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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Applied Physics Letters
Ittaqillah, Sayyidul Istighfar
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Classifying mental workload of esports players using machine learning Fawwaz, Aisy Al; Rahma, Osmalina; Ittaqillah, Sayyidul Istighfar; Shane Kurniawan, Angeline; Putri, Revita Novianti; Varyan, Richa; Adinda, Aura; Ain, Khusnul; Chai, Rifai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp469-480

Abstract

Electrodermal activity (EDA) peak counts, derived from both tonic and phasic components, are widely used as physiological proxies for mental workload in cognitively demanding tasks, such as esports. However, their specificity remains uncertain, particularly given potential confounding effect of time-on-task. This study analyzes 92 competitive gameplay sessions from a multimodal esports dataset using three decomposition techniques: convex decomposition (cvxEDA), sparse deconvolution (sparseEDA), and time varying sympathetic activity (TVSymp). From each method, phasic, and tonic peak counts (TPC), as well as their normalized rates, were extracted. We examined their relationship with self-reported workload through correlation analyses, partial correlations controlling for session duration, and linear mixed-effects models (LMMs). While both peak types exhibited strong positive correlations with gameplay duration (r=0.915 for phasic and r=0.856 for tonic), their association with perceived workload vanished once time was accounted for. Across methods, TVSymp yielded the highest discriminative validity with an area under curve (AUC) of 0.880 in classifying high versus low workload. Machine learning (ML) classifiers trained solely on EDA-based features under a leave-one-subject-out (LOSO) scheme outperformed multimodal models that incorporated heart rate variability (HRV). These results underscore need to disentangle temporal structure from cognitive signals when interpreting EDA and call into question the assumption that EDA peak counts alone reliably encode mental workload across individuals.
Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children Rahma, S.T., M.Si., Osmalina Nur; Harahap, Akila Firdausi; Rahmatillah, Akif; Putra, Alfian Pramudita; Rulaningtyas, Riries; Thifal, Quinolina; Sumanang, Delfina Amarissa; Ittaqillah, Sayyidul Istighfar
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): Indonesian Applied Physics Letters - June 2023
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48153

Abstract

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.
Co-Authors Adinda, Aura Alfian Pramudita Putra Chai, Rifai Fawwaz, Aisy Al Harahap, Akila Firdausi Khusnul Ain Putri, Revita Novianti Rahma, Osmalina Rahma, S.T., M.Si., Osmalina Nur Rahmatillah, Akif Riries Rulaningtyas Shane Kurniawan, Angeline Sumanang, Delfina Amarissa Thifal, Quinolina Varyan, Richa