Decision Support System Analysis of School Promotion Media Selection using MABAC, OCRA And CODAS Methods

Comparison


LITERATURE REVIEW
School promotion is a crucial aspect in developing the image and increasing the attractiveness of educational institutions.In this context, the selection of promotional media plays an important role in ensuring an effective message is delivered to the target audience.In several previous studies, the MOORA (Multi-Objective Optimization by Ratio Analysis) method has been used to assist decision-making related to the selection of promotional media.However, recent research has introduced alternative approaches, namely the MABAC (Multi-Attributive Border Approximation Area Comparison), OCRA (Organizational Criteria Analysis), and CODAS (Complex Proportional Assessment) methods.A number of previous studies have integrated the MOORA method to select the promotional media that best suits the objectives and characteristics of a particular school.MOORA allows decision-makers to evaluate and compare promotional media alternatives based on a number of predefined criteria.Some of the frequently applied criteria include cost, reach, and message effectiveness.
In further development, recent studies have begun to explore the potential of alternative methods such as MABAC, OCRA, and CODAS.The MABAC method offers an innovative approach by using modeling techniques to determine the relative boundaries between alternatives.Meanwhile, OCRA highlights the role of organizational criteria in promotional media selection and presents a more holistic approach.CODAS, with its complex approach, contributes to an in-depth understanding of the impact of promotional media on school image.It is important to compare the strengths and weaknesses of each method in the context of school promotional media selection.Comparative analysis can help in understanding situations where one method is superior to another.In addition, integration of methods can also be an attractive alternative, combining the advantages of each approach.

METHODOLOGY Decision Support System
Michael Scott Morton was the first person to create the idea of a Decision Support System (DSS), previously known as a Management Decision Support System in the early 1970s.This system is a computer-based interactive system that aims to assist in decision making by solving unstructured problems using certain models and data.The processing of data and information carried out during the decision-making process aims to produce various options that can be selected.SPK, an information system implementation, is intended only as a decision-making tool (Primaniyar 2020).Furthermore, during the 2000s, decision support systems began to use web, mobile, and cloud technologies.DSS were also increasingly used in various fields.Major advances in information technology and artificial intelligence during the current big data era further changed the current model of decision support systems.With machine learning and highly advanced data analytics, the latest generation of DSS enables the processing of very large and complex data.

Multi-Attributive Approximation Area Comparison Method (MABAC)
MABAC was developed in 2015 by Pamucar and Cirovic and is well known for providing decision-making solutions.In the MABAC method, the distance between the Border Approximation Area (BAA) and alternatives can determine the best alternative (Handayani, Muhsidi, and Khomalia 2021).As explained in Indic D. & Lukovic's journal, this method was chosen because with other multicriteria decision-making methods such as SAW, COPRAS, MOORA, TOPSIS, and VI-KOR, the MABAC method produces a (consistent) solution ranking and is considered a reliable method for rational decision making.The MABAC method is used for ranking alternatives in this paper.The definition of the criterion function distance of each observed alternative from the border approximation area shows the basic assumptions of the MABAC method.The procedure for applying the MABAC (Multi-Attributive Border Approximation Area Comparison) method, which is a mathematical formulation, is presented in the following section (Ndruru et al., 2020).MABAC is gradually being accepted and used in various industries such as manufacturing, transportation, engineering, and management.Some researchers have even created variants of the existing MABAC method.MABAC continues to evolve and is now one of the alternative choices for multi-criteria decision-making for various purposes.There are many academic studies that continue to investigate it.In performing calculations with the MABAC method, the following steps can be followed (KALEM and AKPINAR 2022), (Baydaş 2022 The value of the normalized matrix (N) is determined using the formula: c. Calculate the weighted matrix where the formula can be seen as follows: ).

𝑚
After calculating the gi value for each criterion, the border approach of the area matrix G is formed with an n x 1 format (n is the number of criteria on which the selection of alternatives is based).
e. Calculation of alternative distances from the border approximation region for matrix element (Q).

Ranking alternatives
The calculation of the criterion function value for alternatives is obtained from the sum of the alternative distances from the border approximation area (Q).The greater the value of Si, the better the alternative.

