Implementation of Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) Method in Determining The Best Employees

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Implementation of Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) Method in Determining The Best Employees

Yuyun Dwi Lestari^1,*, Arief Budiman¹, Dedy Irwan¹, Anggi Hanafiah²

1 Fakultas Teknik dan Komputer, Universitas Harapan Medan, Medan, Indonesia

2 Teknik Informatika, Universitas Islam Riau, Pekanbaru, Indonesia

Email: ^1,*[email protected], ²[email protected],³[email protected], ⁴[email protected] Coressponding Author: [email protected]

Submitted: 21/11/2022; Accepted: 30/11/2022; Published: 30/11/2022

Abstract−Employees are one of the wealth owned by the company. Employees are also able to advance the company and benefit the company if the employee has good performance. Therefore, decision makers in the company must determine the best employees in order to motivate their employees to work well. Decision makers are often faced with difficulties in decision making with so much data. To avoid this, a decision support system can be used with the method of MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS (MOORA) which can help determine the best employees by using predetermined criteria and weights. The criteria used are attendance, letter of reprimand, appreciation, responsibility and timely reporting of duties. This Moora method can help users in determining the Best Employees with optimization values based on these criteria.

Keywords: Best Employee; Decision Support System; MOORA Method

1. INTRODUCTION

An employee is any person who works by providing services to the company and the company will pay the wages that have been set in accordance with the agreement worked by the employee. However, the company will give rewards to employees who are considered to have better performance. The goal is that all employees in the company are enthusiastic in working and providing their respective performance to advance the company.

In these companies, decision makers are often faced with difficult choices in making decisions to determine the best employees with so much data. The company will hold a selection of employees who have the best performance to be awarded reward and other bonuses. The decision-making process must be based on criteria that are in accordance with the problem at hand. Because these criteria will greatly affect the results of the selection of the best employees. This decision in choosing the best employees aims to advance and provide many benefits for the company as well as for the employees themselves. Therefore, decision makers in the company must determine the best employees who aim to motivate employees in the company.

A Decision Support System is a part of a computerized information system including data used to support a company's decisions. The Decision Support System uses decision models and databases to achieve decision-making taken by managers. The decision support system helps managers in the company in making decisions from problems that are semi-structured, namely the type of data entered into the table. The concept of a decision support system can be used for small-scale, medium-scale or large-scale data use, depending on the problem that will be implemented into the Decision Support System.

There are many methods contained in the application of a Decision Support System that can be used to solve problems related to making a decision. The goal is that the results obtained later can be effective, efficient and optimal in determining a decision. To solve these problems, a Decision Support System will be created that will make it easier for managers to make decisions to determine the best employees by applying the MOORA (Multi-Objective Optimization On The Basis Of Ratio Analysis) method. The MOORA method is one of the methods that can be used to make decisions in a Decision Support System that will help determine the best employees. This method has a good degree of selectivity for determining alternatives. MOORA's approach is defined as a process simultaneously to optimize two or more conflicting constraints on multiple constraints. [1] The advantage of this MOORA method is that it is very simple and stable to implement, this method also has accurate and precise results.

There are several related studies that discuss the selection of the best employees and the MOORA method as in the Simatupang research, namely sistem supporting the decision to determine the best employee using the SAW method [2]. In the next study, Manurung was a decision support system for selecting the best teachers and employees using the MOORA method [3]. Firdaus Research, Decision support system for determining the best employees using AHP and TOPSIS methods [4]. Fadlan's research is to apply the MOORA method to the chili seed selection system [5]. And Wijaya's research selects outstanding employees by applying the AHP and VIKOR methods. [6]

The purpose of this study is to determine the best employees in the company based on predetermined criteria so as to get optimal results by applying the MOORA method. So that this research is useful for companies in choosing the best employees who will be rewarded and benefit from advancing the company

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2. RESEARCH METHODOLOGY

2.1 Decision Support System

Decision Support System is an information technology-based system that can produce alternative solutions to help solve semi-structured or unstructured problems in an organization or company. [5] Decision making is the process of choosing an action between several alternatives, so that the desired goal can be achieved. [6]

2.2 Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) Method

The Multi-Objective Optimization by Ratio Analysis

(

MOORA) method is a method introduced by Brauers and Zavadkas (2006). This relatively new method was first used by Brauers in a multi-criteria take.[9] The MOORA method has a degree of flexibility and ease of understanding in separating the subjective part of an evaluation process into decision weight criteria with several decision-making attributes This method has a good level of selectivity because it can determine the purpose of conflicting criteria. Where the criteria can be beneficial (benefit

)

or unprofitable (cost) [10]

[11].

