Decision Support System for Assessing Teacher Performance Using the Simple Additive Weighting (SAW) Method at SMK XYZ ^†

Abstract

SMK XYZ is a private school under Yayasan Pembina Pendidikan Doa Bangsa (YPPDB) which was established in 2011. The school has several expertise programs, including Software Engineering, Institutional Accounting and Finance, and Motorcycle Business Engineering. Assessing the success of a school is an important thing that greatly affects the development of students in the learning process to achieve their goals. Assessment of teachers’ work should be performed using appropriate and efficient methods. To improve teacher performance, the development of an agenda monitoring and assessment system based on the Simple Additive Weighting (SAW) method can be an effective alternative. This system is designed to assist school management in monitoring teacher activities objectively and measurably, as well as providing clear assessments based on certain criteria such as attendance, tardiness, student evaluation results, and innovation in learning. The SAW method is used to calculate the final score of teacher performance by summing up the weighted values of each normalized criterion. In this case study, the system helps decision makers to recognize the strengths and weaknesses of each teacher, so that related recommendations for competency development can be given. The implementation of this system demonstrates increased responsibility in appraisal and motivates teachers to improve their performance according to set standards.

1. Introduction

SMK XYZ is a private school under the Yayasan Pembina Pendidikan Doa Bangsa (YPPDB) that was established in 2011. The school has several skill programs, including Software Engineering, Institutional Accounting and Finance, and Motorcycle Business Engineering. Assessing the success of a school is an important thing that greatly affects the development of students in the learning process to achieve their goals. Assessment of teachers’ work should be performed using appropriate and efficient methods.

A Decision Support System (DSS) is a computer-based system used to assist decision-making in complex and unstructured situations. SPK is designed to support, not replace, decisions made by humans. According to Turban et al. (2011), SPK integrates data, analysis models, and support tools to assist decision makers in solving certain problems [1].

Teacher performance evaluation is the process of assessing a teacher’s ability, competence and performance in carrying out their duties and responsibilities. According to Permendikbud No. 16/2007, teacher performance is assessed based on four main competencies: pedagogical, professional, social, and personality [2].

Teacher performance evaluation aims to improve the quality of education by identifying teachers’ strengths and weaknesses and providing a basis for professional development [2]. In the context of education, CBMS can be used for various purposes, such as teacher performance evaluation, selection of learning methods, and management of school resources. The application of CBMS can increase objectivity and accuracy in the decision-making process. To improve teacher performance, the development of an agenda monitoring and assessment system based on the Simple Additive Weighting (SAW) method can be an effective alternative. This system is designed to assist school management in monitoring teacher activities objectively and measurably, as well as providing clear assessments based on certain criteria such as attendance, tardiness, student evaluation results, and innovation in learning. The SAW method is used to calculate the final score of teacher performance by summing the weighted values of each multi-criteria [3] that have been normalized. According to Saputra [4], the application of the SAW method in assessing teacher performance produces recommendations that assist school management in making strategic decisions, such as giving awards or determining training needs. The SAW method is an effective tool in supporting decision-making, especially in evaluating teacher performance. The application of SAW-based SPK at SMK XYZ is expected to improve the efficiency, accuracy, and objectivity of the assessment process, thus supporting the development of quality education. In this case study, the system helps decision makers to recognize the strengths and weaknesses of each teacher, so that related recommendations for competency development can be given. The implementation of this system demonstrates increased responsibility in appraisal and motivates teachers to improve their performance in accordance with established standards. The purpose of this research is to change the assessment data that is carried out manually into a computerized form that can monitor the progress of performance and assessment and provide recommendations for the best teacher using the SAW method.

2. Materials and Methods

The research methodology used for the Decision Support System to Assess Teacher Performance Using the Simple Additive Weighting (SAW) Method at SMK XYZ follows a structured process. The stages include identifying and determining problems, preparing methods, collecting and analyzing data, implementing the system, and applying the SAW method to produce results. The research stages are outlined in Figure 1.

