IT Support Division Employee Selection Decision Support System Using Simple Additive Weighting Method ^†

Abstract

In today’s digital era, the need for a qualified workforce in the field of IT Support is in-creasing along with the rapid development of information technology. The selection of the right employees is very important to ensure the efficiency and effectiveness of companies’ operations. This research aims to develop a decision support system (SPK) in the IT Support division employee selection process using the Simple Additive Weighting (SAW) method. This method was chosen because of its ability to process qualitative and quantitative data to produce optimal decisions. The results of this study show that the proposed system can assist managers in selecting the most suitable candidates based on predetermined criteria. Thus, it is expected that this system can improve the quality of the recruitment process in companies.

1. Introduction

In an increasingly competitive business world, companies are required to have reliable and professional IT Support teams. These divisions play an important role in maintaining the smooth operation of information technology in companies. According to data from the Indonesian Internet Service Providers Association, by 2022, the number of internet users in Indonesia reached 202.6 million, which shows the importance of adequate technology support [1]. Therefore, selecting the right employees for IT Support divisions is crucial.

The employee selection process is often faced with various challenges, such as a large number of applicants and diverse assessment criteria. In this case, a systematic and objective method for assessing candidates is needed. Simple Additive Weighting (SAW) is one of the methods that can be used to evaluate candidates and to solve this problem. The SAW method works by giving weight to each existing criterion, making it easier to determine the most suitable candidate [2].

Decision support systems (SPKs) using the SAW method can help managers make better and faster decisions. With an SPK, the employee selection process becomes more transparent and accountable [3,4,5]. The application of the SAW method in employee se-lection has proven effective in improving the quality of decisions taken. This shows that the use of technology in the recruitment process can have a positive impact on companies [6,7,8].

In addition, in the context of companies engaged in technology, the use of the right method in employee selection will affect the overall performance of teams [9,10]. Well-selected employees will contribute to increased productivity and innovation in companies [11,12]. Therefore, this research aims to implement the SAW method in an IT Support division employee selection decision support system, so that it can make a significant contribution to companies [13].

With this background, this research is expected to provide new insights into the application of the SAW method in employee selection and provide useful recommendations for companies in improving the quality of their IT Support team [14].

2. Research Method

2.1. Selection Criteria Determination Method

2.1.1. Criteria Identification

Criteria that are relevant in the selection of IT Support division employees include technical knowledge, work experience, interpersonal skills, and others.

2.1.2. Determination of Weight

Measurement of the level of importance of each criterion is used in the assessment, through a consultation approach with relevant experts and stakeholders.

2.2. Candidate Data Collection

2.2.1. Data Sources

Data sources are identified that are used to evaluate candidates, such as test results, work history, and other assessments.

2.2.2. Measurement Criteria

Relevant and measurable data are collected for each predetermined criterion.

2.3. Data Normalization

Normalization of Criteria Values

Change he criteria data into a form that can be compared, for example, in a certain scale range (0–1 or 0–100).

2.4. Ranking and Selection of Employees

2.4.1. Determination of Relative Weight

The value of each criterion is multiplied with the predetermined weight.

2.4.2. Employee Prioritization and Selection

The results of multiplying the weights with the criteria values for each candidate are added up, to obtain the final score.

2.5. Assessment, Ranking, and Selection of Employees

2.5.1. Ranking Generation

Candidates are ranked based on the final score from highest to lowest.

2.5.2. Employee Selection

The best candidate is selected based on the rankings that have been generated.

2.6. Method Evaluation and Development

2.6.1. Selection Result Analysis

The suitability of the selected employees is evaluated with their performance in the IT Support division.

2.6.2. Method Development

If necessary, improvements or adjustments are made to the criteria or weights used in the selection process based on the evaluation results.

2.7. Validation and Verification

2.7.1. Validation of Result

It is ensured that the selection process using the SAW method provides consistent and reliable results.

2.7.2. Verification Against Standards

The selection results are compared with the predetermined standards or performance indicators.

2.8. Continuous Use of SAW Method

2.8.1. Training and Development

Training is conducted with the HR team or related parties to optimize the use of the SAW method in the selection process.

2.8.2. Continuous Monitoring and Evaluation

Monitoring and evaluating the effectiveness of the SAW method in the selection of IT Support division employees are continued, by making periodic improvements.

This research method is expected to provide a strong foundation in implementing the SAW method in the employee selection process of IT Support divisions, as well as increasing accuracy and objectivity in determining the best candidates that match the needs and criteria.

3. Results and Discussion

Let us sort the steps in detail with an example of calculations using the SAW (Simple Additive Weighting) method for the selection of an IT Support division.

