Decision Support Systems for Employee Performance Assessment

Darius Shyafary

, Wahyuni Eka Sari

, Dwi Cahyadi

and Rony H.

Department of Design, Politeknik Negeri Samarinda, Samarinda, Indonesia

Department of Information Technology, Politeknik Negeri Samarinda, Samarinda, Indonesia

Keywords: Employee, Performance, SAW, Profile, Matching, Decision.

Abstract: Quality employees are an essential resource in a company. Employees who have qualification standards that

match the company's profile cause company productivity increased. PT. Pertamina (Persero) is one of the

state companies that manage the oil and gas sector in Indonesia, which maintains the quality of its employees.

PT. Pertamina always considers discipline, health, safety, and employee performance. Employee Performance

Assessment is needed to maintain the quality and profile of the company. Employee performance assessment

using the decision support systems method is Simple Additive Weighting (SAW) and Profile Matching (PM)

can be suitable for assessing employees. It can reduce errors in determining the best employees and obtain a

fair decision. In this study, a comparison between SAW and PM was built to find the best method. The SAW

and PM methods were chosen because they are not complicated in calculations and are suitable for small data.

The results showed that the accuracy of PM was 73% compared to SAW was 46%.

1 INTRODUCTION

Business industries are faced with changing dynamics

to compete with technological advances. Creating

organizational excellence in the company through

employee development is one of the best steps to deal

with today (Chiu et al., 2021) (Bezdrob & Šunje,

2021) (Lei et al., 2021). Employees who have

standards following the company's qualifications, the

company's productivity will indeed be maintained

and increase. Employee performance assessment is

one of the most efficient development, motivation,

and evaluation methods in a company. A performance

assessment system is used to measure the

effectiveness and efficiency of employees (Islami et

al., 2018).

PT Pertamina is one of the state companies with

an important sector, managing oil and gas in

Indonesia. Discipline, Health, safety, and

Performance of employees are always be prioritized

in PT. Pertamina. An employee performance

assessment needs to upgrade and maintain the quality

and profile of the company. In order to support this,

it is necessary to evaluate the quality of employees by

using a decision support system. Employee

performance assessment is a human resource

management activity in a company which is an

essential point in terms of the sustainability of a

company.

One of the leading values of PT. Pertamina is

"Capable," which means it is managed by

professional, skilled, and highly qualified leaders and

workers and is committed to building research and

development capabilities. Employees with these

criteria can improve the quality of the company that

must be appropriately managed. Human Resources

management is a determining aspect of the company's

success. Employee performance assessment is

collected subjectively. The problem for companies in

selecting the best employees is in the subjective and

manual assessment. Companies are challenging to

determine the employees with a good qualification in

a measurable. So this problem can be solved by

building a Decision Support System (Komsiyah et al.,

2019).

The simple Additive Weighting (SAW) method

and Profile Matching were used in this study. The

SAW method was chosen because it is able to select

the best alternative based on the specified criteria

(Roszkowska & Kacprzak, 2016). Research using the

SAW method includes determining the best

ambulance location (Abdullah et al., 2018) and the

best cornfield location (Seyedmohammadi et al.,

2018). The Profile Matching method is the most

appropriate method used in the process of comparing

individual competencies into the competencies of a

position so that differences in competencies can be

known. The initial process is carried out by

determining the aspects and sub-aspects, as well as

766

Shyafary, D., Sari, W., Cahyadi, D. and H., R.

Decision Support Systems for Employee Performance Assessment.

DOI: 10.5220/0010953200003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 766-773

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)

finding the weight value for each sub-aspect, looking

for the gap between the profile and data from the

employee (Rahim, 2017) (Atmanegara et al., 2017).

Several studies with profile matching are for the

position placement of employees (Dhammayanti et

al., 2019), selection of new employees (Sunarti et al.,

2017), and employee performance assessment

(Safrizal et al., 2019)

2 THEORETICAL REVIEW

In this study, two methods are used. Simple Additive

Weighting (SAW) and Profile Matching (PM). There

are eight steps calculating for the SAW method and

seven steps for the Profile Matching method.

2.1 Simple Additive Weighting (SAW)

The step by step SAW (Abdullah et al., 2018) method

calculating are:

1. Determine alternatives.

2. Determine the criteria that will be used as a

reference in making decisions.

3. Converting alternative values into the value of

the match rating on each criterion.