Figure 1. Research stages.

2.1. Problem Identification

Problem identification is carried out at one of the private vocational schools in the Sukabumi Regency, which still uses manual methods for assessing teacher performance. Steps are needed to design, develop, and evaluate an effective Decision Support System using the Simple Additive Weighting (SAW) method. The application of the SAW method in selecting the best teacher and focusing on determining the weight of criteria such as attendance, administration, target achievement, problem solving, and discipline is as follows [5].

2.2. Simple Additive Weighting (SAW) Method

The method used in Simple Additive Weighting (SAW) has the following stages:

2.2.1. Data Collection

Presents methods that can be used to collect data, process it, and draw reliable conclusions [6] by taking from the case study location, SMK XYZ, the following attributes:

• Teacher Data

In teacher data, researchers take assumptions as simulation data in the calculation of the Simple Additive Weighting (SAW) method.

• Attendance Data

A collection of attendance data, part of the institution’s activity report, a component of the institution that contains attendance information, arranged and organized in such a way that it is easy to find and by interested parties when needed [7]. All activities that require information about their participants must exist, as well as the learning process. Absenteeism is often a tedious task for attendance supervisors and is part of the daily tasks that scholars must perform [8]. One of the uses of this student absence information is to calculate the likelihood of students taking tests, and another use of this attendance data is for the organizers of teaching and learning activities to measure student satisfaction with the subject and set future standards for better delivery.

• Work Program Data

According to Hans Hochholzer in E Hetzer [9], a program is a collection of real, systematic, and integrated activities carried out by one or several government agencies in cooperation with the private sector and the community in order to achieve the goals and means set. A program is prepared based on the objectives or targets to be achieved. The program planning arrangement is referred to as a work program. According to Santosa in Soesanto [10], a work program is a system of activity plans that is directed, integrated, and systematized for a predetermined time span by an organization. The work program is a guide for the organization in carrying out organizational routines. Work programs are also used as a means to realize organizational goals. In addition, one of the definitions of work programs is real programs that are possible to implement to achieve the mission of the company or organization, in line with the above views. E Hetzer [9] argues that a work program is an activity that describes in advance the part of the work to be carried out, along with instructions on how to carry it out. This activity of describing in advance also usually concerns the period of completion, the use of materials and equipment needed, the division of authority, and other responsibilities and clarity deemed necessary. According to E Hetzer, [9].

• Main Activities

In the main activity data, researchers take assumptions as simulation data on the calculation criteria for the Simple Additive Weighting (SAW) method.

2.2.2. Determination of Assessment Criteria

An objective and systematic assessment method is needed to avoid bias and provide valid results, namely by using the Simple Additive Weighting method, which is one of the methods in SPK used to solve multi-criteria problems.

2.2.3. Criteria Weight

The Simple Additive Weighting (SAW) method is known as the weighted sum approach, where each criterion is given a certain weight according to its level of importance. The final value is obtained by summing up the results of the multiplication between the weight of the criteria and the alternative value for each criterion. Criteria weights are given according to the priority of the assessment. The system must receive input weights for each of the Cognitive Domain Aspect (ARK) criteria.

2.2.4. Normalization and Ranking Process of Final Assessment

According to Fishburn [11], the SAW method has advantages in the ease in implementation, transparency, and the ability to handle both quantitative and qualitative data. The main steps in the SAW method include:

• Normalizing the decision matrix.
• Multiplying the normalized value by the weight of the criteria.
• Summing the values for each alternative.
• Determining the best alternative based on the highest value.

