K-Means for Majoring Informatics Students' Interests Based on Brainwave Signals
DOI:
https://doi.org/10.12928/mf.v5i1.6629Keywords:
Brainwave, K-Means, Clustering, Principal Component Analysis, SilhouetteAbstract
This study investigates the potential of utilizing EEG (electroencephalogram) as a determinant for the specialization choices of Informatics students. EEG, measuring brain activity patterns, is employed to discern majors of interest among students. A questionnaire revealed that some students opt for specializations due to class availability and peer influence, leading to potential mismatches between their abilities and interests, consequently affecting their final project or thesis. EEG data from 30 respondents, recorded using NeuroSky Mindwave and MyndPlayer Pro software, were subjected to K-Means Clustering after feature extraction through PCA. However, the evaluation using Silhouette indicated a low score of 0.453, possibly due to significant distance between cluster data and centroids, minimal dataset size, and random respondent selection without considering their specific areas of interest. This suggests limitations in using EEG alone for determining specialization choices, necessitating further refinement and integration with additional factors for more accurate predictions.
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