Optimalisasi Strategi Pembelajaran Siswa Melalui Identifikasi Gaya Belajar Menggunakan Klasterisasi K-Means dan Klasifikasi K Nearest Neighbor
-
Ilsa Hidayat
*
Universitas Putra Indonesia YPTK Padang, Padang,
Indonesia
- Musli Yanto Universitas Putra Indonesia YPTK Padang, Padang, Indonesia
- Rini Sovia Universitas Putra Indonesia YPTK Padang, Padang, Indonesia (*) Corresponding Author
Abstract
Accuracy in adjusting teaching strategies to student learning characteristics is important because it can determine the effectiveness of the learning process. One of the key factors in improving the quality of learning is the suitability between teachers' teaching strategies and students' learning styles. The mismatch between the two aspects can reduce the effectiveness of the learning process and have an impact on low learning outcomes. Based on this, this study aims to optimize students' learning strategies through the application of the K-Means clustering model and the K-Nearest Neighbor classification. The performance of the K-Means Algorithm is able to classify learning styles and determine the labeling of learning styles, K-Nearest Neighbor is used to classify data that has been labeled by the K-Means algorithm. This research dataset amounted to 200 student data sourced from SMP Negeri 1 Panyabungan from the results of 20 questions answered by students. The results showed that the combination of the K-Means and K-Nearest Neighbor algorithms produced good performance with an accuracy value of 0.92, precision of 0.92, recall of 0.92, and F1-score of 0.91. The contribution of this research is expected to enrich the literature related to the application of the K-Means and K-Nearest Neighbor models in optimizing learning strategies, as well as assisting teachers at SMP Negeri 1 Panyabungan in designing and implementing learning strategies that are more effective and in accordance with the needs of students.
Downloads
References
M. Astuti, F. Ismail, S. Fatimah, W. Puspita, and Herlina, “The Relevance Of The Merdeka Curriculum In Improving The Quality Of Islamic Education In Indonesia,” Int. J. Learn. Teach. Educ. Res., vol. 23, no. 6, pp. 56–72, 2024, doi: 10.26803/ijlter.23.6.3
Y. J. A. Khasawneh, R. Alsarayreh, A. A. Al Ajlouni, H. M. Eyadat, M. N. Ayasrah, and M. A. S. Khasawneh, “An examination of teacher collaboration in professional learning communities and collaborative teaching practices,” J. Educ. e-Learning Res., vol. 10, no. 3, pp. 446–459, 2023, doi: 10.20448/jeelr.v10i3.4841
E. G. Rincon-Flores et al., “Improving the learning-teaching process through adaptive learning strategy,” Smart Learn. Environ., vol. 11, no. 1, 2024, doi: 10.1186/s40561-024-00314-9
S. Chan, S. Maneewan, and R. Koul, “Teacher educators’ teaching styles: relation with learning motivation and academic engagement in pre-service teachers,” Teach. High. Educ., vol. 28, no. 8, pp. 2044–2065, 2023, doi: 10.1080/13562517.2021.1947226
I. Miguel, A. David, C. Henar, G. Sanz, A. Bustillo, and I. M. Alonso, “Evaluation of the novelty effect in immersive Virtual Reality learning experiences,” Virtual Real., vol. 28, no. 1, pp. 1–23, 2024, doi: 10.1007/s10055-023-00926-5
M. C. Malacapay, “The Influence of Learning Styles and Attitudes on Academic Performance of College Students in a Flipped Learning Environment,” Int. J. Instr., vol. 17, no. 4, pp. 623–644, 2024, doi: 10.29333/iji.2024.17435a
D. Chinnapun and U. Narkkul, “Enhancing Learning in Medical Biochemistry by Teaching Based on VARK Learning Style for Medical Students,” Adv. Med. Educ. Pract., vol. 15, pp. 895–902, 2024, doi: 10.2147/AMEP.S472532
A. Gayef, A. Çaylan, and S. A. Temiz, “Learning styles of medical students and related factors,” BMC Med. Educ., vol. 23, no. 1, pp. 1–11, 2023, doi: 10.1186/s12909-023-04267-4
S. G. Essa, T. Celik, Human-Hendricks, and N. Emelia, “Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review,” IEEE Access, vol. 11, no. April, pp. 48392–48409, 2023, doi: 10.1109/ACCESS.2023.3276439
A. R. Shaidullina et al., “Learning styles in science education at university level: A systematic review,” Eurasia J. Math. Sci. Technol. Educ., vol. 19, no. 7, pp. 1–10, 2023, doi: 10.29333/ejmste/13304
