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APPLICATION OF PROFILE MATCHING ALGORITHM IN SELECTION OF 
THE BEST EMPLOYEES IN PROPERTY COMPANY 

 
Laeli Nurchasanah1, Annisa Cintakami Firdaus2,  Desti Fitriati3 

 
Informatics Engineering Study Program1,2,3 

Universitas Pancasila1,2,3 

laelifebry90@gmail.com1, cintakamiannisa@gmail.com2, desti.fitriati@univpancasila.ac.id3 

 
(*) Corresponding Author 

 
Abstrak 

Pemberian pengharagaan bagi karyawan yang memiliki kelebihan dan kinerja kerja yang baik merupakan 
salah satu cara untuk meningkatkan daya saing yang positif antar karyawan di suatu perusahaan. Penelitian 
ini bertujuan untuk mencari keunggulan yang dimiliki setiap karyawan agar dapat mengetahui karyawan 
yang berprestasi.  Melalui prestasi dalam dunia kerja dapat menjadi tolak ukur untuk mencari karyawan 
terbaik yang layak mendapatkan penghargaan. Analisa data yang digunakan dalam penelitian ini bersumber 
dari data penjualan perusahaan properti selama tiga bulan terakhir. Penelitian ini menggunakan Metode 
Profile Matching untuk menentukan karyawan terbaik di perusahaan properti. Penelitian dengan ini 
dilakukan dengan cara membandingkan karyawan satu dengan kandidat karyawan yang lainnya 
berdasarkan kriteria – kriteria yang telah ditentukan. Hasil dari penelitian ini berupa perangkingan yang 
menunjukan urutan karyawan terbaik yang berhak mendapat penghargaan dari perusahaan.  
 
Kata kunci: Profile matching, Sistem pendukung keputusan, GAP 
 

Abstract 
Giving awards to employees who have advantages and good work performance is one way to increase positive 
competitiveness among employees in a company. This study aims to find the advantages of each employee to 
find out which employees excel. Through achievements in the world of work, it can be a benchmark for finding 
the best employees who deserve awards. Analysis of the data used in this study is sourced from data on sales of 
property companies for the last three months. This study uses the Profile Matching Method to determine the 
best employees in property companies. This research was conducted by comparing one employee with another 
employee candidate based on predetermined criteria. The results of this study are in the form of rankings that 
show the order of the best employees who are entitled to an award from the company. 
 
Keywords: Profile matching, Decision support system, GAP 
 
 
 

INTRODUCTION 
 
Human resources are one of the main 

factors to create an effective and efficient 
performance in a company (Sholihaningtias, 2018). 
The placement of employees according to their 
knowledge and skills will improve the performance 
of the employee (Sugiartawan, Ardriani, & Kusuma, 
2021). Finding the right person worthy of 
appreciation is not easy. A person's performance is 
influenced by many factors such as personality and 
behavioral factors (Herlambang, Dewanto, 
Harjanta, & Setyawati, 2018)  The selection of the 
best employees aims to increase the competence of 
employees (Idam, Junaidi, & Handayani, 2019). 
Computerization or the use of computers on a large 
scale in terms of processing data, one of which is the 

decision-making process for selecting the best 
employees can minimize the occurrence of 
inaccuracies in decision making (Fitriana, Ripanti, & 
Tursina, 2018). To make a decision, of course, it is 
necessary to do careful analysis and calculations by 
predetermined criteria. Decision-making is 
generally done using more than one criterion or 
even many criteria. Therefore, an assessment 
decision model is needed to make it easier for the 
assessment team to make decisions about who 
deserves to be the best employee at property 
company. The assessment factors that will be 
carried out include several criteria, including based 
on discipline, number of absences, accumulation of 
surveyed consumers, the number of bookings, the 
number of consumers of credit contracts, and length 
of work. 

mailto:laelifebry90@gmail.com1
mailto:cintakamiannisa@gmail.com2


 

 
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The Profile Matching method is one of the 
approach methods from several studies in decision 
support systems (Fitriana et al., 2018)). The final 
result of this method aims to produce data in the 
final form of ranking (Ermawita & Fauzi, 2020) With 
a decision support system, it is hoped that it can 
support decisions that will be taken by the selection 
team in a company (Wahyudi & Utama, 2019). 
Currently, at this property company's marketing 
performance assessment is still not available. To 
overcome this problem, manual calculations and 
systems are made using the Profile Matching 
algorithm. This method was chosen in the hope of 
being able to produce the best data and is also 
expected to help determine who is the best 
marketing actor who deserves a reward from the 
property company. The purpose of the study was to 
produce a decision support system for selecting the 
best employees according to predefined criteria. 
 

RESEARCH METHODS 
 

Types of Research 
The data obtained in this study is sourced 

from primary data provided directly by the 
marketing manager of one of the property 
companies in Indonesia. The data provided is sales 
data in the last three months (August, September, 
and October). 