4. Determine the weight of preference or level of

importance (W) of each criterion W=[ W_1

W_2 W_3….W_j]

5. Make a decision matrix X which is formed from

the suitability rating table of each alternative on

each criterion.

6. Normalize the decision matrix by calculating the

value of the normalized performance rating r_ij

from alternative A_i on criteria C_j.

𝑟





⎩

⎪

⎨

⎪

⎧

𝑥



𝑚𝑎𝑥



𝑥





𝑖𝑓 𝑗 𝑖𝑠 𝑏𝑒𝑛𝑒𝑓𝑖𝑡

𝑚𝑖𝑛



𝑥





𝑥



𝑖𝑓 𝑗 𝑖𝑠 𝑐𝑜𝑠𝑡

(1)

7. The results of the normalized performance

rating form a normalized matrix (R)

𝑅

𝑟



𝑟



… 𝑟



𝑟



𝑟



…𝑟



 (2)

The final result of the preference value is obtained

from the sum of the normalized matrix row elements

(R) with the preference weights (W) corresponding to

the matrix column elements (W). The results of the

calculation if the Preference value of the larger

alternative identifies that the alternative is the best

alternative.

2.2 Profile Matching

Profile matching started with defining the minimum

value for each assessment variable. The difference

between each test data value against the minimum

value of each variable is a gap. Then the gap is

weighted. The weight of each variable will be

calculated on average based on the Core Factor (CF)

and Secondary Factor (SF) variable groups. The

composition of CF plus SF is 100%, depending on the

interests of the user of this method. The last stage of

this method is accumulating CF and SF values based

on the values of the testing data variables

(Dhammayanti et al., 2019), (Tharo & Utama

Siahaan, 2016). The smaller the gap produced by the

weight of significant value, the greater opportunity

for employees was occupying these positions (Sunarti

et al., 2017). The competency assessment system will

describe the achievements and potential of human

resources by their work units. Employee achievement

and competence can be a measure of employee

success in completing work.

The weighting of the Profile Matching method is

a definite value that is firm on a certain value because

the existing values are members of the crisp set. In a

crisp set, the membership of an element in the set is

stated explicitly, whether the object is a member of

the set or not, by using a characteristic function.

The steps for the profile matching method are:

1. Determine the required data variables.

2. Determine the aspects used for the assessment.

3. Gap profile mapping.

With the formula:

𝐺𝑎𝑝  𝐶𝑟𝑖𝑡𝑒𝑟𝑖𝑎𝑎𝑠𝑝𝑒𝑐𝑡𝑣𝑎𝑙𝑢𝑒 – 𝑇𝑎𝑟𝑔𝑒𝑡 𝑣𝑎𝑙𝑢𝑒

(3)

4. After obtaining the Gap value, then the weight

is given to each Gap value.

5. Calculation and grouping of Core Factor and

Secondary Factor. After determining the weight

of the gap value, then they are grouped into two

groups, namely:

 Core Factor, which is the most important

or prominent or most needed criteria

(competence) by an assessment that is

expected to obtain optimal results.

𝑁𝐶𝐹 

∑

𝑁𝐶

∑

𝐼𝐶

(4)

Information:

NFC : Average core factor

NC : Total value of core factor

IC : Number of the items core factor

 Secondary Factors (Supporting Factors)

are items other than aspects that exist in the

Decision Support Systems for Employee Performance Assessment

767

core factor. To calculate the secondary

factor, the formula is used.

𝑁𝑆𝐹 

∑

𝑁𝑆

∑

𝐼𝑆

(5)

Information:

NFS : Average secondary factor

NS : Total value of secondary factor

IS : Number of secondary factor items

6. Calculation of Total Value. Total value is

obtained from the percentage of core factors and

secondary factors that are estimated to affect the

results of each profile.

𝑁  𝑥 % 𝑁𝐶𝐹  𝑥 % 𝑁𝑆𝐹

(6)

Information:

N : Total score of criteria

NFS : Average secondary factor

NFC : Average core factor

(x) % : Entered percent value

7. Calculation of ranking determination. The final

result of the profile matching process is ranking.

Determination of ranking refers to the results of

certain calculations.

𝑅𝑎𝑛𝑘𝑖𝑛𝑔  𝑥 % 𝑁𝑀𝐴  𝑥 % 𝑁𝑆𝐴

(7)

Information:

NMA : Total score of Main Aspect criteria

NSA : Total score of Supporting Aspect criteria

(x) % : Entered percent value.