3.1. Example of Calculation with SAW Method

3.1.1. Determination of Selection Criteria

After determining the criteria and the weight of each criterion, the next step is to collect candidate data; from the data, we obtain that there are three people who are candidates to take part in employee screening before finally being determined as candidates who are selected to become employees. The following are the data of the three candidates (see

Table 1. Criteria and weight requirements.

Criteria Code	Criteria	Weight
C1	Technical Knowledge	0.4
C2	Work Experience	0.3
C3	Interpersonal Skills	0.3

and

Table 2. Candidate data requirements.

Candidate	Criteria
	C1	C2	C3
Candidate A	80	5	75
Candidate B	90	3	80
Candidate C	85	4	70

).

The criteria and their weights are presented in Table 1, while the candidate data are shown in Table 2.

The following is the formula for normalizing data:

$$\mathrm{Normalization}\mathrm{Value}={\displaystyle \frac{\mathrm{Original}\mathrm{Value}-\mathrm{Minimum}\mathrm{Value}}{\mathrm{Maximum}\mathrm{Value}-\mathrm{Minimum}\mathrm{Value}}}$$

where

• Original Value is the actual value of each criterion.
• Minimum Value is the smallest value of each criterion among all candidates.
• Maximum Value is the largest value of each criterion among all candidates.

This formula is used to convert existing values into a range of 0 to 1 so as to allow a balanced comparison between candidates based on each criterion that has been set in the selection process with the SAW method.

3.1.2. Determine the Minimum and Maximum Value of Each Criterion

• Technical Knowledge:
• Minimum Value: 80 (Candidate A).
• Maximum Value: 90 (Candidate B).
• Work Experience:
• Minimum Value: 3 years (Candidate B).
• Maximum Value: 5 years (Candidate A).
• Interpersonal Skills:
• Minimum Value: 70 (Candidate C).
• Maximum Value: 80 (Candidate B).

3.1.3. Calculate Normalization for Each Candidate in Each Criterion

Technical Knowledge:

Candidate A:

• Technical Knowledge = (80 − 80)/(90 − 80) = 0/10 = 0
• Work Experience = (5 − 3)/(5 − 3) = 2/2 = 1
• Interpersonal Skills = (75 − 70)/(80 − 70) = 5/10 = 0.5

Candidate B:

• Technical Knowledge = (90 − 80)/(90 − 80) = 10/10 = 1
• Work Experience = (3 − 3)/(5 − 3) = 0/2 = 0
• Interpersonal Skills = (80 − 70)/(80 − 70) = 10/10 = 1

Candidate C:

• Technical Knowledge = (85 − 80)/(90 − 80) = 5/10 = 0.5
• Work Experience = (4 − 3)/(5 − 3) = 1/2 = 0.5
• Interpersonal Skills = (70 − 70)/(80 − 70) = 0/10 = 0

3.2. Calculation of Weight and Final Score

Final Score Calculation

Candidate A: (0.4 × 0) + (0.3 × 1) + (0.3 × 0.5) = 0 + 0.3 + 0.15 = 0.45

Candidate B: (0.4 × 1) + (0.3 × 0) + (0.3 × 1) = 0.4 + 0 + 0.3 = 0.7

Candidate C: (0.4 × 0.5) + (0.3 × 0.5) + (0.3 × 0) = 0.2 + 0.15 + 0 = 0.35

3.3. Employee Ranking and Selection

Ranking Results

Candidate B: Final Score = 0.7

Candidate A: Final Score = 0.45

Candidate C: Final Score = 0.35

3.4. Discussion of Results

Based on calculations with the SAW method, Candidate B achieves the highest rank with a final score of 0.7, followed by Candidate A with a score of 0.45 and Candidate C with a score of 0.35. The predetermined criteria have different weights and influence the final score of each candidate. Candidate B is rated the highest because they have the best performance across several criteria.

4. Conclusions

From the calculation using the Simple Additive Weighting (SAW) method for the selection of an IT Support division with examples of criteria and weights that have been determined, the following can be concluded:

• Candidate B ranked highest with a final score of 0.7, followed by Candidate A with a score of 0.45 and Candidate C with a score of 0.35.
• The assignment of weights to each criterion had a significant influence on the candidate’s final score. Candidate B was rated the highest due to their good performance across several criteria, according to the weights assigned.
• The SAW method enables objective assessment and scalable decision-making in employee selection. This approach combines multiple criteria with different weights, allowing for a comprehensive evaluation of candidates.
• However, it is important to remember that the calculation example above is just a simple simulation. In a real context, the selection process requires more in-depth analysis and adaptation of criteria and weights according to the needs and priorities of each company.

By using the SAW method, companies can make more measurable selection decisions, optimizing objectivity in determining the best employees according to predetermined criteria.