Klasifikasi dan Rekomendasi Strategi Pembelajaran berbasis Gaya Belajar menggunakan Artificial Neural Network

Mohamad Fauzi Yusuf, Manda Rohandi, Bait Syaiful Rijal, Salahudin Olii, Jemmy A. Pakaja, Ihsanulfu'ad Suwandi

Abstract


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 "Very Feasible." The developed system is capable of identifying students' learning styles and automatically generating learning strategy recommendations, thereby potentially supporting teachers in designing more adaptive and personalized learning experiences.

Perbedaan gaya belajar siswa menuntut adanya strategi pembelajaran yang adaptif dan terpersonalisasi. Penelitian ini bertujuan mengembangkan sistem rekomendasi strategi pembelajaran berbasis web yang memanfaatkan Artificial Neural Network (ANN) untuk memodelkan hubungan antara gaya belajar dan strategi pembelajaran yang sesuai. Identifikasi gaya belajar dilakukan menggunakan kuesioner Felder-Silverman yang mencakup empat dimensi: aktif–reflektif, sensori–intuitif, visual–verbal, dan sequential–global. Penelitian menggunakan metode Research and Development (R&D) dengan model 4D dan pendekatan Personal Extreme Programming (PXP). Data dikumpulkan dari 25 siswa kelas VII SMP Negeri 1 Tomilito. Model ANN Multilayer Perceptron dilatih menggunakan algoritma backpropagation dengan 3000 epoch dan menghasilkan nilai Mean Squared Error (MSE) sebesar 0,0541, yang mengindikasikan tingkat kesalahan prediksi yang relatif rendah. Hasil uji kelayakan sistem memperoleh skor 85,42% dengan kategori "Sangat Layak". Sistem yang dikembangkan mampu mengidentifikasi gaya belajar siswa dan memberikan rekomendasi strategi pembelajaran secara otomatis, sehingga berpotensi mendukung guru dalam merancang pembelajaran yang lebih adaptif dan personal.

Keywords


Artificial Neural Network; Gaya belajar; Felder-Silverman; Pembelajaran adaptif; Rekomendasi strategi pembelajaran

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References


Adawiyah, T. A., Harso, A., & Nassar, A. (2020). Hasil belajar IPA berdasarkan gaya belajar siswa. Science and Physics Education Journal (SPEJ), 4(1), 1–8.

Anwar, S., Kurnia, D. A., Faqih, A., & Sari, S. R. (2022). Prediksi hasil belajar hybrid menggunakan artificial neural network dengan multilayer perceptron. JURIKOM (Jurnal Riset Komputer), 9(5), 1591–1600.

Bhakti, H. D. (2019). Aplikasi artificial neural network (ANN) untuk memprediksi masa studi mahasiswa Program Studi Teknik Informatika Universitas Muhammadiyah Gresik. Eksplora Informatika, 9(1), 88–95.

Brahmantio, D. I., & Anistyasari, Y. (2020). Studi literatur pengaruh gaya belajar terhadap e-learning adaptive berbasis web. Jurnal IT-EDU, 5(1), 362–370.

Brownlee, J. (2020). Data preparation for machine learning. Machine Learning Mastery.

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014

Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681.

Graf, S., Liu, T. C., & Kinshuk. (2009). Analysis of learner characteristics for adaptivity in learning management systems. International Journal of Cognitive and Neurological Sciences, 1(1), 1–7.

Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.

Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall.

Kurniati, A. B., Sidik, W. A., & Jajang. (2024). Model artificial neural networks (ANN) untuk prediksi COVID-19 di Indonesia. JST (Jurnal Sains dan Teknologi), 12(3), 833–844.

Lase, Y. Y., Syafli, S. A., Fatmi, Y., Prayudani, S., & Lubis, A. R. (2024). Klasifikasi gaya belajar siswa menggunakan algoritma Naïve Bayes. Jurnal Informatika dan Komputer, 4(3), 1782–1787.

Leff, A., & Rayfield, J. T. (2001). Web-application development using the Model/View/Controller design pattern. In Proceedings of the Fifth IEEE International Enterprise Distributed Object Computing Conference (pp. 118–127). IEEE. https://doi.org/10.1109/EDOC.2001.950428

Means, B., Toyama, Y., Murphy, R., & Bakia, M. (2014). Learning online: What research tells us about whether, when and how. Routledge.

Myers, G. J., Sandler, C., & Badgett, T. (2011). The art of software testing (3rd ed.). Wiley.

Nasikhah, U. (2023). Strategi pembelajaran aktif sebagai upaya dalam meningkatkan aktivitas belajar di kelas. Jurnal Keguruan dan Pendidikan Islam, 1(1), 51–64.

Pamungkas, R. (2017). Implementasi model personal extreme programming (PXP) dalam pengembangan sistem informasi geografis. Jurnal Teknologi dan Sistem Informasi, 3(1), 45–52.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x

Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002

Sugiyono. (2019). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.

Supangat, & Saringat, M. (2020). Development of e-learning system using Felder and Silverman's index of learning styles model. International Journal of Advanced Trends in Computer Science and Engineering, 9(5), 8554–8561.

Thiagarajan, S., Semmel, D. S., & Semmel, M. I. (1974). Instructional development for training teachers of exceptional children. Indiana University.

Tomlinson, C. A. (2014). The differentiated classroom: Responding to the needs of all learners (2nd ed.). ASCD.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0




DOI: https://doi.org/10.37905/jji.v8i1.35294

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