Academica Performance Pattern Identification of FMIPA Medan State University Students Using the K-Prototype Clustering Algorithm
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https://doi.org/10.64803/cessmuds.v1.159Keywords:
Algorithm, Cluster, K-Prototype, SlovinAbstract
This study aims to identify patterns in academic performance among students at the Faculty of Mathematics and Natural Sciences (FMIPA), Medan State University (UNIMED) using the K-Prototype clustering algorithm. The method was chosen due to its capability to handle mixed data types, combining both numerical and categorical variables. Data were collected through a structured questionnaire, and the sample size was determined using Slovin's formula, resulting in 373 student records for analysis. The clustering process revealed three distinct groups: students with stable academic records but low organizational engagement; highly motivated new students; and independent senior students active in extracurricular activities. Each cluster reflects unique combinations of GPA, credit load, learning behavior, and involvement. The findings provide a data-driven foundation for designing targeted academic advising and student support strategies.
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Copyright (c) 2025 Nurul Ain Farhana, Putri Maulidina Fadilah, Putri Harliana, Suwanto Suwanto (Author)
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