Web-Based Decision Support System for Major Selection Using the SAW Method at Efata Ombarade Vocational School - Ina Tena Bolo; Friden Elefri Neno; Emerensiana Dappa Ege (Journal of Computing Innovations and Emerging Technologies, 2025)
Efata Ombarade Vocational High School is located in West Sumba Regency, East Wewewa District, East Nusa Tenggara Province. It is a vocational high school offering two majors: tourism and hospitality services business. Every new school year, this school routinely accepts new students, where each applicant chooses a major according to their preferences, which may not be in line with their abilities. To improve the quality of the school and its students, each new student admission involves a selection process based on criteria set by the school, such as National Examination Scores, Report Card Scores, written tests, interviews, and health checks. The current student registration and selection process has several weaknesses, including the time-consuming process of entering data into Microsoft Excel and the delay in obtaining results due to the lack of a specific application to support the calculations. In view of these issues, a system is needed to assist in the process of making faster, more accurate, and more objective decisions regarding student majors. One solution offered is the implementation of a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method. This method works by assigning weights to each criterion used in the assessment, then calculating preference scores to determine the best alternative. The data used includes students' academic scores, particularly their National Examination results, as well as data on their interests. The use of the SAW method in the major selection decision support system is expected to reduce unfairness in assessment, as small differences in scores can be processed proportionally. With this system in place, schools can more easily determine the majors that suit students' abilities and interests. Additionally, this system can also speed up the major selection process, reduce the potential for manual errors, and provide more accurate and fair recommendations for each student.