3 METHODOLOGY

Section 3 describes the design of the proposed

method and the steps to be completed. There are 4

criteria that influence employee performance

assessment. The alternatives used in the study were

15 employees.

3.1 Design Method

The study was designed to compare the output of the

use of the SAW and PM methods. It is measured

based on the results of alternative rankings. In the

SAW and PM methods, the criteria, alternatives, and

initial weights are prepared in advance, and then all

are presented in the form of a normalized matrix.

Furthermore, the normalized matrix can be processed

using SAW and PM methods. The design method of

this study is presented in Figure 1.

Figure 1: Design Method.

3.2 Criteria

In Table 1, the weight value of the criteria is based on

guidelines from the HRD of PT. Pertamina. The

greatest weight to the criteria of job security. The

lowest value weight is for the active criteria. In Table

2, the assessment uses a Likert scale between 1 – 5

for each choice. Safety criteria are compliance in

applying Safety operational standards during work.

Performance criteria are the ability of employees to

carry out their duties and awards achieved.

Achievement can be measured from discipline,

problem-solving ability, and the resulting product for

the company. Health criteria are the health condition

of employees for a certain period. Health conditions

include regular medical check-ups. Participation

criteria are the activeness of employees in responding

to a problem. Participation includes roles in

discussions, obeying orders from higher

management, and cooperating with the team.

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

768

Table 1: Alternative.

Table 2: Criteria.

Criteria Weight Type Scale Values

Safety 0,3 Benefit

Excellent

Good

Fair

Poor

Very

poo

Performance 0,25 Benefit

Excellent

Good

Fair

Poor

Very

poo

Health 0,25 Benefit

Excellent

Good

Fair

Poor

Very

poo

Participation 0,2 Benefit

Excellent

Good

Fair

Poor

Very

poo

3.3 Alternative

The alternative used is permanent employees at PT

Pertamina RU V Balikpapan. In this study, fifteen

alternative data were used. Alternative data are

presented in Table 2. Employee assessment is carried

out on employees with a tenure of more than five

years and a minimum position of Officer Head.

4 RESULT AND DISCUSSION

Section 4 contains the steps of the calculation Profile

Matching, SAW, and data testing. The selected test is

to calculate the accuracy of the method compared

with the selection results manually.

4.1 Simple Additive Weighting

The criteria and alternative data have been defined

based on Tables 1 and 2, so the next step is to

normalize the matrix. The step is to calculate the

value of the normalized performance rating 𝑟_𝑖𝑗 from

the alternative 𝐴_𝑖 on the criteria 𝐶_𝑗. If it is a benefit

criterion, then the value of 𝑥_𝑖𝑗 is divided by the value

of Max 𝑥_𝑖𝑗 from each column, while the criteria are

No Name Department Position Safety

Performanc

Health

Participatio

1 SR Equipment Overhaul Section Head EO Goo

Poor Goo

Fai

2 IS MA 3 Supervisor

Instrument

Good Fair Poor Fair

3 ABW HSC Shift Supervisor

Distill

Good Fair Fair Poor

4 AI Workshop Section Head

Workshop

Fair Good Poor Fair

5 NH HC Business Partner Officer II HC

Business Partne

Fair Good Poor Fair

6 BD Marine Region VI Assistance

Manager Plan

Fair Good Poor Good

7 L

Marine Region VI Officer PQC Fai

Goo

Fai

Poo

8 DW Project Engineering Sr Supervisor

Cost. Eng

Good Good Fair Poor

9 SH Laboratory Supervisor

Quality', N'Eng

Fair Poor Fair Fair

10 YM Laboratory Shift Supervisor

CONAL Gas',

N'Eng

Fair Fair Fair Fair

11 KM Laboratory Shift Supervisor

CONAL Gas

Fair Good Good Fair

12 IS Dis & Wax Section Head Dis

& Wax

Fair Fair Good Good

13 DEP General Maintenance Supervisor Elect

& Ins

Excellent Good Fair Good

14 BK Oil Movement Shift Supervisor

North Tank Farm

Fair Good Good Good

15 AF Dis & Wax Sr Supervisor

EWTP/DHP

Good Good Fair Fair

Decision Support Systems for Employee Performance Assessment

769

cost, the Min value of 𝑥_𝑖𝑗 from each column is

divided by the value of 𝑥_𝑖𝑗.

Table 3: Normalization Matrix.

