Decision Support System for Performance Assessment of Honorary Personnel Applying TOPSIS, SMART, and MAUT Methods with a Combination of ROC Weighting

This research explores the assessment of honorary personnel performance through the application of the TOPSIS, SMART, and MAUT methods, along with ROC weighting. In today's competitive landscape, effective decision-making is vital for organizational success, and the role of Decision Support Systems (DSS) is emphasized. Unfair assessments can impact motivation and productivity, necessitating comprehensive criteria such as Work


INTRODUCTION
In today's dynamic and competitive era, decision making is at the core of an organization's success.For government institutions or private institutions, the performance of honorary personnel plays an important role in supporting smooth operations and achieving goals.Meanwhile, the Decision Support System (DSS) has become a critical element in assisting in appropriate and efficient decision making (Syafiatun Ihsani Luthfiyah & Candra Noor Santi, 2022).
Assessing the performance of honorary personnels is a vital step in planning human resource development, training, and effective utilization in line with their tasks and functions.However, unfair assessments can have negative impacts on the motivation and productivity of honorary personnels.In this context, it is important to ensure that performance evaluations encompass a number of relevant criteria, such as Work Discipline, Cooperation, Commitment, Service Orientation, Education, and Etiquette.
The increasing complexity of business complexity and demands for operational efficiency place the need to have a system that can provide a holistic view of the performance of honorary personnel.In this context, the application of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), SMART (Simple Multi-Attribute Rating Technique), and MAUT (Multi-Attribute Utility Theory) methods emerged as strategic solutions to compile and evaluate relevant criteria in making decisions on the assessment of honorary personnel.
These methods have proven effective in addressing performance evaluation issues by considering various aspects or criteria simultaneously.Additionally, the use of a combination of Rank Order Centroid (ROC) weighting can provide flexibility in adapting to decision-maker preferences.
Considering the complexity and importance of assessing honorary personnel performance, this research aims to provide an overview of a Decision Support System that applies the TOPSIS, SMART, and MAUT methods with ROC weighting combination.Through this research, it is hoped that an approach can be identified to assist organizations in assessing honorary personnel performance more effectively and efficiently, while reducing the level of subjectivity that may arise in the evaluation process.

LITERATURE REVIEW
In this theoretical review, the focus is on explaining Decision Support Systems (DSS) and their significance in addressing problems using data and models.Furthermore, the review examines various multi-criteria decisionmaking methods such as TOPSIS, SMART, MAUT, and ROC weighting, elucidating their fundamental principles and their roles in enhancing decisionmaking processes.

Decision Support System (DSS)
According to (Aldo et al., 2019), Decision Support System (DSS) is a computer-based system that can assist in decision-making to solve specific problems by utilizing certain data and models.
According to (Seran et al., 2020), Decision Support System (DSS) is a computer-based system that is part of an information system, including knowledge-based or knowledge management systems, used to support decisionmaking in an organization or company.
Based on the explanation above, it can be concluded that a Decision Support System (DSS) is a computer-based system that can assist in decision-making to solve specific problems by utilizing certain data and models.

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
According to (Trise Putra et al., 2020), TOPSIS is a multi-criteria decisionmaking method based on the alternative that is closest to the positive ideal solution and farthest from the negative ideal solution.However, the alternative that has the smallest distance from the positive ideal solution does not necessarily have the largest distance from the negative ideal solution.
According to (Hertyana et al., 2020), TOPSIS is one of the multi-criteria decision-making methods that operates on the principle that the selected alternative should have the closest distance to the positive ideal solution and the farthest distance from the negative ideal solution.
Based on the explanation above, it can be concluded that TOPSIS is a multicriteria decision-making method.It is based on the criterion that the chosen alternative should exhibit the closest proximity to the positive ideal solution while maintaining the farthest distance from the negative ideal solution SMART (Simple Multi-Attribute Rating Technique) According to (Sibyan, 2020), SMART is a decision-making method that addresses multi-criteria issues based on the values associated with each alternative for each criterion, which has been assigned a weight.
According to (Kurniadi & Prehanto, 2021), SMART method essentially is a decision-making approach that involves normalizing the weights of criteria, resulting in an evaluation score.This numerical evaluation facilitates decisionmakers in the decision-making process.
Based on the explanation above, it can be concluded that the SMART method is a decision-making approach that involves normalizing the weights of criteria, resulting in an evaluation score, which facilitates decision-makers in the decision-making process.

