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All Journal International Journal of Electrical and Computer Engineering HAYATI Journal of Biosciences ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Prosiding SNATIKA Vol 01 (2011) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics ILKOM Jurnal Ilmiah Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Aina Musdholifah
Department Of Computer Science And Electronics, Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Journal : Journal of Applied Data Sciences

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Utilizing Support Vector Machine and Dimensionality Reduction to Identify Student Learning Styles within the Felder-Silverman Model Hananto, Andhika Rafi; Musdholifah, Aina; Wardoyo, Retantyo
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.337

Abstract

This research explores the impact of questionnaire structure on the accuracy of learning style classification, focusing on the optimization of the Felder-Silverman Learning Style Model (FSLSM) using advanced machine learning techniques. By employing Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, the study identifies and retains the most informative variables from the original 44-question FSLSM instrument. These refined features are then processed through a Support Vector Machine (SVM) algorithm to evaluate classification performance across various core-to-secondary item ratios. Results indicate that the most optimal configuration—produced through the combined PCA-t-SNE reduction—achieved a peak accuracy of 89.54%, surpassing other configurations and highlighting the effectiveness of selective question modeling. This approach not only enhances prediction accuracy but also introduces a more efficient and streamlined FSLSM formula, reducing redundancy without compromising diagnostic precision. The study contributes to educational data mining by presenting a data-driven strategy for learning style assessment and offers practical implications for the development of adaptive, personalized learning systems grounded in statistically validated models.
Co-Authors Abednego Dwi Septiadi Aditya Permana Anastasya Latubessy Anjani, Sarah Anny Kartika Sari Arda Yunianta Br Bangun, Elsi Titasari Ekki Rinaldi Erick Alfons Lisangan Faisal Ramadhan Hamda, Hizbullah Hananto, Andhika Rafi Hani Febri Mustika Hani Febri Mustika Haviluddin Haviluddin Herdiesel Santoso Herlina Jayadiyanti Jayadiyanti, Herlina Lilik Sumaryanti Luthfiansyah Ilhamnanda Yusuf Mirwan Ushada Mohd Shahizan Othman Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Rahman Indra Kesuma Rahmat Budiarsa Retantyo Wardoyo Rinaldi, Ekki Rosihan Rosihan, Rosihan Sapuletea, Chrissandy Sri Hartati Sri Kusrohmaniah, Sri Sri Mulyana Tempola, Firman Timothy K. Shih Tsuyoshi Okayama Wahyu Srimulyani Yudha Islami Sulistya Yunianta, Arda Yusof, Omar Obarukab Norazah