Alt

Criteria

Safety Performance Health

Participatio

SR 0.80 0.50 1.00 0.75

IS 0.80 0.75 0.50 0.75

ABW 0.80 0.75 0.75 0.50

AI 0.60 1.00 0.50 0.75

NH 0.60 1.00 0.50 0.75

BD 0.60 1.00 0.50 1.00

0.60 1.00 0.75 0.50

DW 0.80 1.00 0.75 0.50

SH 0.60 0.50 0.75 0.75

YM 0.60 0.75 0.75 0.75

KM 0.60 1.00 0.50 0.75

IS 0.60 0.75 1.00 1.00

DEP 1.00 1.00 0.75 1.00

0.60 1.00 1.00 1.00

AF 0.80 1.00 0.75 0.75

The normalized matrix based on Table 3 is

multiplied by the weight criteria in Table 1 to get the

preference value. Furthermore, the final result or the

total preference value is obtained from the sum of the

normalized matrix row elements (R) and the weights.

The following Table 4 is the total preference value

and the ranking order based on the largest preference

value for each alternative.

Table 4: Preferences Value of Alternative.

No Alternative Preference Value Rank

1 SR 0.770 6

2 IS 0.700 14

3 ABW 0.720 9

4 AI 0.712 12

5 NH 0.714 11

6 BD 0.760 7

7 L

0.722 8

8 DW 0.780 5

9 SH 0.640 15

10 YM 0.710 13

11 KM 0.715 10

12 IS 0.820 4

13 DEP 0.940 1

14 B

0.880 2

15 AF 0.830 3

In Table 4, preference values based on the

calculation of the Simple Additive Weighting method

shown that Alternative 13 is ranked 1 with a value of

0.94, Alternative 14 is ranked 2 with a value of 0.88,

and Alternative 15 is ranked 3 with a value of 0.83,

and the following ranking with the preference value

of each employee.

4.2 Profile Matching

In the employee performance assessment calculation

using the profile matching method, the assessment

criteria are first defined. Assessment criteria contain

information about each variable's weight and type of

factor for each variable—the following Table 5 shows

information about the criteria.

Table 5: Assessment Criteria.

Criteria Wei

ht T

C1 Safet

30% Core Facto

C2 Performance 45% Core Facto

C3 Health 45% Secondary Facto

C4 Partici

ation 20% Secondar

Facto

There are two variables as core factors are Safety

and Performance. Two other variables as secondary

factors are Health and Participation. The weights for

each criterion have been presented in Table 5. The

Competency Standards used are 5 for Safety, 4 for

Performance, 4 for Health, 4 for Participation.

Aspects of the assessment criteria are defined as Very

Good equal to 5, Good equal to 4, Neutral equal to 3,

Poor equal to 2, Very Poor equal to 1. The following

in Table 6 describes the aspects of employee

assessment

Table 6: Aspects of Employee Assessment.

Alt

Safety

(C1)

Performance

(C2)

Health

(C3)

Participation

(C4)

SR 4 2 4 3

IS 4 3 2 3

ABW 4 3 3 2

AI 3 4 2 3

NH 3 4 2 3

BD 3 4 2 4

34 3 2

DW 4 4 3 2

SH 3 2 3 3

YM 3 3 3 3

KM 3 4 2 3

IS 3 3 4 4

DEP 5 4 3 4

34 4 4

AF 4 4 3 3

Based on the data on the aspect of the criterion

value, it can be seen that the ideal criterion is the Gap

value. The gap value is obtained from the standard

competency value, subtracted from each value aspect,

then the calculation of the gap value will be obtained

as shown in Table 7. The step to calculate the GAP

value is the standard competency value reduced by

the value of each criterion. For example, the

alternative SR has a GAP value of -1. This is because

SR has a safety value of 4-5, so that a value of -1 is

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

770

obtained. GAP calculations were carried out for all

alternatives against 4 criteria.

Table 7: Gap Value.

