K-Nearest Neighbor with K-Fold Cross Validation and Analytic Hierarchy Process on Data Classification
DOI:
https://doi.org/10.25008/ijadis.v2i1.1204Keywords:
k-Nearest Neighbor, k-Fold Cross Validation, Analytic Hierarchy Process, Machine learning, ClassificationAbstract
This study analyzes the performance of the k-Nearest Neighbor method with the k-Fold Cross Validation algorithm as an evaluation model and the Analytic Hierarchy Process method as feature selection for the data classification process in order to obtain the best level of accuracy and machine learning model. The best test results are in fold-3, which is getting an accuracy rate of 95%. Evaluation of the k-Nearest Neighbor model with k-Fold Cross Validation can get a good machine learning model and the Analytic Hierarchy Process as a feature selection also gets optimal results and can reduce the performance of the k-Nearest Neighbor method because it only uses features that have been selected based on the level of importance for decision making.
Downloads
References
W.M. Lee, "Python® Machine Learning", Publish by John Wiley & Sons, Inc. ISBN: 978-1-119-54563-7, pp. 1-296, 2019.
D. Pan, Z. Zhao, L. Zhang, & C. Tang, "Recursive Clustering K-Nearest Neighbors Algorithm and the Application in the Classification of Power Quality Disturbance". IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1-5, 2017. https://doi.org/10.1109/EI2.2017.8245652
H. Jaafar, N. Mukahar, & D.A. Ramli, "Methodology of Nearest Neighbor: Design and Comparison of Biometric Image Database", IEEE Student Conference on Research and Development (SCOReD), pp. 1-6, 2016. https://doi.org/10.1109/SCORED.2016.7810073
S. Du & J. Li, "Parallel Processing of Improved KNN Text Classification Algorithm Based on Hadoop", IEEE 7th International Conference on Information, Communication and Networks, pp. 167-170, 2019. https://doi.org/10.1109/ICICN.2019.8834973
G. Li & J. Zhang, "Music Personalized Recommendation System Based On Improved KNN Algorithm", IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 777-781, 2018. https://doi.org/10.1109/IAEAC.2018.8577483
A.B. Adege & H. Lin, "Indoor Localization using K-Nearest Neighbor and Artificial Neural Network Backpropagation Algorithms", IEEE 27th Wireless and Optical Communications Conference (WOCC), pp. 1-2, 2018.
Z. Ren, Z. Xu, & H. Wang, "The Strategy Selection Problem on Artificial Intelligence with An Integrated VIKOR and AHP Method under Probabilistic Dual Hesitant Fuzzy Information", IEEE International Journal, pp. 1-23, 2017.
P. Tamilarasi & U. Rani, "Diagnosis of Crime Rate Against Women using k-Fold Cross Validation through Machine Learning Algorithms", IEEE 4th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1034-1038, 2020. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000193
M.R. Wayahdi, D. Syahputra, & S.H.N. Ginting, "Evaluation of k-Nearest Neighbor Model with k-Fold Cross Validation on Image Classification", Journal INFOKUM, Vol. 9, No. 1, ISSN: 2302-9706, pp. 1-6, 2020.
D.R.S. Caon, A. Amehraye, J. Razik, G. Chollet, R.V. Andreao, & C. Mokbel, "Experiments on Acoustic Model Supervised Adaptation and Evaluation by k-Fold Cross Validation Technique", IEEE International Journal, pp. 1-4, 2010. https://doi.org/10.1109/ISVC.2010.5656264
H. Mudia, "Back Propagation Neural Network for Controlling Coupled Water Tank", Bulletin of Comp. Sci. & Electr. Eng., vol. 1, no. 1, pp. 12–18, Jun. 2020. https://doi.org/10.25008/bcsee.v1i1.4
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Zoelkarnain Rinanda Tembusai, Herman Mawengkang, Muhammad Zarlis
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
