DECISION SUPPORT SYSTEM FOR DETERMINING DEPARTMENT USING THE PROFILE MATCHING INTERPOLATION METHOD AT WIKRAMA VOCATIONAL SCHOOL, BOGOR

− Wikrama Vocational High School is one of the schools that routinely carries out the determination of majors every year. The majors process at Wikrama is carried out in the tenth grade by the Guidance and Counseling Teacher (BK Teacher) and the Head of Expertise Competence (Kakomli). BK and Kakomli teachers have difficulty determining the results of majors when there are more interest in one major than other majors, there is a mismatch of majors results because they are not in accordance with the existing majors in the chosen field of expertise and the process of majors is not accurate and fast. This is because it has not used an objective mechanism for determining majors, there is no weighting process, and there is no information system available. Therefore, it is necessary to develop a decision support system (DSS) to assist the process of determining majors using Profile Matching and Interpolation methods. The Profile Matching method is used for appraising decisions, while the Interpolation method is used for the weighting process. The criteria used in each field of expertise are Informatics Engineering with 11 criteria and Computers, Business Management with 8 criteria, and Tourism with 7 criteria. Based on the results of testing and validation that have been carried out by experts, it has an accuracy value of 93%. The accuracy value indicates that the system can provide recommendations for determining the right major. In addition, the interpolation weighting method is proven to increase the accuracy value compared to the ordinal weighting value in Profile Matching. The results of this study are in the form of a decision support system that helps in determining majors objectively, quickly and accurately .

INTRODUCTION Determination of majors in high school is usually determined by academic ability which is supported by interest factors so that students can study a science that suits their personality characteristics. The interest factor in SMK aims to provide opportunities for students to develop attitudes, knowledge competencies, and skill competencies according to their interests, talents and/or abilities in a major. Selection of specialization groups is based on report cards and/or recommendations from BK teachers and/or placement test results at SMK [1]. Majors  Where each student has chosen the desired area of expertise for further selection in determining the majors in that area of expertise so that when the results have been determined they must be in accordance with the existing majors in the chosen area of expertise.
During the majors process, Counseling Teachers and Kakomli experienced difficulties in determining the results of majors when there were more enthusiasts for one major than other majors. Some students ask to change majors with reasons of incompatibility because the results of the majors are not in accordance with the existing majors in the chosen area of expertise. Then the BK teacher and Kakomli still have difficulties in an accurate and fast assessment process. This is because there is no weighting process for each criterion and the determination of the final grade calculation still uses the average so that it cannot be known which criteria have a more important effect for a particular major.
There have been many implementations of decision support systems in various fields, including in terms of determining majors including [2], [3], [4], [5], [6], [7], [8]. There are also quite a lot of methods used to determine recommendations for decision support system solutions. These include the Multifactor Evaluation Process, Simple Additive Weighting, Analythical Hierarchy Process, Techique for Order Preference by Similarty to Ideal Solution, Vise Kriterijuska Optimizacija I Kompromisno Resenje, Fuzzy C-mean Algorithm, Profile Matching. Perdana et al (2021) explained that in order to obtain calculations with faster and more objective results [9], of course, it is necessary to carry out a weighting process and make an appraiser's decision based on proximity to the criteria. This is in line with the case study in determining this major. The suitable method in determining majors based on compatibility between student profiles and majors is the profile matching method. Then to get more objective and precise results, it is necessary to use interpolation in the weighting process. [10] explained that the application of interpolation weighting succeeded in increasing the accuracy value compared to the ordinal weighting method.
Based on the background above, this research focuses on the interpolation profile matching method in determining the direction that suits the needs of SMK Wikrama Bogor.

II. RESEARCH METHODOLOGY A. Research methods
The research to be carried out is a type of quantitative research, which is to take a sample of student data along with existing indicators, then the data is processed using the Profile Matching Interpolation method, the end result is to recommend student majors.

B. Population and Sample Selection Methods
At this stage the researcher chose the population and sample, where the population was students of class x (ten) at Wikrama Bogor Vocational School and the sample was majoring data for the 2020-2021 school year with a total of 642 students. C. Method of collecting data Data collection uses research instruments, analysis and is quantitative or statistical with the aim of testing the hypotheses that have been applied.
Data collection methods that will be used in this study are: 1. Interview The resource persons in this study were Counseling Guidance Teachers/Counseling Teachers at SMK Wikrama Bogor, namely Ms. Novya Azhari to gather information about the process of majoring and what are the criteria for making decisions in recommending majors.

Internal Data
The internal data used for this research is class x (ten) student data. The description of the data on the realization of majors at SMK Wikrama Bogor can be seen in Table 1.

D. Analysis Techniques
The analysis technique used in this study uses a Decision Support System (DSS) approach. The process of analysis was carried out on the results of the stages of data collection by interviews, observation and literature study. In the analysis process, the techniques used are: 1. Analysis of data from a running system. This is done on documents, procedures, databases, and results of reports from the running system. 2. Analysis of the needs of system users, modeling of these needs and what functions are obtained by system users.

