Application of Additive Ratio Assessment (ARAS) Method for the Selection of Youth Red Cross Chairperson at SMA Negeri 1 Lebakwangi Kuningan

ABSTRACT


INTRODUCTION
Technology continues to develop along with human needs in carrying out daily life. Slowly but surely, technology has become essential in supporting various areas of the life of all levels of society, starting from the upper, middle, and lower layers of society. The use of computers as a tool is no longer in doubt, both as a medium for receiving, processing, and storing data. The development of information technology has made it ISSN 2963-7147 37 process to identify and provide estimates of overall system interactions. The reason for choosing ARAS is because ARAS is a form of a decision model suitable for multi-criteria and multi-substitution problems.
This research is essential to do because there are several benefits. The benefits of this research for educational institutions are expected to be a learning material and reference for friends who will conduct further research on topics related to the research title above. In addition, the results of this study are expected to be useful for SMA Negeri 1 Lebakwangi Kuningan, West Java, especially for the PMR board selection committee as input in making decisions on the selection of PMR board candidates.

METHOD
The research method that the author uses in this research is using descriptive analysis method. The descriptive analysis method is a research method that seeks to describe, describe, or explain the subject or object (institution, society, and so on) in research based on data obtained naturally or as it is from the subject or object under study [20].
The steps of the descriptive method generally consist of identifying the problems to be solved in the research, limiting the problems so as not to deviate from the discussion in the research, determining the objectives and benefits of the research, and conducting a literature study that is relevant to the problems in the research, making a framework for thinking in research, designing methods applied in research, collecting and analyzing data using relevant techniques in research, and making reports on research that has been carried out. This study's descriptive analysis method aims to accurately describe, describe, or explain the selection of prospective PMR administrators at SMA Negeri 1 Lebakwangi, Kuningan Regency, West Java.
In order to collect the data needed in this study using the techniques of Observation, Interview, and Literature Study. Observation is the basis of all science, and scientists can only work based on data, namely facts about the world of reality obtained through observation [21]. In observational studies, the status of the phenomenon is determined by not asking questions but by observing. In this case, the author made direct observations at SMA Negeri 1 Lebakwangi, Kuningan Regency, to obtain information for research materials.
The second technique is the interview. An interview is a meeting between two people to exchange information and ideas through question and answer so that meaning can be constructed in a particular topic [21]. In this case, the researcher interviewed the Deputy Head of Student Affairs of SMA Negeri 1 Lebakwangi to obtain the data needed in the study.
While the third technique is literature study, the literature study is collecting several books and magazines relating to the problem and research objectives. The book is considered a source of data to be processed by historians, literature, and language experts [22]. The author uses several references to support the theory concerned with the object of this research, in the form of books, journals, and sources from the internet.

RESULTS AND DISCUSSION
Additive Ratio Assessment (ARAS) is a method used for ranking criteria, and conceptually the ARAS method is used with other methods that use the concept of ranking, where the ranking process must be processed using the ARAS method. The steps in the ranking process using the ARAS method are as follows [23]: a. Formation of Decision-Making Matrix Where : m = Number of alternatives n = number of criteria = Performance value of alternative against criterion = Optimum value of criterion If the optimal value of the criterion ( ) is not known, then: b. Normalization of the decision matrix for all criteria If the Benefit (max) criteria are then normalized as follows: Where * is the normalized value. If the criteria are Non-benefits, then Normalization is carried out following: Where : R = Matrix Normalization c. Determine the weight of the matrix that has been normalized [ ] (7) Where: D = matrix weight Rij = Normalized value Wj = criterion weight d. Determine the value of the optimization function (Si) Where is the value of the alternative optimality function i. The most significant value is the best value, and the most negligible value is the worst. By considering the process of proportional relationship with the value and weight of the criteria known to affect the final result.