Operational Competitiveness Rating Analysis (OCRA) Method
The Operational Competitiveness Rating Analysis (OCRA) method is a relative performance measurement approach based on a nonparametric model.This method was first developed by Parkan in 1994 and is a very useful and simple method to analyze various sectors and compare different decision units.Moreover, the ability to compare and monitor the performance of decision units over time is another important feature of this method.Operational Competitiveness Rating Analysis (OCRA) is a non-parametric efficiency measurement technique and was first proposed to solve the problem of performance measurement and productivity analysis (Nasyuha et al. 2022).This method was developed as a framework to evaluate and improve the operational competitiveness of companies by considering a number of relevant performance perspectives.The Performance Prism framework, also introduced by Professor Neely, is the basis of OCRA.The OCRA method includes the evaluation of critical operational performance based on five perspectives: strategy, procedures, capabilities, stakeholders, and contribution.The OCRA method has been gradually used in corporate management practice since its launch to evaluate operational competitiveness.The method has proven to provide a complete basis.OCRA and its developments continue to be used, especially for operational management studies and corporate performance measurement.The Combinative Distance-Based Assessment Method (CODAS) is an assessment method that combines various assessment methods: distance-based assessment, content-based assessment, and learning-based assessment.In 2010, Dr. Andreas C. Schmidt from the University of Tübingen, Germany, developed this method.The following is a summary of the steps used in the operational competitiveness assessment (OCRA) method: 1.In the first step, form decision matrix Xij 2. In the second step, the preference ranking with respect to the nonbeneficial criteria (cost criteria) is determined.Here, the working values of the alternatives for the criteria to be minimized are calculated only from the beneficial criteria are not considered.
3. In the third step, the linear preference ranking of each alternative for unfavorable criteria is calculated by the formula below.
4. In the fourth step, the preference ranking with respect to the benefit criteria is determined.For beneficial criteria, the alternative that has a higher value is preferred.The total performance rating of alternative i for all the beneficial criteria is calculated by the formula below.
5. In the fifth step, the linear preference ranking is calculated for the useful criteria using the formula.
̿  =  ̿  − min( ̿  ) 6.In the sixth step, the total preference value for each alternative is calculated using the formula below.

Combinative Distance-Based Assessment (CODAS)
Combinative Distance-Based Assessment (CODAS) is one of the methods used to solve decision-making problems that have multiple criteria (Kesharvarz et al. 2016).At Vilnius University of Technology, Lithuania, the CODAS method was first used to solve the problem of selecting students for scholarships.It is considered to be more efficient, consistent with other methods, and has high stability of results.In this method, alternatives are selected through two gauges.The primary measure relates to the alternative's Euclidean distance from the negative-ideal.Using this type of distance requires a standard neglect space I2 for the criteria.The second measure is the Taxicab distance which corresponds to the standard neglect space I1 .The alternative that has a greater distance from the negative-ideal solution is the preferred alternative.In this method, if there are two or more alternatives that have the same Euclidean distance, the Taxicab distance is used as the second measure.Although the standard I2 ignoring space is preferred in CODAS, both ignoring spaces can be taken into account in the process.In conducting the ranking process, the CODAS method has seven stages, which are as follows : a. Formation of Decision Matrix (X), can be calculated by Equation 1. e. Calculate the Euclidean distance (Ei) and Taxicab distance (Ti) of alternatives from the negative-ideal solution, using Equation 6 and Equation 7.
f. Create a Relative Assessment (Ra) matrix and its matrix components (hik), using Equation 8 and Equation 9.
Where  ∈ {1,2, ⋯ , }, and  (read: miu) is a threshold function to recognize the Euclidean distances of two alternatives, and is defined by Equation 10.
Where  (read: tau) is a threshold parameter that can be determined by the decision maker.It is recommended to specify this parameter with a value between 0.01 and 0.05.If the difference between the Euclidean distances of two alternatives is less than , these two alternatives are also compared using Taxicab distance.
g. Calculating the assessment results of each alternative (Hi), can be calculated with Equation 11.
Ranking alternatives based on the results of alternative assessment i ( ).
The alternative with the highest assessment result ( ) is the best choice among the existing alternatives.