In the context of teacher performance evaluation, SAW can be used to assess various teacher performance criteria such as attendance, discipline, agenda completion, and participation. The final result is calculated based on the normalized weights and scores, following the process of normalizing and ranking the final assessment:

Calculations

Normalize X matrix into R Matrix based on the equation in SAW method, namely:

$$R_{ij}=\left\{\begin{array}{c}{\displaystyle \frac{x_{\mathrm{𝒾}j}}{{Max}_{\mathrm{𝒾}}{x}_{\mathrm{𝒾}\mathrm{𝒿}}}} If j is a benefit attribute\\ {\displaystyle \frac{{Min}_{\mathrm{𝒾}}{x}_{\mathrm{𝒾}j}}{X\mathrm{𝒾}j}} If j is a cost attribute\end{array}\right.$$

(1)

Description of each criterion:

$R_{ij}$: Normalized performance rating value.

$X_{ij}$: The value of the attribute that it belongs to.

$Ma{x}_{\mathrm{𝒾}}{x}_{\mathrm{𝒾}\mathrm{𝒿}}$: The largest value of each criterion.

$Mi{n}_{\mathrm{𝒾}}{x}_{\mathrm{𝒾}\mathrm{𝒿}}$: The smallest value of each criterion.

Benefit: If the largest value is the best.

Cost: If the smallest value is the best.

$$V_i=\sum _{j=1}^n{W}_j{R}_{ij}$$

(2)

2.3. System Implementation

The system implementation used uses a website platform, which in the process uses waterfall development, which includes Requirement Analysis, System Design, Implementation, Testing, Deployment, and Maintenance [12]. The system that was built proves that the method can provide consistent results in accordance with predetermined criteria during the process of assessing teacher performance.

3. Results and Discussion

The following are the steps in the discussion of the Simple Additive Weighting (SAW) method along with the implementation results in the form of systems, characteristics, and elements, as well as the role of information systems [13]:

3.1. Simple Additive Weighting (SAW) Method

In the initial stage of the Simple Additive Weighting (SAW) Method, several data assumptions were made, including teacher data as shown in Table 1, attendance data as shown in Table 2, work program data as shown in Table 3, and main activity data as shown in Table 4, which are used to evaluate teacher performance.

Table 1. Teacher data.

No.	Teacher Code	Teaching	Education	Position
1	GR001	11 AKL	Bachelor	Principal/Educator
2	GR002	10 PPLG	Bachelor	WAKASEK Curriculum/Educator
3	GR003	10 AKL	Bachelor	WAKASEK Kesiswaan/Educators
4	GR004	10 TO	Bachelor	WAKASEK HUMAS/Educator
5	GR005	11 ACP	Bachelor	WAKASEK Sarana/Tenaga Educator
6	GR006	11 PPLG	Master	Educators
7	GR007	10 AKL	Bachelor	Educators
8	GR008	11 TO	Master	Educators
9	GR009	12 ACP	Bachelor	Educators
10	GR010	11 AKL	Bachelor	Educators

Table 2. Attendance data.

No.	Teacher Code	Teaching	Log in	Exit
1	GR001	11 AKL	06.30	15.50
2	GR002	10 PPLG	06.42	15.43
3	GR003	10 AKL	06.47	15.45
4	GR004	10 TO	07.23	16.40
5	GR005	11 ACP	07.08	16.00
6	GR006	11 PPLG	07.30	15.40
7	GR007	10 AKL	07.43	15.43
8	GR008	11 TO	06.43	15.41
9	GR009	12 ACP	07.43	15.42
10	GR010	11 AKL	06.47	16.40

Table 3. Work program data.

