Application of TOPSIS and K-Means Clustering Methods in Recommendations and Analysis of Study Program Interests for New Students
DOI:
https://doi.org/10.31004/jestm.v5i1.251Keywords:
Decision Support System, Topsis, Clustering, K - Means, Pahlawan Tuanku Tambusai UniversityAbstract
The selection of a study program is one of the crucial initial decisions for prospective students when entering college. This decision is ideally based on a good understanding of their interests, talents, and abilities so that prospective students can study optimally and in accordance with their potential. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision-making method where the best alternative has the longest distance from the negative ideal solution and has the shortest distance from the positive ideal solution. The selection of these criteria and alternatives aims to produce relevant and accurate recommendations in helping prospective students determine the choice of study program that best suits their potential and preferences. The results of these recommendations are then further analyzed to group the results of the recommendations based on the category of interest. The K-Means Clustering method using the K-Means method with the results of C1 with 9 Respondentsts of less interested study programs, C2 with 25 Respondentsts of moderately interested study programs, and C3 with 21 Respondentsts of highly interested study programs
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