Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Articles

ANALYSIS OF APRIORI AND K-NEAREST NEIGHBOR (KNN) ALGORITHM IN RECOMMENDING APPROPRIATE LEARNING METHOD

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  • Nuril Lutvi Azizah,
  • Ade Eviyanti,
  • Novia Ariyanti,
  • Gita Wardani,
  • Naila Farah Diba
Nuril Lutvi Azizah
Informatics Department, Faculty of Science and Technology, Universitas Muhammadiyah Sidoarjo, Indonesia
Ade Eviyanti
Informatics Department, Faculty of Science and Technology, Universitas Muhammadiyah Sidoarjo, Indonesia
Novia Ariyanti
Informatics Department, Faculty of Science and Technology, Universitas Muhammadiyah Sidoarjo, Indonesia
Gita Wardani
Informatics Department, Faculty of Science and Technology, Universitas Muhammadiyah Sidoarjo, Indonesia
Naila Farah Diba
Informatics Department, Faculty of Science and Technology, Universitas Muhammadiyah Sidoarjo, Indonesia
DOI: https://doi.org/10.30598/barekengvol20iss1pp0557-0572
Published November 24, 2025
Keywords
  • Apriori algorithm,
  • Classification,
  • K-Nearest neighbor,
  • Learning methods
How to Cite
[1]
N. L. Azizah, A. Eviyanti, N. Ariyanti, G. Wardani, and N. F. Diba, “ANALYSIS OF APRIORI AND K-NEAREST NEIGHBOR (KNN) ALGORITHM IN RECOMMENDING APPROPRIATE LEARNING METHOD”, BAREKENG: J. Math. & App., vol. 20, no. 1, pp. 0557-0572, Nov. 2025.

Copyright (c) 2025 Nuril Lutvi Azizah, Ade Eviyanti, Novia Ariyanti, Gita Wardani, Naila Farah Diba

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

The study investigates the utilization of data mining techniques, especially the Apriori algorithm and K-Nearest Neighbor (KNN) classification, in recommending appropriate learning methods based on student data. The purpose of this research is to analyze patterns and groupings in students’ behavior, preferences, and academic performance to support more informed and personalized educational strategies. The Apriori algorithm is used to identify frequent associations among learning related attributes, while KNN classification helps group students with similar learning characteristics. The analysis revealed that the digital learning method is the most preferred by students, with a percentage of 84.29%, followed by the traditional lecture method at 15.70%. These results reflect a notable trend toward technology-driven, flexible learning environments, although conventional approaches continue to hold relevance for a portion of learners. The research concludes that the integration of the Apriori algorithm and KNN clustering proves to be an effective analytical framework for facilitating adaptive learning. This approach allows educators and institutions to make data-driven decisions in tailoring instructional methods that align with the diverse needs and preferences of students.

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