No.	Activities	Description	Implementation Time
1	Introduction to Software Engineering and IT Project Flow	Introduction to Software Engineering, IT Project.	Week 1–2
2	Introduction to Java Programming	Java programming basics, basic data structures.	Week 4–5
3	Introduction to Web Frameworks (Spring, Django)	Basic concepts of frameworks, web application development.	Week 6–7
4	Database and Database Management (MySQL, SQLite)	Database design, normalization, database operations.	Week 8–9
5	Practical Exam and Evaluation	Practical exam for web and mobile application development.	Week 10–11
6	Application Development Training	Organize training to improve students’ skills in web-based and mobile application development.	Every Saturday (1 h)
7	Coding Competition	Organize an internal coding competition to improve students’ logic and technical skills.	Third month of each semester
8	Applied Programming Workshop	Invite industry practitioners to give workshops on the latest trends in programming.	Second month of each semester
9	Midterm Evaluation	Theoretical and practical exams covering material taught up to Week 7.	Week 13
10	End of Semester Evaluation	Theoretical and practical exams covering the entire semester.	Week 15
11	Project Assessment	Assessment of the final project results in the form of applications developed.	Week 14
12	Skills Portfolio	A collection of assignments and projects that have been carried out during.	Throughout the Semester
13	Training in the Use of Development Tools	Improve skills in using the latest programming software such as IDEs, frameworks, and design tools.	Every Month (2 h)
14	Information Technology Seminar	Participate in seminars or webinars related to the latest technology in the RPL field.	Every Semester
15	Self-Evaluation	Conduct personal reflection on teaching and student development.	Every End of Month

Table 4. Main activity data.

No.	Activities	Description	Implementation Time
1	Practical Exam and Evaluation	Practical exam for web and mobile application development.	Week 10–11
2	Application Development Training	Organize training to improve students’ skills in web-based and mobile application development.	Every Saturday (1 h)
3	Training in the Use of Development Tools	Improve skills in using the latest programming software such as IDEs, frameworks, and design tools.	Every Month (2 h)
4	Information Technology Seminar	Participate in seminars or webinars related to the latest technology in the RPL field.	Every Semester
5	Self-Evaluation	Conduct personal reflection on teaching and student development.	Every End of Month
6	Project Assessment	Assessment of the final project results in the form of applications developed.	Week 14
7	Skills Portfolio	A collection of assignments and projects that have been carried out during the semester.	Throughout the Semester

3.1.1. Determination of Assessment Criteria

Furthermore, after obtaining data as input, the next stage is determining the assessment criteria. The following assessment criteria are used in the SAW calculations, as shown in Table 5.

Table 5. Determination of Assessment Criteria.

No.	Assessment Criteria	Description
1	Attendance (C1)	Percentage of teacher attendance in teaching activities.
2	Discipline (C2)	Attendance to school and prompt entry to class.
3	Agenda Completion (C3)	Percentage of learning agendas that are successfully completed.
4	Participation in Professional Development (C4)	Number or score of training activities or seminars attended.

3.1.2. Criteria Weight

At this stage, the assessment criteria that have been determined will be given a value in the form of criteria weights, as shown in Table 6.

Table 6. Criteria weight.

No.	Aspects of the Cognitive Domain (ARK)	Criteria	Value	Interests	Weight	Weight Percentage
1	C1	Attendance	Above 95% in a month	Very good	3	35%
			Between 90 and 95% of the month	Good	2
			Below 90% in a month	Not good	1
2	C2	Discipline	Attendance on time, maintain order and discipline consistently	Very good	3	25%
			Attendance is on time, but there are few violations	Good	2
			Untimely attendance and frequent offenses	Not good	1
3	C3	Agenda Completion	Agenda implementation above 3 in a year	Very good	3	25%
			2–3 Agenda	Good	2
			Under 2 Agenda	Not good	1
4	C4	Participation	Above 3 certifications	Very good	3	15%
			2–3 certifications	Good	2
			Under 1 certification	Not good	1
Total						100%

3.1.3. Normalization and Ranking Process of Final Assessment

The last stage of SAW calculation lies in the normalization process to determine the final assessment. The calculations on normalization and ranking results in the final assessment process are as follows:

• GR001

GR001.1 =${\displaystyle \frac{3}{(2;2;3;1;1;2;3;1;1;1)}}={\displaystyle \frac{3}{3}}=$ 1.7

GR001.2 =${\displaystyle \frac{3}{(3;2;3;1;1;1;3;1;1;3)}}={\displaystyle \frac{3}{3}}$ = 1.9

