Open Access Peer-reviewed Research Article

Smartwatch Selection Recommendation System Using the K-Nearest Neighbor (KNN) Algorithm with Dynamic Dataset Optimization

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Data Process Architecture Design
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Submitted   Jan 24, 2025
Published   Apr 25, 2025
Issue    Vol 5 No 1 (2025)
DOI   10.25082/AMLER.2025.01.013

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Gino Erman Agusta corresponding author
Department of Electronics and Informatics Engineering Education, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
Fatchul Arifin
Department of Electronics and Informatics Engineering Education, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia

Abstract

This research aimed to develop a smartwatch recommendation system using the K-Nearest Neighbor (KNN) algorithm with dynamic dataset optimization. By employing a dynamic dataset, the accuracy of KNN calculations was enhanced. The dataset, stored in CSV format, was filtered based on user preferences when searching for a smartwatch, generating a dynamic dataset tailored to individual needs. The research involved 35 respondents to evaluate the precision and feasibility of the application. Results showed that 25.7% of respondents found the application highly relevant to their preferences, 31.4% relevant, and 31.4% somewhat relevant. User satisfaction levels indicated that 34.3% were very satisfied, 34.3% satisfied, and 20% somewhat satisfied, highlighting the application’s effectiveness in meeting user expectations.

Keywords
dynamic dataset, K-Nearest Neighbor, recommendation system, smartwatch

Article Details

How to Cite
Agusta, G. E., & Arifin, F. (2025). Smartwatch Selection Recommendation System Using the K-Nearest Neighbor (KNN) Algorithm with Dynamic Dataset Optimization. Advances in Mobile Learning Educational Research, 5(1), 1388-1399. https://doi.org/10.25082/AMLER.2025.01.013
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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