Comparison of K-Means Clustering and Fuzzy Tsukamoto Algorithms for Grouping Student Data of NU Medan Middle School Based on Academic Achievement
DOI:
https://doi.org/10.35335/jict.v16i2.262Keywords:
Clustering, Educational Data Analysis, Fuzzy Tsukamoto, K-Means, Student AchievementAbstract
The increasing need for data-driven decision-making in education has encouraged the use of intelligent algorithms to evaluate and classify student academic performance more effectively. However, differences in algorithmic approaches often lead to variations in interpretation and categorization outcomes. This study aims to compare the performance of the K-Means and Fuzzy Tsukamoto algorithms in clustering student achievement data at SMP NU Medan to determine which method provides a more accurate and interpretable classification model. The research employs quantitative analysis using students’ semester grades processed through Python and Microsoft Excel, where K-Means utilizes centroid-based clustering (75, 85, and 95) and Fuzzy Tsukamoto applies fuzzy logic with weighted membership values (0, 5, and 10). The results reveal that K-Means produces a more proportional and stable clustering structure, effectively differentiating student achievement levels within the same population, while Fuzzy Tsukamoto offers a simpler, rule-based classification system aligned with fixed academic standards. The findings indicate that K-Means is more suitable for analyzing relative performance variations, whereas Fuzzy Tsukamoto is better suited for absolute classification and administrative evaluation. Both methods are easily implemented and can be integrated into educational management systems to enhance instructional decision-making. The study implies that a hybrid combination of these two algorithms may provide a more comprehensive analytical framework for evaluating student performance.
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