GR001.3 =${\displaystyle \frac{3}{(2;1;2;2;2;3;1;3;3;2)}}={\displaystyle \frac{3}{3}}$ = 2.1

GR001.4 =${\displaystyle \frac{3}{(3;1;3:3;2;2;3;1;3;1)}}={\displaystyle \frac{3}{3}}=$ 2.22

• GR002

GR002.1 =${\displaystyle \frac{3}{(1;2;1;2;1;2;1;1;2;3)}}={\displaystyle \frac{3}{3}}=$ 1.6

GR002.2 =${\displaystyle \frac{3}{(2;3;3;2;3;2;1;2;1;1)}}={\displaystyle \frac{3}{3}}=$ 2.4

GR002.3 =${\displaystyle \frac{3}{(1;2;1;3;3;2;3;3;2;3)}}={\displaystyle \frac{3}{3}}=$ 2.3

GR002.4 =${\displaystyle \frac{3}{(2;3;1;3;2;3;1;3;3;3)}}={\displaystyle \frac{3}{3}}=$ 2.6

• GR003

GR003.1 = 2${\displaystyle \frac{3}{\left(2;3;3;2;3;1;1;1;2;3\right)}}={\displaystyle \frac{3}{3}}=2$

GR003.2 =${\displaystyle \frac{3}{(3;3;1;3;2;1;1;3;3;1)}}={\displaystyle \frac{3}{3}}=$ 2.5

GR003.3 =${\displaystyle \frac{3}{(1;1;3;;1;2;1;1;2;3;2)}}={\displaystyle \frac{3}{3}}=$ 1.72

GR003.4 =${\displaystyle \frac{3}{(2;2;3;3;1;3;3;2;3;2)}}={\displaystyle \frac{3}{3}}=$ 2.4

• GR004

GR004.1 =${\displaystyle \frac{3}{(3;3;2;1;2;3;1;3;3;1)}}={\displaystyle \frac{3}{3}}=$ 2.3

GR004.2 =${\displaystyle \frac{3}{(1;1;2;3;1;3;1;1;2;1)}}={\displaystyle \frac{3}{3}}=$ 1.6

GR004.3 =${\displaystyle \frac{3}{(2;3;1;2;2;1;3;1;2;1)}}={\displaystyle \frac{3}{3}}=$ 1.85

GR004.4 =${\displaystyle \frac{3}{(3;2;1;3;2;1;3;2;2;3)}}={\displaystyle \frac{3}{3}}=$ 2.25

• GR005

GR005.1 =${\displaystyle \frac{3}{(1;1;3;2;2;3;2;1;3;1)}}={\displaystyle \frac{3}{3}}=$ 1.92

GR005.2 =${\displaystyle \frac{3}{(2;2;1;3;2;1;2;1;3;1)}}={\displaystyle \frac{3}{3}}=$ 1.82

GR005.3 =${\displaystyle \frac{3}{(3;1;3;1;1;2;2;2;1;1)}}={\displaystyle \frac{3}{3}}=$ 1.7

GR005.4 =${\displaystyle \frac{3}{(3;2;1;1;2;2;1;2;3;1)}}={\displaystyle \frac{3}{3}}=$ 1.8

• GR006

GR006.1 =${\displaystyle \frac{3}{(2;2;3;1;2;1;2;1;3;1)}}={\displaystyle \frac{3}{3}}=$ 1.8

GR006.2 =${\displaystyle \frac{3}{(2;3;3;1;1;3;2;1;3;2)}}={\displaystyle \frac{3}{3}}=$ 2.1

GR006.3 =${\displaystyle \frac{3}{(2;3;1;3;1;2;3;1;2;1)}}={\displaystyle \frac{3}{3}}=$ 1.9

GR006.4 =${\displaystyle \frac{3}{(3;1;3;2;1;3;1;2;3;3)}}={\displaystyle \frac{3}{3}}=$ 2.2