MAUT (Multi-Attribute Utility Theory)
According to (Sari & Hayati, 2019), MAUT is a method in which the weighted sum of values is sought for the same utilities in each attribute.This method can also process data from all attributes with different utilities.
According to (Murti et al., 2023), MAUT is a quantitative comparison method that typically combines measurements of different cost, risk, and benefit considerations.Each criterion involved has several alternatives capable of providing solutions.
Based on the explanation above, it can be concluded that MAUT is a method that seeks the weighted sum of values for the same utilities in each attribute.Additionally, MAUT is a quantitative comparison method that integrates measurements of various cost, risk, and benefit considerations.Each criterion in this method involves multiple alternatives capable of providing solutions.

RESULTS
In this study, researchers used the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), SMART (Simple Multi-Attribute Rating Technique) and MAUT (Multi-Attribute Utility Theory) methods in the process of assessing the performance of honorary personnel.The TOPSIS, SMART and MAUT methods require preference criteria and weights to determine the best alternative.

Determination of Criteria, Weights and Alternatives
Producing the best alternative requires how many attributes or criteria are used as internal requirements problem solving, as well as the weight of each conflicting criteria.
Table 1.Criteria From the results of the above table, there are 6 criteria that can be explained as follows: 1. Work Discipline Work discipline is an attitude of mutual respect, obedience, compliance, and appreciation for both written and unwritten rules, as well as the ability to adhere to them.

Teamwork
Teamwork is an activity carried out within an organization involving several individuals with the aim of achieving a specific goal.

Commitment
Commitment is a form of dedication or obligation that binds individuals in relation to specific matters or actions.

Service Orientation
Service orientation is the willingness or desire to serve or assist others in meeting their needs.

Education
Education is the academic level someone attains through learning in schools or higher education institutions.

Politeness
Politeness is the demeanor of an individual related to ethics, speech, and friendly behavior displayed in front of others with the intention of respecting them to foster harmony in social interactions.
Furthermore, the data for each alternative obtained can be found in Table 2.

Quite Good
In Table 2 above, a significant portion of the data is linguistic in nature, such as Very Good, Good, and Quite Good.This data needs to be weighted so that values for alternatives can be calculated using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), SMART (Simple Multi-Attribute Rating Technique) and MAUT (Multi-Attribute Utility Theory) methods applying ROC weighting.The weighting can be seen in the following Table 3: After the weighting of criteria values is completed, the next step is to create a compatibility rating, which can be observed in the following Table 4.After the compatibility ratings are determined in the above Table 4, the next step involves calculations using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), SMART (Simple Multi-Attribute Rating Technique) and MAUT (Multi-Attribute Utility Theory) method applying ROC weighting.
The following are the weights obtained using the Rank Order Centroid (ROC) method as shown below:

Create a Y-Weighted Normalized Matrix
Calculate the value for the weighted normalized matrix Y by multiplying the weighted value obtained from calculations using the ROC method with the R matrix

Determine the Positive Ideal Solution Matrix (A+) and the Negative Solution Matrix (A-)
Selection of the positive ideal solution (A+) by selecting the maximum value for each criterion and for the negative ideal value (A-) by selecting the minimum value for each criterion based on the Yweighted normalization value.
weighting, the next step involves calculations using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), SMART (Simple Multi-Attribute Rating Technique) and MAUT (Multi-Attribute Utility Theory) method.

Table 3 .
Weight of Criteria Values

Table 4 .
Compatibility Rating for Each Criteria

Table 5 .
Determine the Positive Ideal Solution matrix (A+) and the Negative Solution Matrix (A-)

Table 6 .
Distance between the weighted values of each alternativeFrom the calculation of the preference values above, the results are: Based on calculations using the TOPSIS method, first place is Cinta with a score of 0.93, second place is Loli with a score of 0.27, third place is Maya with a score of 0.18, fourth place is Sinta with a score of 0.06, and for sixth place there are two candidates.namely Gisel and Karin with a score of 0.3.SMART

Table 8 .
Criterion Score for each Alternative