Normalization
Normalization is a technique by taking a bottom-up approach that is used to help identify relationships. Meanwhile, according to Connolly and Begg, Normalization is a technique that produces a collection of relations with the desired property by providing a data requirement for the company [24]. The objectives of Normalization are as follows: a. To get rid of duplicate data b. To reduce complexity c. To make it easier to modify data In carrying out Normalization, several processes are needed, including the following: a. The data is described in tabular form, then analyzed based on specific requirements to several levels. b. If the table being tested does not meet specific requirements, then the table needs to be broken down into several more straightforward tables to meet the optimal data.
The objectives of Normalization are as follows: a. To remove duplicate data. b. To reduce complexity. c. To facilitate data modification. Normalization has the following forms, namely: a. The form is not normal (unformalized form) This form is a form of recorded data, and there is no need to follow a particular format; the data may be incomplete or duplicated. b. First normal form (1NF or first normal form) The first normal form has the characteristic that each data is formed in a flat file (base file), and the data is formed in one record after another. There are no repeating or multiple-valued attribute sets. c. Second normal form (2NF or second normal form) The second normal form has the condition that the data form has met the criteria for the first normal form; the non-key attribute must be functionally dependent on the primary key or primary key, so for the second normal form, the field keys must have been determined. The field key must be unique and can represent other attributes that are members of it.
ISSN 2963-7147 40 d. Third normal form (3NF or three normal forms) To be in the third normal form, the relation must be in the second normal form, and the non-primary attributes must not have a transition relationship; in other words, each unlocking attribute must depend on the primary key as a whole.

Normalization Test
Normalization is the process of grouping data elements into tables that show entities and their relationships.

Abnormal Shape
Write down all the data to be recorded; the double part does not need to be written. It is shown in Table 1 below:

Second Normal Form (2NF)
The 2NF requirement is that there is no partial "functional dependency" on the primary key in a table. The point is that at this 2NF normalization stage, the table must be broken down based on the primary key. So that the 2NF normalization form of these tables is as shown in Table 5 below:

1.
To compare each candidate's data in the alternative table, a table of criteria such as discipline, responsibility, organizational activity, achievements, and skills is needed. The criteria weight the selection committee will determine data for prospective management with the sum of all criteria weights equal to 100 or 1. The criteria weight table is as follows:  4. From the data criteria that have been determined, the next step is to determine the suitability rating, as shown in the table below: In Table 7, Table 8, Table 9, and Table 10, it can be explained that the initial data include Criteria, Weights, and Alternatives (Prospective Management). Alternatives are obtained from the candidate's name for the board of directors. In contrast, the criteria are obtained from the value data that the prospective administrator has carried out. The weight data is obtained from the criteria data whose weight value has been determined by the authors and administrators. Max Min value is obtained from Benefit and Cost, where if Benefit, the highest value will be taken, and if Cost, the lowest value will be taken from each alternative value (prospective management).

Define the decision matrix
[ ] ISSN 2963-7147 44 6. Normalize the decision matrix for all criteria: rit ri S r T t S r rit ri R su ts its so that the normalization matrix is obtained, which can be seen in the following table

Weighted Normalization
The following process is to perform weighted Normalization of all criteria. This can be done by multiplying the decision matrix normalized to the weight of the criteria.
So that the weighted normalization data is obtained as follows   Based on the final result of the calculation using the ARAS method, the final calculation result is in the form of ranking. From the ranking results, first place is the alternative with candidate code 1920004.
Based on the results above, using the ARAS method is very helpful in determining the selection of the chairman of the Youth Red Cross by involving many profile variables from candidates. With this technique, the number of variables that will be used will not be a problem recruiting very potent leaders in various aspects.

CONCLUSION
Based on the research that has been carried out by the author, which is described in this final project regarding the applications that have been done, it can be concluded that a decision support system for the selection of candidates for PMR management assists the election committee in assessing and selecting candidates for PMR management according to the criteria. In addition, the design of a decision support system for selecting PMR management at SMA Negeri 1 Lebakwangi is implemented using a web-based programming language, for the predetermined assessment criteria and sub-criteria can be stored and systemized, thus enabling relatively faster processing. By using this decision support system application that has been designed, the assessment and selection of candidates for PMR management become more accurate and more manageable because it uses computerized media so that the selection of candidates for management can be more neat and systematic.