• GR007

GR007.1 =${\displaystyle \frac{3}{(2;2;1;3;1;1;2;3;1;3)}}={\displaystyle \frac{3}{3}}=$ 1.91

GR007.2 =${\displaystyle \frac{3}{(3;1;2;1;2;1;2;1;2;3;)}}={\displaystyle \frac{3}{3}}=$ 1.83

GR007.3 =${\displaystyle \frac{3}{(3;2;3;1;3;3;2;3;2;3)}}={\displaystyle \frac{3}{3}}=$ 2.5

GR007.4 =${\displaystyle \frac{3}{(2;2;3;3;1;1;2;3;2;2)}}={\displaystyle \frac{3}{3}}=$ 2.7

• GR008

GR008.1 =${\displaystyle \frac{3}{(1;1;2;1;2;1;2;2;2;1)}}={\displaystyle \frac{3}{3}}=$ 1.5

GR008.2 =${\displaystyle \frac{3}{(3;2;2;2;2;3;1;1;3;3)}}={\displaystyle \frac{3}{3}}=$ 2.2

GR008.3 =${\displaystyle \frac{3}{(2;3;1;3;3;2;2;2;3;3)}}={\displaystyle \frac{3}{3}}=$ 2.4

GR008.4 =${\displaystyle \frac{3}{(1;1;3;2;1;2;3;1;2;3)}}={\displaystyle \frac{3}{3}}=$ 1.83

• GR009

GR009.1 =${\displaystyle \frac{3}{(2;2;3;3;1;2;3;1;2;3)}}={\displaystyle \frac{3}{3}}=$2.2

GR009.2 =${\displaystyle \frac{3}{(3;1;2;1;3;1;2;1;3;1)}}={\displaystyle \frac{3}{3}}=$ 1.8

GR009.3 =${\displaystyle \frac{3}{(2;2;2;1;3;1;3;1;2;3)}}={\displaystyle \frac{3}{3}}=$ 2

GR009.4 =${\displaystyle \frac{3}{(3;2;3;2;3;1;1;1;2;3)}}={\displaystyle \frac{3}{3}}=$ 2.1

• GR010

GR010.1 =${\displaystyle \frac{3}{(2;2;1;3;1;2;1;3;1;3)}}={\displaystyle \frac{3}{3}}=$ 1.9

GR010.2 =${\displaystyle \frac{3}{(2;3;1;3;1;2;3;1;3;1)}}={\displaystyle \frac{3}{3}}=$ 2

GR010.3 =${\displaystyle \frac{3}{(2;3;1;3;2;1;1;3;1;1)}}={\displaystyle \frac{3}{3}}=$ 1.8

GR010.4 =${\displaystyle \frac{3}{(2;3;2;2;1;3;1;2;2;1)}}={\displaystyle \frac{3}{3}}=$ 1.9

The results of the normalization calculations are presented in tabular form, specifically in the table showing the average value of the statement of criteria. The data for the average value of the criteria statements can be seen in Table 7, while the normalization process is outlined in Table 8.

Table 7. Average Value of Statement of Criteria.

No.	Code	Average Value of Criteria Statements (In Percent)
		C1	C2	C3	C4
1	GR001	1.7	2.3	2.1	2.22
2	GR002	1.6	2.5	2.3	2.6
3	GR003	2	1.7	1.72	2.4
4	GR004	2.3	1.75	1.85	2.25
5	GR005	1.92	1.9	1.7	1.8
6	GR006	1.8	1.72	1.9	2.2
7	GR007	1.91	2	2.5	2.7
8	GR008	1.5	1.8	2.4	1.83
9	GR009	2.2	2.6	2	2.1
10	GR010	1.9	2.7	1.8	1.9
		35%	25%	25%	15%

Table 8. Normalization Process.

