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All Journal Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Innovation Multidisipliner Research
Rahmat Fuadi Syam
Universitas Pancasakti Makassar
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Spatio-Temporal Graph Neural Network Based on Nonlinear Time–Frequency Features for Mu-ERD Classification in Multi-Session EEG Motor Imagery Firman Aziz; Jeffry Jeffry; Syahrul Usman; Rahmat Fuadi Syam; Muhammad Nur Arafah; Nurul Fathanah Mustamin
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8679

Abstract

Mu rhythm event-related desynchronization (ERD) is a key indicator of motor imagery activity based on EEG signals. However, accurate classification of ERD remains challenging due to the nonlinear nature of EEG signals and inter-session variability. This study proposes a motor imagery classification approach using a Spatio-Temporal Graph Neural Network (ST-GNN) model that leverages nonlinear time-frequency features extracted via Variational Mode Decomposition (VMD) and Synchrosqueezing Transform (SST). The dataset was collected from a single healthy subject across five separate sessions, each consisting of two conditions: relaxation and motor imagery. After preprocessing and segmentation, features were extracted and represented as spatio-temporal graphs to be processed by the ST-GNN. The model was evaluated using metrics such as accuracy, F1-score, AUC-ROC, and the Session Stability Index (SSI). The results show that the ST-GNN achieved an accuracy of 94.2%, F1-score of 94.1%, and AUC-ROC of 96.1%, along with high prediction stability across sessions. This performance outperformed baseline models including CNN, CSP+SVM, and STFT+MLP.These findings support the hypothesis that ERD is a distributed brain network phenomenon and demonstrate that the ST-GNN approach with VMD/SST-derived features is a promising strategy for developing adaptive and accurate BCI systems.
Machine Learning, Supervised Leveraging Supervised Machine Learning to Improve Accuracy in Predicting Smartphone Addiction Muhammad Nur Arafah; Imran Iskandar; Rahmat Fuadi Syam; Fitri Rahmadani; Mario Dendo; Serpasius Dappa Sudda
Indonesian Journal of Innovation Multidisipliner Research Vol. 4 No. 2 (2026): April - Juni
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v4i2.473

Abstract

The transformation of smartphones into essential life companions triggers new challenges in the form of digital addiction that erodes productivity and cognitive focus. In contrast to previous studies that focused on screen duration, this study explored a more critical dimension, namely the degradation of productive hours. The research aims to improve the prediction of addiction using supervised machine learning by integrating productivity variables as a core diagnostic feature. Through a database of 7,500 respondents, a comparative analysis was carried out on the Random Forest, K-Nearest Neighbors (KNN), and Naïve Bayes algorithms in The results of the study proved that the productivity variable significantly increased the predictability of the model. Random Forest consistently outperformed other algorithms across scenarios, with the highest accuracy reaching 93.32% at a 70:30 data ratio, surpassing KNN (89.66%) and Naïve Bayes (87.96%). With a specificity of 92.70%, these findings reveal that workflow disruption has a higher differentiating power than conventional duration metrics. In conclusion, productivity interference is a key indicator in identifying digital behavioral disorders. In practical terms, this research provides guidance for developers and managers in designing precise early warning systems to restore daily efficiency amid the distractions of the digital world.
Co-Authors Arafah, Muhammad Nur Firman Aziz Fitri Rahmadani Iskandar, Imran Jeffry Jeffry Mario Dendo Nurul Fathanah Mustamin Serpasius Dappa Sudda Syahrul Usman