E. Research Steps
In order for this research to be carried out properly, a structured research method is needed. The research steps carried out are presented in Figure 1.
The steps contained in Figure 3.1 include:

Data Setup
This stage is the initial data processing to obtain the parameters used in determining the suitability of majors with student profiles. The data is in the form of student data, value data, majors data. The preparation of this data is done by interviewing and verifying the counseling teachers.

Determination of Majors
At this stage, consultations were carried out with the guidance counselor to determine the majors at SMK Wikrama Bogor. It is known that at SMK Wikrama Bogor there are 7 majors which are divided into 3 areas of expertise, namely: Informatics and Computer Engineering (Software Engineering, Multimedia and Computer and Network Engineering), Business Management (Office Automation and Governance) and Tourism ( Hospitality and Catering).

Determination of Criteria
At this stage it was carried out based on student data in consultation with the BK teacher and the Head of the Department, several parameters were obtained that influenced the determination of majors. The outputs from this stage are 11 (eleven) criteria for majors in the field of ICT expertise, 8 (eight) criteria for majors in the field of Business Management and 7 (seven) criteria for majors in the field of Tourism.

scoring
This stage is the determination of suitability matching between student profiles and majors. The method used to perform suitability matching is the Profile Matching method which produces a prototype of the DSS model for determining majors.

Model Testing
The final stage of this research is testing the resulting model (prototype). A series of tests were carried out to determine the quality of the resulting model by looking at the accuracy value. Verification of the outcome of the model (prototype) was carried out by the Counseling Teacher and Head of Department who have handled the process of determining majors at SMK Wikrama Bogor for more than 2 (two) years. At the data preparation stage, data collection was carried out. The purpose of this stage is to obtain the parameters used for the development of the SPK model for determining majors. The preparation of this data is done by interviewing and verifying the counseling teachers.

E.2. Determination of Majors
At this stage it was carried out based on student data in consultation with the BK teacher and the Head of the Department, several parameters were obtained that influenced the determination of majors. The outputs from this stage are 11 (eleven) criteria for majors in the field of ICT expertise, 8 (eight) criteria for majors in the field of Business Management and 7 (seven) criteria for majors in the field of Tourism. The rules that exist at SMK Wikrama Bogor include: 1. The Department of Computer and Network Engineering only accepts men and the Department of Automation and Office Management only accepts women, other than that it can be either a man or a woman.

The Department of Computer and Network
Engineering does not accept students who are color blind 3. Provisions for majors cannot cross fields of expertise, namely when students are interested in the ICT field, the majors that must be matched are the majors in that area of expertise.

E.3. Determination of Criteria
Determination of major criteria was obtained from the results of consultations with experts at SMK Wikrama Bogor involving Counseling Guidance Teachers (BK Teachers) and Heads of Expertise Competence (Kakomli). The results of consultations with experts obtained data from 642 students who would be tested. In addition, based on consultations with experts, 11 (eleven) criteria were obtained for the area of expertise in Informatics and Computer Engineering, 8 (eight) criteria for the area of expertise in Business Management, and 7 (seven) criteria for the area of expertise in Tourism.
The criteria that will be used in this study in determining the majors are as follows: a. Criteria in the Field of ICT Expertise 1) Middle school report card MTK grades (K1)  The following is an explanation of each of the criteria used in this study, namely: a. ICT Expertise 1) Mathematics and English grades from junior high school report cards Mathematics and English grades originating from junior high school report cards or equivalent are criteria that influence the determination of majors. The calculation of math scores and English scores is determined by the educational unit with the value interval i shown in Table 3.  Table 4.  Table 5.

) The Value of Logic and Creativity
The value of logic and creativity that comes from the results of the psychological test is a criterion that influences the determination of majors with interval values equivalent to the scores of productive subjects in SMK. The calculation of logical value and creativity value is determined by the educational unit with the value interval i shown in Table 6.  Table 7.  Table 8. The KDK score derived from the results of matriculation is one of the criteria that influences the determination of majors, where the results of this matriculation are obtained at the beginning of the learning period for class x (ten) with interval values equivalent to the scores of productive subjects in Vocational High Schools. The calculation of the KDK value is determined by the educational unit with the value intervals shown in Table 9. The score of the interview is one of the criteria that influences the determination of majors with an interval value equal to the value of productive subjects at SMK. The calculation of the interview value is determined by the education unit with the value intervals shown in Table 10.   The PJOK score derived from the matriculation results is one of the criteria that influences the determination of majors, where the matriculation results are obtained at the beginning of the learning period for class x (ten) with interval values equivalent to the scores of productive subjects in SMK. The calculation of the PJOK value is determined by the educational unit with the value intervals shown in Table 13. The score of the interview is one of the criteria that influences the determination of majors with an interval value equal to the value of productive subjects at SMK. The calculation of the interview value is determined by the educational unit with the value intervals shown in Table 14. At this stage a matching process is carried out between student profiles and majors which is divided into the following stages: 1. Identify the needs of the decision maker 2. Determination of the membership function of each criterion

