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September 10, 2025Jurnal Penelitian Pendidikan/Jurnal penelitian pendidikan

Course Scheduling Optimization Using Genetic Algorithms with Fuzzy Tsukamoto-Based Fitness Adjustment

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Authors

TATaripar Matius AlexanderAPAnggyi Trisnawan Putra

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Overview

This analysis demonstrates improved scheduling outcomes using a genetic algorithm and fuzzy inference, indicating more efficient course management.

Key Points

  • The hybrid genetic algorithm improves convergence speed by 42%, achieving an average fitness value of 0.89.
  • Constraint satisfaction increased from 82.4% to 94.7%, indicating higher compliance with scheduling requirements.
  • The study utilized a synthetic dataset of 50 courses, optimizing workload distribution and classroom utilization.
  • Results show significant performance enhancements, with statistical testing demonstrating a significant effect size (Cohen’s d = 1.23).

Cite This Study

Alexander et al. (2025) studied this question.

synapsesocial.com/papers/68c1aabf54b1d3bfb60e2e3ahttps://doi.org/10.15294/jpp.v42i2.31594
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