Klasifikasi dan Rekomendasi Strategi Pembelajaran berbasis Gaya Belajar menggunakan Artificial Neural Network - Yusuf, Mohamad Fauzi; Rohandi, Manda; Rijal, Bait Syaiful; Olii, Salahudin; Pakaja, Jemmy A.; Suwandi, Ihsanulfu'ad (Universitas Negeri Gorontalo, 2026)
Differences in student learning styles demand adaptive and personalized learning strategies. This study aims to develop a web-based learning strategy recommendation system utilizing Artificial Neural Network (ANN) to model the relationship between students' learning styles and appropriate learning strategies. Learning style identification was conducted using the Felder-Silverman questionnaire encompassing four dimensions: active–reflective, sensing–intuitive, visual–verbal, and sequential–global. The study employed a Research and Development (R&D) method with the 4D model and Personal Extreme Programming (PXP) approach. Data were collected from 25 seventh-grade students at SMP Negeri 1 Tomilito. A Multilayer Perceptron ANN model was trained using the backpropagation algorithm over 3,000 epochs, yielding a Mean Squared Error (MSE) value of 0.0541, indicating a relatively low prediction error rate. System feasibility testing obtained a score of 85.42%, categorized as