Alt Safety

(K1)

Performanc

e (K2)

Healt

h (k3)

Participatio

n (K4)

SR 4 2 4 3

IS 4 3 2 3

ABW 4 3 3 2

AI 3 4 2 3

NH 3 4 2 3

BD 3 4 2 4

3 4 3 2

DW 4 4 3 2

SH 3 2 3 3

YM 3 3 3 3

KM 3 4 2 3

IS 3 3 4 4

DEP 5 4 3 4

3 4 4 4

AF 4 4 3 3

Comp

etency

Stand

ard

5 4 4 4

SR -1 -2 0 -1

IS -1 -1 -2 -1

ABW -1 -1 -1 -2

AI -2 0 -2 -1

NH -2 0 -2 -1

BD -2 0 -2 0

-2 0 -1 -2

DW -1 0 -1 -2

SH -2 -2 -1 -1

YM -2 -1 -1 -1

KM -2 0 -2 -1

IS -2 -1 0 0

DEP 0 0 -1 0

-2 0 0 0

AF -1 0 -1 -1

After obtaining the Gap value for each criterion,

each employee's profile is assigned a weight value by

matching the Gap. The weight value based on PM

Method as shown as in Table 8.

Table 8: Weight Value for Gap.

No Gap Weight

1 4 5

2 3 4,5

3 2 4

4 1 3,5

5 0 3

6 -1 2,5

7 -2 2

8 -3 1,5

9 -4 1

The weight value based on the gap has been

obtained based on the guidelines from Table 8

(Dhammayanti et al., 2019). The next step is to

calculate the NCF and NSF values. NCF and NSF

calculation steps are based on formulas 4 and 5. Gap

values for all alternatives are based on Table 7. The

results of NCF and NSF calculations can be seen in

Table 9.

Table 9: NCF and NSF Value.

Alternative NCF NSF

2,25 2,75

2,5 2,25

ABW

2,5 2,25

2,5 2,5

2,5 2,25

2,75 2,25

2 2,5

2,25 2,5

2,5 2,25

2,25 3

DEP

3 2,75

2,5 3

2,75 2,5

NCF and NSF values are obtained for each

alternative. The next step is to calculate the total

value. The results of calculating the total weight and

ranking for the Profile Matching method are

presented in Table 10. The formula to calculate the

total value is based on Formula 6, and to get the

ranking value is based on Formula 7.

Table 10: Total Value and Rank.

Alternative Total Rank

2,4750 7

2,3871 10

ABW

2,3873 9

2,3870 11

2,3869 12

2,5000 6

2,3866 13

2,5250 5

2,2250 15

2,3625 14

2,3875 8

2,5875 4

DEP

2,8875 1

2,7250 2

2,6375 3

Based on the calculation of the Profile Matching

method, it was found that Alternative 13 was ranked

1 with a total value is 2.8875, Alternative 14 was

ranked 2 with a total value is 2.725, and Alternative

15 was ranked 3 with a total value is 2.6375, as well

as other rankings with a value of each employee. The

Decision Support Systems for Employee Performance Assessment

771

highest-ranking is based on the most significant total

value. All data can be seen in Table 10.

4.3 Discussion

The accuracy between the SAW and PM methods is

influenced by many factors. It can be caused by the

conversion of scale values which can affect the

difference in the ranking results and the far accuracy

results between the two methods. The completion

stage in PM is more effectively used in terms of

determining the best employees at PT Pertamina RU

V Balikpapan, compared to SAW. This can be seen

from the results of testing the method with data in the

field. The accuracy value of the PM method is higher

than the SAW method. In some conditions that

require accuracy of results, it is necessary to focus on

the final total score obtained, not only focus on

ranking. In more significant cases, other methods or

algorithms can be used so that the input value can

match the real conditions.

Table 11: Results of SAW and PM Method.

Alt

Results

Manual SAW

ABW

DEP

The accuracy is made by comparing the

calculation of manual data with the proposed method.

SAW method obtained conformity with the manual

data is 7 data. In contrast to SAW, for the PM method,

the similarity with the original data is 11 data. Based

on the similarity of data, the accuracy of the SAW

method is 46%, and PM is 73%. The test results are

described in Table 11. The coloured line indicates that

there is a discrepancy in the calculation results.

5 CONCLUSIONS

The decision support system was successfully

designed to select the best employees at PT Pertamina

RU V Balikpapan by applying the Simple Additive

Weighting and Profile Matching methods. Based on

the results of manual and system tests, the results

show that the SAW ranking method provides an

accuracy of 46% and the PM ranking method shows

an accuracy of 73%. In the cases of the best

employees at PT Pertamina RU V Balikpapan, the

Profile Matching method is more effectively used

because the method test provides a greater level of

accuracy than the Simple Additive Weighting

method. Providing criteria by combining methods

and machine learning such as naive Bayes or fuzzy in

the data analysis process so that the results obtained

are more accurate. The decision support system is

expected to be developed online so that employees

can access the calculation results in a transparent

assessment.

