Abstract
There are at least two factors that determine the success of a student in choosing a study program. First, the selection of study programs must be in accordance with their interests and talents. In this case, it is necessary to take an interest and aptitude test. One of the most well-known interest and aptitude tests is the RIASEC (Realistic, Investigative, Artistic, Social, Enterprising, and Conventional) test introduced by John Holland in the 1970s. Secondly, prospective students must compare themselves to similar alumni in terms of abilities and school backgrounds to determine their suitability for the same study program. The first factor has been discussed and solved by Intan et al. in [1]. To tackle the second factor, this paper introduces a soft computational technique for prospective students to predict their performance by analyzing alumni with similar profiles. Some of the attributes used to assess the similarity of prospective students with their seniors include grades in mathematics and English, school origin, and majors in high school.
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Intan, R., Setiawan, J.A. (2024). Study Program Recommendation System Based on the Ability Prediction of Prospective Students. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 1167. Springer, Cham. https://doi.org/10.1007/978-3-031-73318-5_19
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DOI: https://doi.org/10.1007/978-3-031-73318-5_19
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