Perbandingan Metoda K-NN, Random Forest dan 1D CNN untuk Mengklasifikasi Data EEG Eye State
-
Muhammad Ibnu Choldun Rachmatullah
*
Universitas Logistik dan Bisnis Internasional, Bandung,
Indonesia
- Aryaputra Wicaksono Universitas Logistik dan Bisnis Internasional, Bandung, Indonesia
- Virdiandry Putratama Universitas Logistik dan Bisnis Internasional, Bandung, Indonesia (*) Corresponding Author
Abstract
The use of machine learning / machine learning methods is very important in developing identification of the status of the human eye, especially in terms of processing Electroencephalogram (EEG) signals to identify eye status. In previous research the method used can be a combination method between supervised learning and unsupervised learning, or a single method using supervised learning. In this study, the EEG Eye State classification uses a single method with supervised learning, namely using the following methods: K-nearest neghbors (k-NN), random forest, and 1D Convolutional Neural Networks (1D CNNs). The performance of the three classifier methods is measured using four measures, namely: accuracy, recall, precision, and F1-Score. From the experimental results it was found that the k-NN method has the best performance compared to the other two methods in terms of the four measures used, where the value of each measure is: accuracy = 82.30%; recall=82.30%; precision= 82.36%; and F1-Score=82.30%. K-NN is more suitable for classifying EEG Eye State than the other two methods, because all input attributes are from the dataset. has a data type of real numbers.
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References
W. Mumtaz et al., “Biomedical Signal Processing and Control Electroencephalogram ( EEG ) -based computer-aided technique to diagnose major depressive disorder ( MDD ) ଝ,” Biomed. Signal Process. Control, vol. 31, no. 2017, pp. 108–115, 2020, doi: 10.1016/j.bspc.2016.07.006.
A. Rafiei, R. Zahedifar, and C. Sitaula, “Automated Detection of Major Depressive Disorder With EEG Signals : A Time Series Classification Using Deep Learning,” IEEE Access, vol. 10, no. July, pp. 73804–73817, 2022, doi: 10.1109/ACCESS.2022.3190502.
N. O. R. Safira et al., “Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power,” IEEE Access, vol. 9, pp. 121608–121631, 2021, doi: 10.1109/ACCESS.2021.3109780.
D. Mahmood, H. Nisar, V. V. Yap, and C. Tsai, “The Effect of Music Listening on EEG Functional Connectivity of Brain : A Short-Duration and Long-Duration Study,” Mathematics, vol. 10, 2022.
C. Cooney, A. Korik, and D. Coyle, “Evaluation of Hyperparameter Optimization in,” Sensors, vol. 20, 2020.
F. Laport, P. M. Castro, A. Dapena, F. J. Vazquez-araujo, and D. Iglesia, “Study of Machine Learning Techniques for EEG Eye State Detection †,” Proceedings, vol. 54, no. 53, pp. 53–55, 2020, doi: 10.3390/proceedings2020054053.
J. Zhou, G. Wang, J. Liu, and D. Wu, “Automatic Sleep Stage Classification With Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model,” pp. 57283–57297, 2020.
N. Ahmadi et al., “Eye State Identification Utilizing EEG Signals : A Combined Method Using Self-Organizing Map and Deep Belief Network,” vol. 2022, 2022.
H. Dose, J. S. Muller, H. K. Iversen, and S. Puthusserypady, “An End-to-end Deep Learning Approach to MI-EEG Signal Classification for BCIs,” Expert Syst. Appl., 2018, doi: 10.1016/j.eswa.2018.08.031.
E. Santamaría-vázquez, V. Martínez-cagigal, and S. Pérez-velasco, “Computer Methods and Programs in Biomedicine Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning,” vol. 215, 2022, doi: 10.1016/j.cmpb.2022.106623.
S. Manikandan, “A Deep Learning approach for automatic classification of Cognitive Task using the scalp Electroencephalogram signals A Deep Learning approach for automatic classification of Cognitive Task using the scalp Electroencephalogram signals,” no. February, 2021.
Y. R. Tabar and U. Halici, “A novel deep learning approach for classification of EEG motor imagery signals,” J. Neural Eng., p. 16003, doi: 10.1088/1741-2560/14/1/016003.
I. Kayadibi, G. Emre, G. Uçman, E. Nurgül, and Ö. Süzme, “An Eye State Recognition System Using Transfer Learning : AlexNet ‑ Based Deep Convolutional Neural Network,” Int. J. Comput. Intell. Syst., vol. 2, 2022, doi: 10.1007/s44196-022-00108-2.
A. Naimi, J. Deng, and S. Member, “Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor,” IEEE Access, vol. 10, pp. 17113–17121, 2022.
I. F. Yuliati and P. R. Sihombing, “Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir Rendah di Indonesia Implementation of Machine Learning Method in Risk Classification on Low Birth weight in Indonesia,” Matrik J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 2, pp. 417–426, 2021, doi: 10.30812/matrik.v20i2.1174.
U. Erdiansyah, A. I. Lubis, and K. Erwansyah, “Komparasi Metode K-Nearest Neighbor dan Random Forest Dalam Prediksi Akurasi Klasifikasi Pengobatan Penyakit Kutil,” J. MEDIA Inform. BUDIDARMA, vol. 6, pp. 208–214, 2022, doi: 10.30865/mib.v6i1.3373.
S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, “1D convolutional neural networks and applications : A survey,” Mech. Syst. Signal Process., vol. 151, p. 107398, 2021, doi: 10.1016/j.ymssp.2020.107398.
G. Lu, Y. Wang, H. Yang, and J. Zou, “One-dimensional convolutional neural networks for acoustic waste sorting,” J. Clean. Prod., p. 122393, 2020, doi: 10.1016/j.jclepro.2020.122393.
M. S. T. Putra and Y. Azhar, “Perbandingan Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 6, no. 1, p. 29, 2021, doi: 10.14421/jiska.2021.61-04.
Q. H. Nguyen et al., “Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil,” vol. 2021, 2021.
R. Indransyah et al., “Klasifikasi Sentimen Pergelaran Motogp Di Indonesia Menggunakan Algoritma Correlated Naïve Bayes Clasifier,” pp. 60–66, 2022.
A. Nabillah, S. Alam, and M. G. Resmi, “Twitter User Sentiment Analysis Of TIX ID Applications Using Support Vector Machine Algorithm,” vol. 3, no. 1, pp. 14–27, 2022.
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