Prediksi Gaya Belajar Selmes Melalui Kemampuan Berpikir Aljabar Menggunakan Analisis Diskriminan Linear
DOI:
10.29303/jm.v8i3.12706Published:
2026-08-12Downloads
Abstract
Learning styles can be associated with mathematical abilities, particularly algebraic thinking ability, where variations in students abilities may indicate differences in their learning styles. This study aims to predict Selmes learning styles based on algebraic thinking ability using Linear Discriminant Analysis (LDA). This research employed a quantitative approach conducted among 7th and 8th grade students at SMP Islam Teratai Putih Global. The sample consisted of 57 students selected using a simple random sampling technique. The research instruments included an algebraic thinking ability test and the Selmes learning style questionnaire, which classified into five categories: surface, deep, hard work, organisation, and motivation. The data were analyzes using LDA to predict students learning styles according to Selmes theory based on their algebraic thinking ability. The result showed that the model generated discriminant functions and posterior probability that were used to determine prediction outcomes. Furthermore, the test results indicated that the discriminant model was statically significant in predicting Selmes learning styles based on algebraic thinking ability. The model performance evaluation demonstrated that the model achieved relatively good performances in terms of accuracy, macro average precision, and macro average F1-Score. However, the macro average recall indicated that the model performance still requires further improvement.
Keywords:
Algebraic Thinking Ability Linear Analysis Discriminant Selmes Learning Styles Kemampuan Penalaran MatematisReferences
Ainurrochmah, A., Hayati, M. N., & Satriya, A. M. A. (2020). Perbandingan klasifikasi analisis diskriminan fisher dan metode naive bayes. Jurnal Aplikasi Statistika & Komputasi Statistik, 11(2), 37–48. https://doi.org/10.34123/jurnalasks.v11i2.156
Amin, N. F., Garancang, S., & Abunawas, K. (2023). Konsep umum populasi dan sampel dalam penelitian. PILAR, 14(1), 15–31. https://doi.org/10.26618/whw41w62
Arielhan, F. N., & Lestari, K. E. (2025). Model persamaan diskriminan untuk prediksi gaya belajar selmes berdasarkan kemampuan metakognisi. Polinomial: Jurnal Pendidikan Matematika, 4(3), 807–819. https://doi.org/10.56916/jp.v4i3.2067
Ghosh, A., SahaRay, R., Chakrabarty, S., & Bhadra, S. (2021). Robust generalised quadratic discriminant analysis. Pattern Recognition, 117, 1–16. https://doi.org/10.1016/j.patcog.2021.107981
González-estrada, E., Villaseñor Rocío, J. A., & Acosta-Pech, J. (2022). Shapiro-Wilk test for multivariate skew-normality. Computational Statistics, 37(3), 1985–2001. https://doi.org/10.1007/s00180-021-01188-y
Gunawan, R. A., Zulkarmain, D. P., & Arianto, S. T. (2024). Perbandingan metode ordinary least square (OLS) dan metode partial least square (PLS) untuk mengatasi multikolinearitas. Socius: Jurnal Penelitian Ilmu-Ilmu Sosial, 1(6), 97–103. https://doi.org/doi.org/10.5281/zenodo.10476911
Handayani, M., Putri, N. L., & Wulandari, S. P. (2024). Analisis diskriminan pada indikator yang memengaruhi indeks kerentanan pangan menurut provinsi di indonesia tahun 2023. Bilangan : Jurnal Ilmiah Matematika, Kebumian Dan Angkasa, 2(6), 67–88. https://doi.org/10.62383/bilangan.v2i6.319
Isra, N., Annas, S., & Aidid, M. K. (2022). Pengembangan paket R untuk analisis diskriminan berbasis graphical user interface web interaktif. VARIANSI: Journal of Statistics and Its application on Teaching and Research, 4(3), 128–141. https://doi.org/10.35580/variansiunm24
Khondoker, M., Dobson, R., Skirrow, C., Simmons, A., & Stahl, D. (2016). A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies. Statistical Methods in Medical Research, 25(5), 1804–1823. https://doi.org/10.1177/0962280213502437
Kieran, C. (2004). Algebraic thinking in the early grades : what is it? The Mathematics Educator, 8(1), 139–151. https://math.nie.edu.sg/ame/matheduc/tme/tmeV8_1/Carolyn Kieran.pdf
Konilah, K., Sunarsih, D., & Purnomo, A. (2022). Analisis gaya belajar peserta didik pada pembelajaran matematika kelas V MI. Jurnal Ilmiah KONTEKSTUAL, 3(2), 141–149. https://doi.org/10.46772/kontekstual.v3i02.664
Lew, H.-C. (2004). Developing algebraic thinking in the earlier grades : case study of korean elementary school mathematics. The Mathematics Educator, 8(1), 88–106. https://math.nie.edu.sg/ame/matheduc/tme/tmeV8_1/Hee-Chan Lew.pdf
Pinem, T. H., & Putra, Z. P. (2025). Evaluasi kinerja algoritma klasifikasi deep learning dalam prediksi diabetes. FIFO, 17(1), 17–28. https://doi.org/10.22441/fifo.2025.v17i1.003
Rencher, A. C. (2002). Methods of multivariate analysis (Second Edi). John Wiley & Sons, Inc.
Rosida, A., & Masduki, M. (2023). Exploring student algebraic thinking in solving math problems in terms of kolb’s learning style. Jurnal Pendidikan Matematika (JUPITEK), 6(1), 62–72. https://doi.org/10.30598/jupitekvol6iss1pp62-72
Saputri, I., Widiawati, C. R. A., & Yunita, I. R. (2025). Implementasi linear discriminant analysis ( LDA ) untuk deteksi gejala penyakit jantung. Infotekmesin, 16(02), 507–516. https://doi.org/10.35970/infotekmesin.v16i2.2377
Selmes, I. (1987). Improving study skills. Hodder And Stoughton.
Susanto, C. P., Arini, D. U., Yuntina, L., Soehaditama, J. P., & Nuraeni, N. (2024). Konsep penelitian kuantitatif: populasi, sampel, dan analisis data (sebuah tinjauan pustaka). Jurnal Ilmu Multidisplin, 3(1), 1–12. https://doi.org/10.38035/jim.v3i1.504
Wahyuni, N., & Rindrayani, S. R. (2025). Metodologi penelitian asosiasi. Musytari: Jurnal Manajemen, Akuntansi, Dan Ekonomi, 14(9). https://doi.org/10.8734/musytari.v14i9.10767
License
Copyright (c) 2026 Rashieka Widyarini Suryoputro, Karunia Eka Lestari, Ekasatya Aldila Afriansyah
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