The MOORA method has a degree of flexibility and ease of understanding in separating the subjective part of an evaluation process into decision weight criteria with several decision-making attributes. [1], [12] [13], The MOORA method consists of five main steps, namely as follows:

Step 1

The first step to be taken is to determine the direction of the goal and identify the attributes of the evaluation concerned.

Step 2

Displays all the information available for an attribute so that it can form a matrix in a decision. The data presented by equation 1 represents as matrix x. where Xij denotes the i measure of the alternative on the j attribute, m indicates the multiplicity of alternatives and n indicates the number of attributes. Then a ratio system is developed at each result of an alternative being compared to a denominator that represents all alternatives to the attribute as in the following equation (1):

𝑋 = [

𝑋11 𝑋12 . 𝑋1𝑁 𝑋21 𝑋22 . 𝑋2𝑁

. . .

𝑋𝑀1 𝑋𝑀2 𝑋𝑀𝑁

] (1)

Step 3

Brauers et al. (2008) concludes that the denominator, the best choice of the square root of the sum of the squares of each alternative per attribute. This ratio dapat is expressed in the following equation (2):

𝑋𝑖𝑗 = ^𝑋𝑖𝑗

√∑^𝑚_𝑗=1𝑥²𝑖𝑗

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Where Xij is the dimension value on the one that has the interval [0,1] presented normalized results to - i on attributes to-j.

Step 4

Untuk multi-objective optimization, The result of normalization is the addition in terms of maximization (of the beneficial attributes) and the subtraction in terms of minimization (of the unfavorable attributes. Furthermore, the problem of optimasi becomes like the following equation (3):

𝑌𝑖 = ∑^𝑔_𝑗=1𝑥𝑖𝑗− ∑^𝑛_𝑗=𝑔+1𝑋𝑖𝑗 (3)

Where g is the value of the criterion to be maximized, (n-g) is the value of the minimized criterion and Yi is the value of the alternative normalization assessment i against all attributes. In some cases, often observe some other more important criteria. order to provide more important attributes, it is performed with the appropriate weight (significant coeffesient). When the weight of this criterion is considered then the equation Yi is on the following equation (4):

𝑌𝑖 = ∑^𝑔_𝑗=1𝑊𝑗𝑋𝑖𝑗− ∑^𝑛_𝑗=𝑔+1𝑊𝑗𝑋𝑖𝑗 (4)

Where Wj is the weight of the attribute j.

Step 5:

The value Yi can be Positive or negative depending on the maximum (favorable criteria) and minimal (unfavorable criteria) in the decision matrix.

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3. RESULT AND DISCUSSION

On this issue, the selection of the best employees will be discussed using MOORA method. As for the first step that will be done in doing the calculation, it must determine the criteria to be used. These criteria can be seen in the following table 1:

Table 1. Criteria and Weight Values

Criterion Information Weight Value

C1 Presence 30%

C2 Letter Of Reprimand 5%

C3 Appreciation 25%

C4 Responsibility 20%

C5 Timely Task Reporting 20%

Alternative Assessment Data based on the above criteria can be seen in the following table.

Table 2. Assessing Each Alternative

No Employee Name C1 C2 C3 C4 C5

1 A 280 day 0 0 5 5

2 B 213 day 0 0 0 0

3 C 212 day 0 0 1 0

4 D 257 day 1 0 0 0

5 E 287 day 0 1 1 0

6 F 273 day 0 0 1 1

The alternative changes were obtained as follows:

Table 3. Changes in the Value of Each Alternative No Alternative C1 C2 C3 C4 C5

1 A1 3 3 1 3 3

2 A2 2 3 1 1 1

3 A3 2 3 1 2 1

4 A4 3 2 1 1 1

5 A5 3 3 2 2 1

6 A6 3 3 1 2 1

From the above table can be obtained the decision matrix as follows:

X = [

3 3 1 3 3

2 3 1 1 1

2 3 1 2 1

3 2 1 1 1

3 3 2 2 1

3 3 1 2 1]

Then Normalize the matrix as follows:

