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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri
Chai, Rifai
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Measuring anxiety level on phobia using electrodermal activity, electrocardiogram and respiratory signals Ain, Khusnul; Rahma, Osmalina Nur; Purwanti, Endah; Varyan, Richa; Ittaqilah, Sayyidul Istighfar; Arfensia, Danny Sanjaya; Sosialita, Tiara Dyah; Qulub, Fitriyatul; Chai, Rifai
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp337-348

Abstract

People with spider phobia experience excessive anxiety reactions when exposed to spiders that will interfere with daily life. Diagnosing and measuring anxiety levels in patients with spider phobia is a complex challenge. Conventional diagnosis requires psychological evaluations and clinical interviews that take time and often result in a high degree of subjectivity. Therefore, there is a need for a more objective and efficient approach to measuring anxiety levels in patients. This study performs anxiety level classification based on electrodermal activity, electrocardiogram (ECG) and respiratory signals using the dataset of Arachnophobia subjects. Each raw data is preprocessed using 24 types of features. Feature performance is processed using the recursive feature elimination method. Data processing was performed in 3 anxiety levels (high, medium, low) and two anxiety levels (high, low) with the support vector machine method and hold-out validation method (7:3). The performance of the model is evaluated by showing the accuracy, precision, recall and F1 score values. The polynomial kernel can perform optimal classification and obtain 100% accuracy in 2 classes and three classes with 100% precision, recall, and F1 score values. This result shows excellent potential in measuring anxiety levels that correlate with mental health issues.
Online PID-neural network for tracking lower limb rehabilitation exoskeleton angular position Hanifah, Ummi; Adinda, Aura; Rahmatillah, Akif; Sapuan, Imam; Ain, Khusnul; Septanto, Harry; Chai, Rifai
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9395

Abstract

Gait trajectory tracking control is an essential component of a lower limb rehabilitation exoskeleton (LLRE). Meanwhile, the proportional-integral-derivative (PID) controller remains popular for a variety of applications, including LLRE. Nonetheless, employing PID presents a significant issue, namely determining how to choose or tune the parameters. This paper addresses the LLRE’s hipknee angular position tracking system based on an online PID-NN controller, i.e., a PID controller, whose parameters are online modified by a trained neural network (NN). A proposed framework for designing the PID-NN controller is elaborated. Numerical verifications are carried out by comparing the performance of the PID-based control system, whose parameters have been tuned using Ziegler-Nichols (ZN), without and using NN. Performance comparisons involving the presence of external disturbance are also carried out. The simulation results show that the proposed PID-NN-based control system provides better performance with lower mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) values.
Peningkatan Produktivitas pada Proses Belajar Mengajar di Ruang Kelas dengan Menggunakan Stimulasi Cahaya dan Suara untuk Meningkatkan Fokus dan Kenyamanan Peserta Ajar Candra, Henry; Setyaningsih, Endah; Pragantha, Jeanny; Chai, Rifai
Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri 2019: Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Prduktivitas proses belajar mengajar di ruang kelas sangat dipengaruhi oleh tingkat fokus dan kenyamanan dari para peserta ajar. Dengan menggunakan stimulasi berupa cahaya dan suara level fokus dari para peserta di dalam ruang kelas dapat ditingkatkan. Stimulasi cahaya dilakukan dengan melakukan penyesuaian iluminasi pencahayaan yang sesuai standar dan pemilihan temperatur warna cahaya. Sedangkan stimulasi suara dilakukan dengan membangkitkan gelombang suara yang memiliki frekuensi yang beresonansi dengan frekuensi gelombang otak. Pada penelitian ini dilakukan investigasi untuk mengetahui pengaruh dari kombinasi stimulasi cahaya dan suara di ruang kelas pada peserta yang sedang mengikuti suatu pelajaran di kelas tersebut. Percobaan dilakukan dengan mempersiapkan suatu ruang kelas yang dilengkapi dengan penstimulasi cahaya dan suara. Pemantauan level fokus dan kenyamanan dari para peserta diukur dengan menggunakan kuesioner, di mana para peserta diminta untuk menilai tingkat kenyamanan dan fokus mereka dengan memilih temperatur warna cahaya yang paling sesuai untuk masing-masing peserta. Beberapa peserta juga direkam pola gelombang otaknya dengan menggunakan electroencephalography. Hasil analisis dari kuesioner dan pola gelombang otak menunjukkan bahwa pengaturan iluminasi dan pemilihan temperatur warna cahaya yang dikombinasikan dengan stimulasi suara dapat meningkatkan level fokus dan kenyamanan dari para peserta ajar.
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.
Co-Authors Adinda, Aura Arfensia, Danny Sanjaya Endah Setyaningsih Fawwaz, Aisy Al Fitriyatul Qulub Henry Candra Imam Sapuan Ittaqilah, Sayyidul Istighfar Ittaqillah, Sayyidul Istighfar Jeanny Pragantha Khusnul Ain Osmalina Nur Rahma Putri, Revita Novianti Rahma, Osmalina Rahmatillah, Akif Septanto, Harry Shane Kurniawan, Angeline Solly Aryza Sosialita, Tiara Dyah Ummi Hanifah Varyan, Richa