Komparasi Algoritma Machine learning untuk Klasifikasi Kondisi Mental Siswa Berdasarkan Sinyal EEG Wearable
Keywords:
kondisi mental, machine learning, random forest, decision tree, svm, knn, naive bayes
Abstract
The quantifying of the minds of students in objective, real-time and application of this information in education is one of the biggest issues in education in general, and in learning of numbers and mathematics in particular. In this research paper, we are going to compare the performance of five machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, Naive Bayes and random forest in classifying three mental states such as relaxed, neutral and concentrating using EEG signals of a low cost wearable device, the Muse headband. The data had been received in the open dataset EEG Brainwave Dataset: Mental State on Kaggle where they were recorded using four channels (TP9, AF7, AF8, TP10). The signal processing pipeline involves signal pre-processing (band-pass filter), feature extraction (mean, standard deviation, skewness, kurtosis) of both time domain and frequency domain (delta, theta, alpha and beta bandpower) and dimensionality reduction (Principal Component Analysis (PCA)). Accuracy, precision, recall and F1-score were tested on 102 test samples. They found that the most successful one was Random Forest whose accuracy was 0.84 and macro F1-score was 0.84 and SVM (0.82), KNN (0.78), Decision Tree (0.75) and Naive Bayes(0.68) followed. A constant problem in all of the models was the neutral condition because the signal characteristics of the neutral condition lie between those of the other two conditions. These results imply that a set of basic characteristics of a low-cost wearable EEG device and the random forest algorithm may already be sufficient to classify mental states, and has potential as a platform on which to build adaptive, real-time cognitive preparedness monitoring systems,
References
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[2] J. Wang, S. Huo, K. C. Wu, J. Mo, W. L. Wong, and U. Maurer, “Behavioral and neurophysiological aspects of working memory impairment in children with dyslexia,” Sci. Rep., vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-16729-8.
[3] P. Walla, “The Power of Time: Editorial on the Advantages of Electroencephalography (EEG) and Event-Related Potentials (ERPs) in Affective and Cognitive Neuroscience,” Oct. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/brainsci15101054.
[4] P. Walla, “The Power of Time: Editorial on the Advantages of Electroencephalography (EEG) and Event-Related Potentials (ERPs) in Affective and Cognitive Neuroscience,” Oct. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/brainsci15101054.
[5] T. Uudeberg, L. Päeske, H. Hinrikus, J. Lass, T. Põld, and M. Bachmann, “Individual stability of single-channel EEG measures over one year in healthy adults,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-13614-y.
[6] E. Gkintoni and C. Halkiopoulos, “Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective,” Nov. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/biomimetics10110730.
[7] K. T. Do, S. G. Paolizzi, and M. N. Hallquist, “How adolescents learn to build social bonds: A developmental computational account of social explore-exploit decision-making,” Dev. Cogn. Neurosci., vol. 69, Oct. 2024, doi: 10.1016/j.dcn.2024.101415.
[8] E. Lekati et al., “Wearable EEG Sensor Analysis for Cognitive Profiling in Educational Contexts,” Sensors, vol. 25, no. 20, Oct. 2025, doi: 10.3390/s25206446.
[9] E. Lekati et al., “Wearable EEG Sensor Analysis for Cognitive Profiling in Educational Contexts,” Sensors, vol. 25, no. 20, Oct. 2025, doi: 10.3390/s25206446.
[10] S. Kotte, A. Elkhouly, M. F. Abd Malek, and Z. Abohaia, “Deep learning-based EEG mental state classification to support mental focus in female cricketers,” Discover Artificial Intelligence, vol. 5, no. 1, Dec. 2025, doi: 10.1007/s44163-025-00615-z.
[11] H. Xie et al., “EEG analysis of brain dynamics in a simulated multi-task and multi-stage learning environment,” NPJ Sci. Learn., vol. 10, no. 1, Dec. 2025, doi: 10.1038/s41539-025-00376-5.
[12] M. K. Syed, H. Wang, A. A. Siddiqi, S. Qureshi, and M. A. Gouda, “EEG-Based Attention Classification for Enhanced Learning Experience,” Applied Sciences (Switzerland), vol. 15, no. 15, Aug. 2025, doi: 10.3390/app15158668.
[13] S. K. Khare, V. Bajaj, N. B. Gaikwad, and G. R. Sinha, “Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals,” Sensors, vol. 23, no. 18, Sep. 2023, doi: 10.3390/s23187860.
[14] Z. Liu and J. Zhao, “Leveraging deep learning for robust EEG analysis in mental health monitoring,” Frontiers in Neuroinformatics , vol. 18, 2024, doi: 10.3389/fninf.2024.1494970.
[15] M. Zuo, X. Chen, and L. Sui, “Evaluation of Machine Learning Algorithms for Classification of Visual Stimulation-Induced EEG Signals in 2D and 3D VR Videos,” Brain Sci., vol. 15, no. 1, Jan. 2025, doi: 10.3390/brainsci15010075.
[16] E. H. Houssein, A. Hammad, and A. A. Ali, “Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review,” Aug. 01, 2022, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s00521-022-07292-4.