No.	Code	Criteria
		C1	C2	C3	C4
1	GR001	0.73913	0.85185	0.84	0.822222
2	GR002	0.69565	0.92593	0.92	0.962963
3	GR003	0.86957	0.62963	0.688	0.888889
4	GR004	1	0.64815	0.74	0.833333
5	GR005	0.83478	0.7037	0.68	0.666667
6	GR006	0.78261	0.63704	0.76	0.814815
7	GR007	0.83043	0.74074	1	1
8	GR008	0.65217	0.66667	0.96	0.677778
9	GR009	0.95652	0.96296	0.8	0.777778
10	GR010	0.82609	1	0.72	0.703704

After the process is complete, the final stage is to examine the rankings in the teacher performance assessment data. The rankings can be seen in Table 9, while the detailed criteria ranking results are presented in Table 10.

Table 9. Ranking.

No.	Code	Criteria
		C1	C2	C3	C4	Total Value	Overall Ranking of Criteria
1	GR001	0.2587	0.21296	0.21	0.123333	0.804992	6
2	GR002	0.24348	0.23148	0.23	0.144444	0.849404	3
3	GR003	0.30435	0.15741	0.172	0.133333	0.767089	7
4	GR004	0.35	0.16204	0.185	0.125	0.822037	5
5	GR005	0.29217	0.17593	0.17	0.1	0.7381	9
6	GR006	0.27391	0.15926	0.19	0.122222	0.745395	8
7	GR007	0.29065	0.18519	0.25	0.15	0.875837	2
8	GR008	0.22826	0.16667	0.24	0.101667	0.736594	10
9	GR009	0.33478	0.24074	0.2	0.116667	0.89219	1
10	GR010	0.28913	0.25	0.18	0.105556	0.824686	4

Table 10. Criteria ranking results.

No.	Code	Ranking of Criteria
		C1	C2	C3	C4
1	GR001	8	4	4	5
2	GR002	9	3	3	2
3	GR003	3	10	9	3
4	GR004	1	8	7	4
5	GR005	4	6	10	10
6	GR006	7	9	6	6
7	GR007	5	5	1	1
8	GR008	10	7	2	9
9	GR009	2	2	5	7
10	GR010	6	1	8	8

3.2. System Implementation

After the SAW calculation, the next stage is the implementation of the calculation results into the development system. At the implementation stage of the Decision Support System for Assessing Teacher Performance Using the Simple Additive Weighting (SAW) Method at SMK XYZ, a web-based system has been successfully developed. The following are the results of the implementation in the form of systems, characteristics, and elements, as well as the role of information systems [13]:

3.2.1. Database Design

Database design at this stage of the research is the process of designing the structure and organization of the database to support the efficient storage, management, and retrieval of data. A good database design ensures that data can be accessed, manipulated, and managed in an optimal way, while minimizing redundancy (repetition of data) and maintaining data integrity. The database design created by the researcher is shown in Figure 2.

Figure 2. Database design.

3.2.2. Menu Display

This menu page is designed with a focus on user-friendliness and professionalism. This view of the application’s main dashboard is designed to monitor teacher performance in real-time. Users can view activity progress, evaluate performance indicators (attendance, discipline, agenda completion, and professional development), and use navigation to explore other features such as ratings, rankings, and reports. This application is very helpful to provide a complete picture and simplify teacher performance management at SMK XYZ. The following views of the application are shown in Figure 3.

Figure 3. (a) Login page, (b) Dashboard page, (c) Assessment dashboard page, (d) Attendance assessment page, (e) Rank page, and (f) Report page.

4. Discussion

Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.

5. Conclusions

The main conclusion of this research is that this ranking shows that the Decision Support System base method provides an objective ranking based on the weights and values of each criterion. With these results, GR009 can be considered as the best performing candidate in the context of this evaluation. GR009 managed to excel in all the criteria assessed (C1 to C3), thus obtaining the highest score and being ranked first. The difference in scores between first and second place was significant (Simple Additive Weighting (SAW) 0.10839704), demonstrating the consistent superiority of GR009 over the other participants.