ACKNOWLEDGEMENTS

The collected data was obtained from PT. Pertamina

RU V Balikpapan, Indonesia.

REFERENCES

Abdullah, L., Adawiyah, C. W. R., & Kamal, C. W. (2018).

A decision-making method based on interval type-2

fuzzy sets: An approach for ambulance location

preference. Applied Computing and Informatics, 14(1),

65–72. https://doi.org/10.1016/j.aci.2017.04.003

Atmanegara, F. D. S., Putri, R. R. M., & Sutrisno. (2017).

Implementasi Metode Profile Matching untuk Seleksi

Penerimaan Anggota Asisten Praktikum ( Studi Kasus :

Laboratorium Pembelajaran Kelompok Praktikum

Basis Data FILKOM ). Jurnal Pengembangan

Teknologi Informasi Dan Ilmu Komputer, 1(12), 1804–

1812.

Bezdrob, M., & Šunje, A. (2021). Transient nature of the

employees' job satisfaction: The case of the IT industry

in Bosnia and Herzegovina. European Research on

Management and Business Economics, 27(2).

https://doi.org/10.1016/j.iedeen.2020.100141

Chiu, Y. Te, Zhu, Y. Q., & Corbett, J. (2021). In the hearts

and minds of employees: A model of pre-adoptive

appraisal toward artificial intelligence in organizations.

International Journal of Information Management,

60(June), 102379. https://doi.org/10.1016/j.ijinfomgt.

2021.102379

Dhammayanti, K., Wicaksana, A., & Hansun, S. (2019).

Position placement dss using profile matching and

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

772

analytical hierarchy process. International Journal of

Scientific and Technology Research, 8(11), 204–207.

Islami, X., Mulolli, E., & Mustafa, N. (2018). Using

Management by Objectives as a performance appraisal

tool for employee satisfaction. Future Business

Journal, 4(1), 94–108. https://doi.org/10.1016/j.fbj.

2018.01.001

Komsiyah, S., Wongso, R., & Pratiwi, S. W. (2019).

Applications of the fuzzy ELECTRE method for

decision support systems of cement vendor selection.

Procedia Computer Science, 157, 479–488.

https://doi.org/10.1016/j.procs.2019.09.003

Lei, C., Hossain, M. S., Mostafiz, M. I., & Khalifa, G. S. A.

(2021). Factors determining employee career success in

the Chinese hotel industry: A perspective of Job-

Demand Resources theory. Journal of Hospitality and

Tourism Management, 48(September 2020), 301–311.

https://doi.org/10.1016/j.jhtm.2021.07.001

Rahim, R. (2017). A Study Approach of Decision Support

System with Profile Matching. International Journal of

Recent Trends in Engineering and Research, 3(2), 31–

44. https://doi.org/10.23883/ijrter.2017.3001.zl93n

Roszkowska, E., & Kacprzak, D. (2016). The fuzzy saw

and fuzzy TOPSIS procedures are based on ordered

fuzzy numbers. Information Sciences, 369, 564–584.

https://doi.org/10.1016/j.ins.2016.07.044

Safrizal, Tanti, L., Puspasari, R., & Triandi, B. (2019).

Employee Performance Assessment with Profile

Matching Method. 2018 6th International Conference

on Cyber and IT Service Management, CITSM 2018,

Citsm, 1–6. https://doi.org/10.1109/CITSM.2018.

8674256

Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A. A.,

Ghorbani, M. A., & Shahbazi, F. (2018). Application of

SAW, TOPSIS, and fuzzy TOPSIS models in

cultivation priority planning for maize, rapeseed, and

soybean crops. Geoderma, 310(September 2017), 178–

190. https://doi.org/10.1016/j.geoderma.2017.09.012

Sunarti, S., Rangga, R. Y., & Marlim, Y. N. (2017).

Application Profile Matching Method for Employees

Online Recruitment. IOP Conference Series: Earth and

Environmental Science, 97(1). https://doi.org/10.1088/

1755-1315/97/1/012035

Tharo, Z., & Utama Siahaan, A. P. (2016). Profile Matching

in Solving Rank Problem. IOSR Journal of Electronics

and Communication Engineering, 11(05), 73–76.

https://doi.org/10.9790/2834-1105017376

Decision Support Systems for Employee Performance Assessment

773