[17] A. Yudhana, R. Umar, and S. Saputra, “Fish Freshness Identification Using Machine Learning: Performance Comparison of k-NN and Naïve Bayes Classifier,” Journal of Computing Science and Engineering, vol. 16, no. 3, pp. 153–164, Sep. 2022, doi: 10.5626/JCSE.2022.16.3.153.
[18] F. J. Ramírez-Arias et al., “Evaluation of Machine Learning Algorithms for Classification of EEG Signals,” Technologies (Basel)., vol. 10, no. 4, Aug. 2022, doi: 10.3390/technologies10040079.
[19] M. Balconi, K. Rovelli, L. Angioletti, and R. A. Allegretta, “Working Memory Workload When Making Complex Decisions: A Behavioral and EEG Study,” Sensors, vol. 24, no. 17, Sep. 2024, doi: 10.3390/s24175754.
[20] J. J. Bird, L. J. Manso, E. P. Ribeiro, A. Ekart, and D. R. Faria, “A Study on Mental State Classification using EEG-based Brain-Machine Interface,” in 2018 International Conference on Intelligent Systems (IS), IEEE, Sep. 2018, pp. 795–800. doi: 10.1109/IS.2018.8710576.
[21] D. Jan et al., “Non-Invasive Classification of Mental Health Disorders Using Resting-State EEG with Dry Electrodes for Scalable Triage,” Cognit. Comput., vol. 17, no. 6, Dec. 2025, doi: 10.1007/s12559-025-10529-8.
[22] Y. Pramitarini, R. H. Y. Perdana, K. Shim, and B. An, “DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with Cell-Free Massive Multiple-Input Multiple-Output †,” Sensors, vol. 23, no. 18, Sep. 2023, doi: 10.3390/s23187960.
[23] H. Yuliansyah, R. Ardiansyah, and A. Yudhana, “A Machine Learning Approach for Multi-Label Classification in Candidate Election Social Media Analysis,” ECTI Transactions on Computer and Information Technology (ECTI-CIT), vol. 20, no. 1, pp. 50–62, Jan. 2026, doi: 10.37936/ecti-cit.2026201.263596.
[24] K. K. M. Rahman and K. M. Nasor, “Classification of Mental State Using a Muse Headband and Machine Learning Algorithm,” in Advances in Signal Processing and Communication Engineering, 2024, pp. 453–462. doi: 10.1007/978-981-97-0562-7_34.
[25] C. Orovas, T. Sapounidis, C. Volioti, and E. Keramopoulos, “EEG in Education: A Scoping Review of Hardware, Software, and Methodological Aspects,” Jan. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s25010182.
[26] Y. Chen, W. Wang, S. Yan, Y. Wang, X. Zheng, and C. Lv, “Application of Electroencephalography Sensors and Artificial Intelligence in Automated Language Teaching,” Sensors, vol. 24, no. 21, Nov. 2024, doi: 10.3390/s24216969.
[27] J. J. Bird, L. J. Manso, E. P. Ribeiro, A. Ekart, and D. R. Faria, “A Study on Mental State Classification using EEG-based Brain-Machine Interface,” in 2018 International Conference on Intelligent Systems (IS), IEEE, Sep. 2018, pp. 795–800. doi: 10.1109/IS.2018.8710576.
[28] D. A. Coman, S. Ionita, and I. Lita, “Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks,” Sensors, vol. 24, no. 11, Jun. 2024, doi: 10.3390/s24113316.
[29] B. Yildirim, O. Ulkir, M. Kaya, A. K. Singh, and S. Krishnan, “Trends in EEG signal feature extraction applications,” 2023. doi: 10.3389/frai.2022.1072801.
[30] I. Alreshidi, I. Moulitsas, and K. W. Jenkins, “Multimodal Approach for Pilot Mental State Detection Based on EEG,” Sensors, vol. 23, no. 17, Sep. 2023, doi: 10.3390/s23177350.
[31] A. Chaddad, Y. Wu, R. Kateb, and A. Bouridane, “Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques,” Jul. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s23146434.
[32] A. Yudhana, A. Muslim, D. E. Wati, I. Puspitasari, A. Azhari, and M. M. Mardhia, “Human emotion recognition based on EEG signal using fast fourier transform and K-Nearest neighbor,” Advances in Science, Technology and Engineering Systems, vol. 5, no. 6, pp. 1082–1088, 2020, doi: 10.25046/aj0506131.
[33] S.-A. Sadegh-Zadeh, “Comparative analysis of dimensionality reduction techniques for EEG-based emotional state classification,” Am. J. Neurodegener. Dis., vol. 13, no. 4, pp. 23–33, 2024, doi: 10.62347/zwry8401.
Published
2026-07-01
How to Cite
Waluyo, A., Yudhana, A., & Yuliansyah, H. (2026). Komparasi Algoritma Machine learning untuk Klasifikasi Kondisi Mental Siswa Berdasarkan Sinyal EEG Wearable. JIIFKOM (Jurnal Ilmiah Informatika Dan Komputer), 5(2), 111-123. https://doi.org/https://doi.org/10.51901/jiifkom.v5i02.743
Section
Articles
