Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)

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Implementation of K-Means and K-Nearest Neighbor Methods for Laptop Recommendation Websites

Authors
Vincentius Riandaru Prasetyo1, *, Mohammad Farid Naufal1, Budiarjo1
1Department of Informatics Engineering, Faculty of Engineering, University of Surabaya, Surabaya, Indonesia
*Corresponding author. Email: vincent@staff.ubaya.ac.id
Corresponding Author
Vincentius Riandaru Prasetyo
Available Online 19 November 2023.
DOI
10.2991/978-94-6463-288-0_38How to use a DOI?
Keywords
laptop; recommendation; K-Means; KNN
Abstract

Along with technology development, laptops are becoming increasingly popular and are handy tools in everyday life. However, with so many brands and laptops available, people often find it difficult and need help choosing the laptop that best suits their needs and desires. A website-based system has been created to provide laptop recommendations based on user needs and preferences. This system uses the K-Nearest Neighbor (KNN) method to classify user input with datasets that have been grouped using the K-Means method. Thus, users can choose the right laptop according to their needs with the help of this system. Based on the tests’ results, the highest accuracy in the training process is 97%, with a total dataset of 1000 data, which comes from the websites versus.com and arenalaptop.com. Whereas for the validation results obtained from 51 users, the majority stated that the recommendation results were by the criteria of the users.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)
Series
Atlantis Highlights in Engineering
Publication Date
19 November 2023
ISBN
10.2991/978-94-6463-288-0_38
ISSN
2589-4943
DOI
10.2991/978-94-6463-288-0_38How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Vincentius Riandaru Prasetyo
AU  - Mohammad Farid Naufal
AU  - Budiarjo
PY  - 2023
DA  - 2023/11/19
TI  - Implementation of K-Means and K-Nearest Neighbor Methods for Laptop Recommendation Websites
BT  - Proceedings of the 4th International Conference on Informatics, Technology and Engineering 2023 (InCITE 2023)
PB  - Atlantis Press
SP  - 457
EP  - 469
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-288-0_38
DO  - 10.2991/978-94-6463-288-0_38
ID  - Prasetyo2023
ER  -