normalization decision W. Sample:

0,464 * 4 = 1,857.

d. Calculate the ideal solution matrix of positive

and negative, the ideal solution matrix are

shown in table 6:

Table 6: Table of Matrix Solutions

A+

1,857

2,728

1,933

2,097

2,887

2,761

A-

1,486

1,637

1,547

1,258

1,732

1,656

Implementation of Algorithm TOPSIS and ISO 9126 on the Selection of Employee Acceptance

143

e. Calculate the distance between the value of

each alternative with ideal solution matrix of

positive and negative ideal solution matrix.

that will be shown in table 7:

Table 7: Table of Matrix Solutions Ideal Positive and

Negative

D+

D-

1,157588

1,474639

0,898207

1,046059

1,427105

1,215706

1,409644

0,713553

1,154701

1,476901

f. Calculate the value of preferences for each

alternative, which would be shown on table 8:

Table 8: Table Value Preference

No

Alternative

Name

Alternative

Value (V)

1

Indah

0,560

2

Rudiyanto

0,538

3

Kurniawan

0,460

4

A. Riyan

0,336

5

Charles

0,561

Based on calculation using TOPSIS method then

obtained data that the alternative name charles who

gained the highest score namely 0.561 and charles

received employee.

3.2 Test Validating ISO 9126

Test Validating ISO 9126 (Examine of

Functionability, Reliability, Usability, Efficiency

Model). In testing with the use of ISO 9126 applied

this, researchers involving 3 users to give value to

this Model. and using likert scale for the assessment

a. Percentage Score models for Variable

Functionality

Table 9: Table of Score Model Variable Functionality

% Score Actual: Score Actual Functionality x 100 %

Score Ideal Reliability

% Score Actual: 89 x 100%

120

% Score Actual: 74,2 %

From the result of the calculation of Functionaliy

model obtained a value of 74,2%. Then it can be

concluded this model goes well.

b. Percentage Score models for Variable

Reliability

Table 10: Table of Score Model Variable Reliability

% Score Actual: Score Actual Reliability x 100 %

Score Ideal Reliability

% Score Actual: 57 x 100%

75

% Score Actual :76 %

From the result of the calculation of Reliability

model obtained a value of 76%. Then it can be

concluded this model goes well.

c. Percentage Score models for Variable

Usability

Table 11: Table of Score Model Variable Usability

ICT4BL 2019 - International Conference on IT, Communication and Technology for Better Life

144

% Score Actual: Score Actual Usability x 100 %

Score Ideal Usability

% Score Actual: 80 x 100%

105

% Score Actual: 76,19 %

From the result of the calculation of Usability

model obtained a value of 76,19%. Then it can be

concluded this model goes well.

d. Percentage Score models for Variable

Efficiency

Table 12: Table of Score Model Variable Efficiency

% Score Actual: Score Actual Efficiency x 100 %

Score Ideal Efficiency

% Score Actual: 46 x 100%

60

% Score Actual: 76,67 %

From the result of the calculation of Efficiency

model obtained a value of 76,67 %. Then it can be

concluded this model goes well.

Description:

SS = Very Agree

S = Agree

R = Hesitation

TS = Not Agree

STS = Very Not Agree

4 CONCLUSIONS AND FURTHER

WORK

In application testing using ISO 9126 obtained a

value of functionality of 74.2 and reability 76.19

while usability value 76.19 and efficiency value

76.67. The final result obtained 75.76%. This

indicates that the ISO 9126 test goes well. In

calculating the value of prospective employees using

the TOPSIS algorithm obtained the data received

into the employee is Charles with a total value of

0561 while the lowest value of prospective

employees obtained A. Riyan with the value of

0336. For subsequent research the algorithm can be

combined with other algorithms such as AHP, ANP,

WP or any other algorithm. As for testing

applications can use Test Accepment Test or other

test models.

ACKNOWLEDGEMENTS

The authors wish to thank Rector Budi luhur

University and Faculty of information technology

and the related parties so that the journal can be

resolved.

REFERENCES

Al-Qutaish, Rafa, E. 2010. Quality Models in Software

Engineering Literature: An Analytical and

Comparative Study. Journal of American Science, vol.

6. pp.166-175.

Kusumadewi, S. et al. 2006. Fuzzy Multi-Attribute

Decision Making. Graha Ilmu. Yogyakarta.

Alrawashdeh, T. A., Muhairat, M., & Althunibat, A. 2013.

Evaluating the Quality of Software in ERP Systems

Using the ISO 9126 Model. International Journal of

Ambient Systems and Applications (IJASA), 1(1), 1-9.

Marimin dan Maghfiroh, N. 2013. Aplikasi Teknik

Pengambilan Keputusan dalam Manajemen Rantai

Pasok, Penerbit IPB Press. Bogor

Lorincova, S. 2015. The Improvement of the Effectiveness

in the Recruitment Process in the Slovak Public

Administration, Business Economics and Management

2015 Conference, 34(2015): 382-389.

Mujito, et al. 2018. Selection of Prospective Employees

Using Analytical Hierarchy Process (AHP) and ISO

9126. International Conference on Applied

Information Technology and Innovation (ICAITI

2018) in Padang, Indonesia. September 4-5, 2018.

Djamain ,Y., dan De Christin, H., 2015. Sistem

Pendukung Keputusan Penerimaan Pegawai Baru PT.

PLN (PERSERO) Kantor Pusat Dengan Menggunakan

Metode Simple Additive Weighting (SAW), Jurnal

Teknik Informatika, UIN Jakarta.

Wahyuni, Elyza Gustri ., Anggoro, Ananto Tri ., Sistem

Pendukung Keputusan Penerimaan Pegawai dengan

Metode TOPSIS, Jurnal Sains, Teknologi dan Industri,

Vol. 14, No. 2, Juni 2017, pp.108 – 116

Turban E, Aronson JE, & Liang Ting-Peng, 2005.

Decision Support Systems and Intelligent Systems.

International Edition, 7th edition. New Jersey: Pearson

Prentice-Hall Education International

Implementation of Algorithm TOPSIS and ISO 9126 on the Selection of Employee Acceptance

145

Tseng, G.H. and Huang, J.J. 2011. Multiple Attribute

Decision Making, Methods and Applications. CRC

Press, Boca Raton

Sparague, R. H. and Watson H. J. 1993. Decision Support

Systems: Putting Theory Into Practice. Englewood

Clifts, N. J., Prentice Hall

ICT4BL 2019 - International Conference on IT, Communication and Technology for Better Life

146