A. Accuracy Testing
The test results for calculating the accuracy value show that the SPK model is quite good. The test was carried out involving the BK teacher and the head of the department using 30 test data. The author tries to compare the results of accuracy testing between calculations determined by experts and decision support system applications as follows: For manual accuracy testing, it can be seen in Table  16. Experts are asked to determine the direction according to the criteria that have been presented which will then be compared with the results of the decision support system, which can be seen in Table 16. Based on the results of the comparison of accuracy testing in Table 16 above, it can be concluded that the manual system and the application are not much different, it's just that the application displays the calculation results in more detail down to the ranking, where the ranking will determine whether students enter the department according to the quota exist or not. With this it can be seen that the application of this decision support system has the same validity as the results of manual system determination by experts. Table 16. displays the test results in the form of a confusion matrix , it can be seen that from all the test data (30 students) there were 11 people recommended to the Software Engineering major, 11 people were recommended to the Multimedia department and 8 people were recommended to the Computer and Network Engineering department.  Table 17. Then the accuracy value can be calculated as follows: (%) = (10 + 10 + 8) (10 + 1 + 0 + 0 + 10 + 1 + 0 + 0 + 8) 100% = 93% From these calculations obtained an accuracy value of 93%. The accuracy value indicates that the system can provide recommendations for determining the direction correctly.

B. Comparison of Interpolation and Ordinal Weights
In making a comparison of the weighting method between interpolation weighting and ordinal weighting, the researcher performed an accuracy calculation using the ordinal weighting method with the same test data as in Table 15 and the results can be seen in Table 18. (%) = (9 + 7 + 6) (9 + 1 + 1 + 1 + 7 + 3 + 2 + 0 + 6) 100% = 73% From these calculations obtained an accuracy value of 73%. The accuracy value indicates that the ordinal weighting is below the accuracy value calculated using interpolation weighting. To see the performance of the interpolation method, a comparison is made with the ordinal method which is the weighting of the profile matching method . Figure 1. presents the results of a comparison of the accuracy values of the interpolation weighting method with ordinal weighting.  Figure 2. shows that the accuracy value of the profile matching method is higher when the interpolation weighting method is applied, compared to the ordinal weighting method. In interpolation weighting, the accuracy value increases compared to ordinal weighting. This can happen because in the interpolation weighting method, the resulting weight values are more accurate because they use a proportional weighting formula.
Meanwhile in ordinal weighting, the value of the weight is determined constant (fixed). Figure 3. presents an example of a comparison of weights on mathematical value parameters using the interpolated weighting method which will produce weights that are more flexible than ordinal weightings. At the testing stage with UAT, the researcher used descriptive analysis. Researchers provide or distribute questionnaires to users based on perceived usefulness constructs , perceived ease of use constructs, and perceived user acceptance constructs . Questionnaire-based UAT testing used a Likert scale and was given to 8 respondents from Counseling Guidance Teachers and Head of Skills Competency at SMK Wikrama Bogor as system users. The Likert scale is given a weighted value as below:   Strongly agree To calculate the results of the UAT questionnaire, the following equation is used: 1. The total answer indicators are obtained by adding up each line of answer indicators 2. The actual score is obtained by multiplying the value weight by the number of answers 3. The actual total score is obtained by adding up each actual score value 4. The ideal score is obtained by multiplying the number of respondents with the highest weight 5. The ideal total score is obtained by multiplying the ideal score by the number of questions that exist. The following is the calculation of the percentage of each determined perception aspect: 1. Percentage of Scores on Perceived Usefulness Aspects With an actual % score of 81.88%, it can be concluded that the respondents strongly agree from the aspect of user acceptance.  Table 26. summarizes the results of UAT testing with 3 (three) aspects of testing, obtained from the percentage of model scores on the perceived usefulness aspect of 84.17%, the percentage of perceived ease of use aspects (perceived ease of use ) of 81.00% and aspects of user acceptance ( User Acceptance ) of 81.88%. From the overall average test results using the UAT method of 82.35%, it is concluded that the user strongly agrees with the proposed system.

IV.
CONCLUSION Based on the research and discussion that has been done, it can be concluded as follows: 1. This research produces a decision support system model for determining majors using the Profile Matching Interpolation method that suits your needs. 2. Based on the results of testing the decision support system model for determining the direction, an accuracy value of 93% was obtained. 3. The decision support system model for determining the direction using the Profile Matching method with interpolation weighting has succeeded in increasing the accuracy value compared to the ordinal weighting method.