Implementasi Naive Bayes untuk Mengklasifikasi Pembelajaran Inovatif Terhadap Motivasi Belajar - Damar Firdaus; Efan Efan; Yadi Yadi (Explorer, 2026)
Learning motivation is a key factor in determining students' success in the educational process. Innovative learning methods are believed to increase motivation through interactive and engaging approaches. However, identifying the level of student motivation based on learning activities still becomes a challenge. This study aims to classify the influence of innovative learning on student motivation using the Naïve Bayes algorithm. The research method applies the CRISP-DM framework, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data were collected through observation, interviews, questionnaires, and literature studies conducted at MTS Guppi Pagar Alam. The dataset consists of 70 students with attributes including teaching methods, learning media, classroom interaction, task creativity, and learning independence. The classification process divides data into training (80%) and testing (20%) sets. The results show that the Naïve Bayes model achieves an accuracy of 87%, indicating that the algorithm performs effectively in classifying student motivation levels. The findings reveal that classroom interaction and task creativity significantly influence learning motivation. This study contributes to providing insights for educators in designing more effective and adaptive learning strategies.