Application of the C4.5 Algorithm for Predicting Students' Learning Styles Based on Somatic, Auditory, Visual, and Intellectual Models
DOI:
10.33395/sinkron.v8i4.14032Keywords:
Data Mining, C4.5 Algorithm, Learning Styles, Decision Tree, Prediction, StudentAbstract
Education in Indonesia has seen significant development over the past few decades, with government efforts to improve access and quality of education throughout the country. Programs such as the 12-Year Compulsory Education and curriculum revitalization have driven an increase in school participation rates. However, challenges such as the quality gap between urban and rural areas and the low competence of teachers remain key issues in achieving more equitable and high-quality education for all segments of society. This study aims to apply the C4.5 algorithm to predict students' learning styles based on the Somatic, Auditory, Visual, and Intellectual (SAVI) model. Learning styles are an important aspect of education that affects the effectiveness of learning. By understanding individual learning styles, educators can optimize teaching methods according to students' needs. In this study, student learning style data was collected and analyzed using the C4.5 algorithm, an effective decision tree method for data classification. The results of this algorithm are decision trees that categorize students into one of four learning styles based on specific features. This study shows that the C4.5 algorithm has good accuracy in predicting learning styles, with an entropy value of 1.55 and a gain of 0.156. The implementation of the results of this study is expected to help teachers develop more optimal teaching strategies in preparing learning materials according to students' learning styles.
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