ANALISIS DAMPAK STRATEGI PEDAGOGI TERHADAP MINAT BELAJAR SISWA MENGGUNAKAN RANDOM FOREST

Student learning interest plays a crucial role in educational success, as it directly influences engagement, comprehension, and academic achievement. This study aims to analyze the influence of pedagogical strategies on students’ learning interest using a machine learning approach with the Random Forest algorithm. Eight aspects of teaching strategies were examined as predictor variables, while learning interest was measured through two main indicators: interest in real-world application of the material and motivation for self-directed learning. Data were collected from 100 students via a Likert-scale questionnaire and analyzed using Orange Data Mining. The model was validated through 10-fold cross-validation and evaluated using accuracy, precision, recall, F1-score, and AUC. The results indicate strong model performance, with 95% accuracy, 96.7% precision, 97.8% recall, and a 97.2% F1-score. Feature importance analysis identified practical activities (P4), an inclusive learning environment (P6), and the use of technology (P3) as the most influential predictors of learning interest. In contrast, variables such as P1, P2, and P8 showed minimal contribution. These findings demonstrate that Random Forest is not only effective for classification tasks but also valuable in identifying key factors for improving pedagogical strategies. The results are expected to inform the development of more adaptive, interactive, and student-centered learning environments.