Aplikasi Deep Learning untuk Prediksi Hasil Belajar Matematika Berdasarkan Gaya Belajar Siswa - Tulloh, Irza As-sajdah; Dwitasari, Putri; Pratama, Ayoudya Shakthi; Netriwati, Netriwati; Nendra, Fadly (Griya Journal of Mathematics Education and Application, 2025)
This study aims to analyze the application of deep learning in predicting students’ mathematics learning outcomes based on their learning styles. This research employs a quantitative approach using descriptive, inferential, and predictive analysis techniques based on deep learning. The research sample consisted of 61 eleventh-grade students from SMAN 12 Bandar Lampung. Data were collected through a learning style questionnaire based on the VARK model (Visual, Auditory, Read/Write, and Kinesthetic) and a mathematics achievement test (posttest). Descriptive analysis results showed that the most dominant learning style was read/write. The Kolmogorov–Smirnov normality test indicated that the data were not normally distributed, so the analysis was continued using the Kruskal–Wallis test. The results showed an Asymp. Sig value of 0.684 (> 0.05), indicating no significant difference in mathematics learning outcomes among dominant learning style groups. Nevertheless, the implementation of deep learning still shows potential in assisting the prediction of learning outcomes by considering various factors such as motivation, teaching methods, and learning environment.