 
Time and Place of Research 

The research was conducted over the last 
three months from August 2021 to early November 
2021. The research was conducted at the marketing 
office of a property company in Cibinong. Sales data 
from the last three months was obtained after the 
interview ended. 

 
Decision Support System 
 Decision Support Systems (DSS) is a part of 
a computer-based (including knowledge-based) 
information system that is used to support decision-
making in an organization or company (Wahyudi & 
Utama, 2019). DSS by utilizing data and models to 
identify, solve problems, and make decisions aims 
to help make better decisions (Sutinah, 2017). In 
general, a decision support system is designed as a 
support for decision-making in a problem that 
involves many criteria (Fauzi, 2019) 
 The stages of the Decision Support System 
are problem definition, collecting data or relevant 
information elements, processing data into 
information in the form of graphic and written 
reports, and determining alternative solutions (can 
be in percentages). The objectives of DSS include 
helping to solve semi-structured problems, 
supporting managers in making decisions about a 

problem, and increasing the effectiveness not the 
efficiency of decision making. 
 
Profile Matching 
 Profile Matching is a component that 
expects an ideal indicator variable level with a base 
level that should be met or passed by workers 
(Kurnia, 2021). In Profile Matching, the minimum 
value for each variable is called the GAP value. The 
smaller the GAP value, the greater the weight 
(Herlambang et al., 2018).  The profile matching 
method is commonly used in solving semi-
structured problems in solving problems of 
determining the best employees, selecting 
scholarships, and selecting collaboration partners 
(Indriyani, 2019).  
 
Procedure 

The Profile Matching process includes 
several stages as follows: 
1. Investigation of a problem 
2. Determine criteria, sub-criteria, and values 
3. Determining the value of each alternative 
4. Determination of target values 
5. Determine the GAP 
 
The gap is the difference in the value obtained from 
the selection team for employees who are 
nominated for the best employee (Saputra, 
Regasari, & Sutrisno, 2017). Gap collection for each 
attribute has a different calculation for each 
problem. The gap value is obtained from the 
following formula. 
 
Gap = Value – Standard Value ..................................... (1) 

 
6. Convert GAP to the weighted value 
7. Calculation of core factor and secondary factor 

 
 In the calculation of the Profile Matching 
method, the core factor and secondary factor have 
different assessment weights (Sudarmadi, Santoso, 
& Sutrisno, 2017). The core factor is the attribute 
that takes precedence or is the most prominent in a 
position  (Pungkasanti & Nurma’arif, 2019). The 
core factor aspect is estimated to produce optimal 
performance. The calculation of the core factor is 
formulated as follows. 
 

𝑁𝐶𝐹 =  
Ƹ𝑁𝐶

Ƹ𝐼𝐶
  ......................................................................... (2) 

 
Description : 
NCF = average core factor 
NC = total number of core factor values 
IC = number of aspects core factor (Malau, 2020) . 
 



 

 
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 The secondary factor is supporting aspects 
other than those in the core factor (Pungkasanti & 
Nurma’arif, 2019). The Secondary factor calculation 
can be seen in the following formula. 
 

𝑁𝑆𝐹 =  
Ƹ𝑁𝑆

Ƹ𝐼𝑆
  .......................................................................... (3) 

 
Description : 
NSF = average value of secondary factor 
NS = total number of secondary factor scores 
IS = number of aspects of a secondary factor 
(Kusumantara, Pamuji, & Putri, 2019). 
8. Sum of core factor and secondary factory value 
9. Calculation of criteria value 
10. Determine the total value 

The calculation of the total value is obtained 
from the following formula.  

 
NP = (x) % NCF + (x) % NSF......................................... (4) 
 
Description: 
NP = total score of criteria 
NFS = average score secondary factor average 
NFC = core average factor 
(x) % = the percent value that input. 
 
11. Rank 

After going through the calculation process 
above, researchers will get conclusions about the 
value of several alternatives that have been 
inputted. The highest scoring alternative will be a 
priority to choose the best employee and will be 
entitled to a gift from the property company. 

 
Data, Instruments, and Data Collection 
Techniques 

This study uses primary data. The 
technique of collecting criteria and alternative data 
that will be used in system testing is obtained by 
conducting direct interviews with the marketing 
manager of a property company. For information on 
sales data for the last three months, it was obtained 
directly in hardcopy form by the marketing 
manager of the property company after the 
interview. 

 
Data Analysis Technique 

The data in this study consisted of 
quantitative and qualitative data. For qualitative 
data, researchers convert it into numbers 
(quantitative data) to make it easier to calculate 
both manual and system calculations. 
 
 
 
 

RESULTS AND DISCUSSION 
 

The first step for Profile Matching is to 
determine the alternatives and criteria used. 
Alternatives can be seen in table 1. 

 
Table 1.  Alternatives 

Alternate Code Alternative Name 

A1 Kahfi 
A2 Hendri 
A3 Surya 
A4 Winda 

 
 After determining the alternatives, the 
criteria and values are determined as in table 2. 
 

