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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Rahmaniar, Thalita Dewi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Unlocking Early Detection and Intervention Potential: Analyzing Visual Evoked Potentials (VEPs) in Adolescents/Teenagers with Narcotics Abuse Tendencies from the TelUnisba Neuropsychology EEG Dataset (TUNDA) Wijayanto, Inung; Sulistyo, Tobias Mikha; Nur Pratama, Yohanes Juan; Safitri, Ayu Sekar; Rahmaniar, Thalita Dewi; Sa’idah, Sofia; Hadiyoso, Sugondo; Wibowo, Raiyan Adi; Kurnia Ismanto, Rima Ananda; Putri, Athaliqa Ananda; Khasanah, Andhita Nurul; Diliana, Faizza Haya; Azzahra, Salwa; Gadama, Melsan; Utami, Ayu Tuty
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.476

Abstract

Narcotics abuse has extensive negative impacts on individuals, families, and society, including physical harm to organs and mental health disorders. Addressing teenage narcotics problems requires collaborative efforts involving educational institutions, families, and psychologists. Currently, narcotics has increasingly targeted teenagers, becoming a serious issue that demands special attention in prevention and treatment. Handling narcotic problems at the adolescent level necessitates close collaboration among educational institutions, families, and the community, including psychologists. Emphasizing the importance of early detection and prevention, this study proposes a method to detect the possibility of narcotic abuse in adolescents using the Go/No-Go Association Task (GNAT) test designed by psychologists. The study introduced the TelUnisba Neuropsychology EEG Dataset (TUNDA), an open EEG dataset with data on the emotional and habitual aspects of drug abuse in Indonesia, classified into "normal" and "risk" by psychologists. The processed EEG signal is the visual evoked potential (VEP) within 1000 milliseconds following the visual stimulus onset. The data is classified as “slow” and “fast” based on respondent's responses using MobileNetV2 architecture. Results showed MobileNetV2 achieved the highest accuracy for both normal and risk categories, with accuracies of 0.86 and 0.85 respectively. This study obtained ethical clearance and received funding support from Telkom University and Universitas Islam Bandung, with technical assistance from the Smart Data Sensing Laboratory. The authors declare no conflicts of interest related to this study.
Application of Hybrid Metaheuristic Algorithms for Feature Selection in Event-Related Potential Classification in Problematic Gamers Using Electroencephalograph Signal Wijayanto, Inung; Hadiyoso, Sugondo; Safitri, Ayu Sekar; Rahmaniar, Thalita Dewi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.638

Abstract

Online games have become a popular form of entertainment, particularly for relieving stress, and the rise in online gaming has led to an increase in problematic gaming behaviors. Excessive use of the internet for gaming has raised concerns about its neurophysiological impact, particularly on cognitive and emotional functions. Electroencephalogram signal and Event-Related Potential analysis are valuable tools for monitoring these effects. Given the vast amount of features that can be extracted from EEG signals, it is crucial to apply efficient feature selection methods to identify the most informative ones. This study utilizes the Go/No-Go Association Task combined with the recording of 16-channel EEG signals, chosen as the data-recording method to observe the response of individuals who are problematic online gamers to several stimulus themes. In this context, metaheuristic algorithms like Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization are employed to enhance feature selection. A hybrid approach, combining one of these methods with Binary Stochastic Fractal Search is proposed to improve classification accuracy and optimize feature selection. The results demonstrate that the hybridization of the best algorithm with B-SFS successfully selects the optimal features, achieving perfect classification performance, with an accuracy, sensitivity, and specificity of 1.00 for all respondents. This emphasizes the effectiveness of B-SFS, particularly its diffusion process, where Gaussian distribution facilitates the search for the best solution, thereby improving the reliability of feature selection for detecting problematic gaming behavior.
Co-Authors Andhita Nurul Khasanah Azzahra, Salwa Diliana, Faizza Haya Gadama, Melsan Inung Wijayanto Kurnia Ismanto, Rima Ananda Nur Pratama, Yohanes Juan Putri, Athaliqa Ananda Safitri, Ayu Sekar Sa’idah, Sofia Sugondo Hadiyoso Sulistyo, Tobias Mikha Utami, Ayu Tuty Wibowo, Raiyan Adi