RESULTS AND DUSCISSIONS
The decision support system built in this research is implemented using MABAC, OCRA, CODAS methods as calculation methods for determining priority ranking.Criteria, alternatives and criteria weight values are obtained from interviews with the school promotion team as a decission maker.The weight value of criteria and its types and the scale of alternative value assessment can be seen in Tables 1 and 2. Furthermore, it will carry out the ranking process with the MABAC, OCRA, CODAS methods.In this study, the system was tested using input data as shown in Table 3.In Table 3, alternatives are coded with the provisions of numbers 1 representing Brochures, 2 representing Posters, 3 representing Billboards, 4 representing Banners, and 5 representing Newspaper advertisements.

CONCLUSIONS AND RECOMMENDATIONS
(MABAC) The MABAC method can be concluded and implemented in finding the best alternative ranking of the criteria for selecting promotional media for vocational schools, so that the media Brochures, Posters, Billboards, Newspaper Advertising Banners, so as to determine promotional media recommendations with the highest final results obtained, namely in alternative A1 which uses brochure media with a value of 1,64605.So that the alternative that has the highest priority ranking is A1.
(OCRA) Based on the results of the research that has been done, it can be concluded that the Operational Competitiveness Rating Analysis (OCRA) method has been successfully implemented in the decision support system for selecting school promotion media at this SMK.From the results of system calculations according to the weight of criteria and input alternatives obtained from the school promotion team, it is found that brochure media is the alternative that has the highest priority ranking.From the results of testing the accuracy of system calculations so as to obtain an optimization value of -0.175 on alternative A1 as an alternative that is entitled as the first rank.
(CODAS) In the selection of school promotional media, the use of the CODAS (Complex Proportional Assessment) method can provide significant support in decision-making.This method allows for a comprehensive evaluation by considering a wide range of relevant criteria.The results of the analysis using the CODAS method enable the identification of the most effective promotional media based on various aspects, such as target audience, budget, reach and impact.The use of this method helps prioritize based on the relative importance and weight of each criterion.From the results of testing the accuracy of system calculations to obtain an optimization value of 0.631792065 on alternative A3 as an alternative that is entitled as the first rank.

FURTHER STUDY
Conduct further research to analyze the performance comparison of the MABAC, OCRA, and CODAS methods in the context of selecting promotional media.Comparative analysis can provide a more in-depth picture of the strengths and weaknesses of each method.Recommend the development of an integrative model that combines the best elements of the three methods.This model can provide a more comprehensive and adaptive approach to school promotion media selection.Evaluate the social and economic impacts of implementing the proposed method.This research can provide insight into the concrete benefits provided by the use of decision support systems in school promotion media selection.

ACKNOWLEDGMENT
The first thank you to the publisher of Formosa International Journal of Integrative Sciences (IJIS) for publishing scientific papers as a result of the collaboration of all authors, the second thank you to all researchers who have become references for the literature review in this article, the third thank you to the author's campus, Pamulang University.The last thank you to all readers of this article who have provided suggestions for the benefit of further research.Zega, M. and Syahrizal, M. ( 2022

Figure
Figure 1.Conceptual Framework of criteria n : Number of alternatives xij : performance value of alternative i against criterion j b.Normalization of the Decision Matrix for all criteria.Linear normalization is used for performance values with Equation 2. performance value of alternative i against criterion j  : Benefit type criteria  : Cost type criteria c.Calculating the normalized and weighted decision-making matrix.The normalized and weighted performance value of alternative i against criterion j (rij) can be calculated using Equation3.  =   .  the ideal-negative solution point of each criterion (nsj) using Equation4and Equation5.= [  ] 1  = min a normalized matrix based on the weight of each sub-

Table 1 .
Weight Value and Criteria Type

Table 10
Table 11 is a negative ideal value taken based on the lowest value of the normalized and weighted matrix value.

Table 13 .
Matriks Relative Assessment (RA) Summing the assessment matrix values that have been obtained previously in one row.Table14is the assessment score value which is the total of the assessment matrix values.

Table 15 .
The eighth stage performs ranking of the assessment score (H) which is then sorted based on the highest to lowest value.The table is the assessment score value that has been sorted based on its value.Ranking So alternative 3 is the best alternative for school promotional media.