Table 2. Criteria 

 
 Table 2 contains the type of each criterion 
specified by the company. The score for the criteria 
obtained from the property company can be seen in 
table 3. 
 

Table 3. Criteria Value of Each Alternative 
Criteria Alternative 

Kahfi Hendri Surya Winda 

Discipline Good Excellent Enough Enough 

Number of 
Absences 

1 0 4 3 

Accumulated 
Consumer Survey 

15 12 10 20 

Number of 
Bookings 

5 5 5 12 

Jumlah Credit 
Akad Consumer 

5 4 4 10 

Long Work 7Years 7 Years 5 Years 3 Years 

 
 After knowing the information about the 
value of each criterion for 4 predefined alternatives. 
The weight value of the criteria expected by the 
company can be seen in table 4. 
 
 
 
 
 
 

Table 4. Criteria Weight Value from the Company 



 

 
137 

 

 

 
 The next step is to determine the value of 
the core factor and secondary factor. The value can 
be seen in table 5. 

 
Table 5. Weight of Core Factor and Secondary 

Factor 
Core Factor 60% 

Secondary Factor 40% 

  
 The next step is to determine the gap 
mapping. The results can be seen in table 6. 
 

Table 6. Gap Mapping 
Criteria Assessment 

Factor 
Weight 

Discipline Excellent 5 
Good 4 
Enough 3 
Less 2 
Ineligible 1 

Number of 
Absences 

0 5 
1 - 3 4 
4 - 6 3 
7 - 9 2 
>9 1 

Accumulated 
Consumer 
Survey 

>15 5 
11 - 15 4 
6 - 10 3 
1 - 5 2 
0 1 

Number of 
Bookings 

> 12 5 
9 - 12 4 
5 - 8 3 
1 - 4 2 
0 1 

Number of 
Consumer 
Credit 
Agreements 

> 9 5 
7 - 9 4 
4 - 6 3 
1 - 3 2 
0 1 

Working 
Period 

> 6 Years 5 
5 - 6 Years 4 
3 - 4 Years 3 
 1 – 2 Years 2 
< 1 Year 1 

 
 The results of the gap calculation can be 
seen in table 7. 

 
Table 7. Gap Calculation 

  
 The next step is determining the weight of 
the gap value based on table 8. 
 

Table 8. Weighted Gap  Value 

  
The results can be seen in table 9. 
 

Table 9. Weighted Results 

  
 The next step is to calculate the value of the 
core factor and secondary factor which is then 
multiplied directly by the weight. The results can be 
seen in table 10. 

Table 10. Calculation Results of Core Factor and 
Secondary Factor 



 

 
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Employees NCF NSF Core 
Factor 
60% 

Secondary 
Factor 
40% 

A1 3,6 4,5 2,16 1,8 
A2 4 4,5 2,4 1,8 
A3 3 5 1,8 2 
A4 4,2 4 2,52 1,6 

 
 The final assessment calculation is determined 
using formula 4. The final calculation results can be 
seen in table 11. 
 

Table 11. Ranking Values 
Employees NP Ranking 

Hendri 4,2 1 
Winda 4,12 2 
Kahfi 3,96 3 
Surya 3,8 4 

 
 Based on table 11, it is known that the 
order of employees who deserve to be appreciated 
are Hendri, Winda, Kahfi, and Surya. 
 
System Result 
 Implementation to determine the best 
employees as shown in figure 1. Input the username 
and password to enter the system. Input 
alternatives, criteria, core factor value, and 
secondary factor value. 
 

 
Figure 1. System Main Page 

  
 After successfully inputting the value, click 
the "Continue To Process" button. To find out the 
rankings, click the "Save and View Ratings" button. 
 

 
Figure 2. Ranking Results 

 

 Figure 2 is the result of the calculation 
according to the criteria and alternatives that have 
been determined at the beginning. 
 
Evaluation Result 
 Accuracy testing aims to find out how much 
accuracy the level of the system has been made.  For 
accuracy testing, the results of the decision support 
system with the profile matching method will be 
compared between manual calculations and the 
system. Accuracy is determined by the following 
formula. 
 

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =  
𝑇𝑃+𝑇𝑁

𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
𝑥 100% ........................... (5) 

 
Description : 
TP = True Positive 
TN = True Negative 
FN = False Positive 
FN = False Negative 
 
 The research was conducted using 30 
sample data. By using the formula above obtained 
an accuracy value of 100%. 

 
CONCLUSIONS AND SUGGESTIONS 

 
Conclusions 

Decision Support Systems using Profile 
Matching Algorithms can help the role of property 
management in objective decision making with 
more effective time. The implementation of the 
Profile Matching method for the selection of the best 
employee selection at property company obtained 
excellent results by its accuracy rate of 100%. 

 
Suggestions 

In the next research, Decision Support 
Systems using Profile Matching Algorithms could be 
created not only to determine the best employees in 
property companies but also to be applied to other 
fields to increase the variety of use of Profile 
Matching Algorithms. 
 

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