𝐶1 = √3²+ 2²+ 2²+ 3²+ 3²+ 3² = √9 + 4 + 4 + 9 + 9 + 9 = √44 = 6.633

X11 = 3/6.633 = 0.452 X21 = 2/6.633 = 0.302 X31 = 2/6.633 = 0.302 X41 = 3/6.633 = 0.452 X51 = 3/6.633 = 0.452 X61 = 3/6.633 = 0.452

𝐶2 = √3²+ 3²+ 3²+ 2²+ 3²+ 3² = √9 + 9 + 9 + 4 + 9 + 9 = √49 = 7

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X12 = 3/7 = 0.429 X22 = 3/7 = 0.429 X32 = 3/7 = 0.429 X42 = 2/7 = 0.286 X52 = 3/7 = 0.429 X62 = 3/7 = 0.452

𝐶3 = √1²+ 1²+ 1²+ 1²+ 2²+ 1² = √1 + 1 + 1 + 1 + 4 + 1 = √9 = 3

X13 = 1/3 = 0.333 X23 = 1/3 = 0.333 X33 = 1/3 = 0.332 X43 = 1/3 = 0.333 X53 = 2/3 = 0.667 X63 = 1/3 = 0.333

𝐶4 = √3²+ 1²+ 2²+ 1²+ 2²+ 2² = √9 + 1 + 4 + 1 + 4 + 4 = √23 = 4.8

X14 = 3/4.8 = 0.626 X24 = 1/4.8 = 0.209 X34 = 2/.633 = 0.302 X44 = 3/6.633 = 0.452 X54 = 3/6.633 = 0.452 X64 = 3/6.633 = 0.452

𝐶5 = √3²+ 1²+ 1²+ 1²+ 1²+ 1² = √9 + 1 + 1 + 1 + 1 + 1 = √14 = 3.74

X15 = 3/3.74 = 0.802 X25 = 1/3.74 = 0.267 X35 = 1/3.74 = 0.267 X45 = 1/3.74 = 0.267 X55 = 1/3.74 = 0.267 X65 = 1/3.74 = 0.267

Then can be seen the following normalized matrix:

X = [

0.452 0.429 0.333 0.626 0.802 0.302 0.429 0.333 0.209 0.267 0.302 0.429 0.333 0.417 0.267 0.302 0.286 0.333 0.209 0.267 0.302 0.429 0.667 0.417 0.267 0.302 0.429 0.333 0.417 0.267]

After that, calculate the Optimization Value for each given alternative. The value is the sum of the multiplication of the weight of the criterion by the value of the attribute.

𝑦 ∗ 1 = 0.452 ∗ 0.3 + 0.429 ∗ 0.05 + 0.333 ∗ 0.25 + 0.626 ∗ 0.2 + 0.802 ∗ 0.2 = 0.53 𝑦 ∗ 2 = 0.302 ∗ 0.3 + 0.429 ∗ 0.05 + 0.333 ∗ 0.25 + 0.209 ∗ 0.2 + 0.267 ∗ 0.2 = 0.29 𝑦 ∗ 3 = 0.302 ∗ 0.3 + 0.429 ∗ 0.05 + 0.333 ∗ 0.25 + 0.417 ∗ 0.2 + 0.267 ∗ 0.2 = 0.33 𝑦 ∗ 4 = 0.302 ∗ 0.3 + 0.286 ∗ 0.05 + 0.333 ∗ 0.25 + 0.209 ∗ 0.2 + 0.267 ∗ 0.2 = 0.33 𝑦 ∗ 5 = 0.302 ∗ 0.3 + 0.429 ∗ 0.05 + 0.667 ∗ 0.25 + 0.417 ∗ 0.2 + 0.267 ∗ 0.2 = 0.46 𝑦 ∗ 6 = 0.302 ∗ 0.3 + 0.429 ∗ 0.05 + 0.333 ∗ 0.25 + 0.417 ∗ 0.2 + 0.267 ∗ 0.2 = 0.38

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From the results of the previous Optimization Value calculation, the results can be sorted from largest to smallest where the highest value is the best alternative to the existing data and will be the chosen alternative. It can be seen in the following table 4:

Table 4. Crisps Alternative Yi(max) Crisps

A1 0.53 1

A5 0.46 2

A6 0.38 3

A3 0.33 4

A4 0.33 5

A2 0.29 6

From this process, it can be produced that A1 is the best alternative.

4. CONCLUSION

The conclusions drawn from the research are by applying the Moora method, it can help users in determining the Best Employees with optimization values based on specified criteria. The Decision Support System is able to provide alternatives to determine the best employees using the Moora method.

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