R. R. Iyer and R. Sethuraman, “Role of ehealth literacy, learning styles, and patterns of web‑based e‑content access for seeking health information among dental university students in Vadodara, India,” ournal Educ. Heal. Promot., vol. 1, no. 56, February 2024, pp. 1–6, 2024, doi: 10.4103/jehp.jehp_750_23
E. El-Saftawy, A. A. A. Latif, A. M. ShamsEldeen, M. A. Alghamdi, A. M. Mahfoz, and B. E. Aboulhoda, “Influence of applying VARK learning styles on enhancing teaching skills: application of learning theories,” BMC Med. Educ., vol. 24, no. 1, 2024, doi: 10.1186/s12909-024-05979-x
H. Taş and M. B. Minaz, “The Effects of Learning Style-Based Differentiated Instructional Activities on Academic Achievement and Learning Retention in the Social Studies Course,” SAGE Open, vol. 14, no. 2, pp. 1–14, 2024, doi: 10.1177/21582440241249290
Y. Yousaf, M. Shoaib, M. A. Hassan, and U. Habiba, “An intelligent content provider based on students learning style to increase their engagement level and performance,” Interact. Learn. Environ., vol. 31, no. 5, pp. 2737–2750, 2023, doi: 10.1080/10494820.2021.1900875
M. Chaudhry, I. Shafi, M. Mahnoor, D. L. R. Vargas, E. B. Thompson, and I. Ashraf, “A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective,” Symmetry (Basel)., vol. 15, no. 9, pp. 1–44, 2023, doi: 10.3390/sym15091679
A. Sánchez, C. Vidal-Silva, G. Mancilla, M. Tupac-Yupanqui, and J. M. Rubio, “Sustainable e-Learning by Data Mining—Successful Results in a Chilean University,” Sustain., vol. 15, no. 2, pp. 1–16, 2023, doi: 10.3390/su15020895
S. M. Miraftabzadeh, C. G. Colombo, M. Longo, and F. Foiadelli, “K-Means and Alternative Clustering Methods in Modern Power Systems,” IEEE Access, vol. 11, no. September, pp. 119596–119633, 2023, doi: 10.1109/ACCESS.2023.3327640
I. Hidayat, E. Darnila, and Y. Afrillia, “Clustering Zonasi Daerah Rawan Bencana Alam di Kabupaten Mandailing Natal menggunakan Algoritma K-Means,” G-Tech J. Teknol. Terap., vol. 7, no. 3, pp. 1218–1226, 2023, doi: 10.33379/gtech.v7i3.2880
M. Yang, L. Huang, and C. Tang, “K-Means Clustering with Local Distance Privacy,” Big Data Min. Anal., vol. 6, no. 4, pp. 433–442, 2023, doi: 10.26599/BDMA.2022.9020050
E. Setyaningsih, N. Hidayat, U. Lestari, and A. Septiarini, “Modification of K-Means and K-Mode Algorithms To Enhance the Performance of Clustering Student Learning Styles in the Learning Management System,” ICIC Express Lett., vol. 17, no. 1, pp. 49–59, 2023, doi: 10.24507/icicel.17.01.49
J. Li, J. Zhang, J. Zhang, and S. Zhang, “Quantum KNN Classification With K Value Selection and Neighbor Selection,” IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 43, no. 5, pp. 1332–1345, 2024, doi: 10.1109/TCAD.2023.3345251
R. K. Halder, M. N. Uddin, M. A. Uddin, S. Aryal, and A. Khraisat, “Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications,” J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-024-00973-y
S. V. Razavi-Termeh, A. Sadeghi-Niaraki, S. Razavi, and S. M. Choi, “Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling,” Int. J. Digit. Earth, vol. 17, no. 1, pp. 1–29, 2024, doi: 10.1080/17538947.2024.2311325
S. Daulay and R. Wandri, “Integrating K-Means Clustering and K-Nearest Neighbor Classification for Effective Scholarship Recipient Selection,” 2025. doi: 10.32520/stmsi.v14i1.4818
S. Daulay and R. Wandri, “Integrating K-Means Clustering and K-Nearest Neighbor Classification for Effective Scholarship Recipient Selection,” Sistemasi, vol. 14, no. 1, p. 235, 2025, doi: 10.32520/stmsi.v14i1.4818
M. Jebbari, B. Cherradi, S. Hamida, and A. Raihani, Identifying learning styles in MOOCs environment through machine learning predictive modeling, vol. 29, no. 16. Springer US, 2024. doi: 10.1007/s10639-024-12637-8
G. Vardakas, I. Papakostas, and A. Likas, “Efficient error minimization in kernel k-means clustering,” Pattern Anal. Appl., vol. 28, no. 2, 2025, doi: 10.1007/s10044-025-01463-4
G. R. Kanagachidambaresan and N. Bharathi, Learning Algorithms for Internet of Things, 1st ed. Apress Berkeley, CA, 2024. doi: //doi.org/10.1007/979-8-8688-0530-1
H. Zhou, Learn Data Mining Through Excel, 2nd ed. Apress Berkeley, CA, 2023. doi: 10.1007/978-1-4842-9771-1_10
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Optimalisasi Strategi Pembelajaran Siswa Melalui Identifikasi Gaya Belajar Menggunakan Klasterisasi K-Means dan Klasifikasi K Nearest Neighbor
Pages: 807-815
Copyright (c) 2026 Ilsa Hidayat, Musli Yanto, Rini Sovia
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
