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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control International Journal of Engineering, Science and Information Technology Jurnal INFOTEL
Nurlida Basir
Universiti Sains Islam Malaysia
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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The effect of power spectral density on the electroencephalography of autistic children based on the welch periodogram method Melinda Melinda; I Ketut Agung Enriko; Muhammad Furqan; Muhammad Irhamsyah; Yunidar Yunidar; Nurlida Basir
JURNAL INFOTEL Vol 15 No 1 (2023): February 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i1.874

Abstract

Autism spectrum disorder (ASD) is a serious mental disorder affecting social behavior. Some children also face intellectual delay. In people with ASD, the signals detected have abnormalities compared to normal people. This can be a reference in diagnosing the disorder with electroencephalography (EEG). This study will analyze the effect of Power spectral density (PSD) on the EEG of autistic children and also compare it with the PSD value on the EEG of normal children using the Welch Periodogram method approach. In the preprocessing stage, the Independent Component Analysis (ICA) method will be applied to remove artifacts, and a Finite Impulse Response (FIR) filter to reduce noise in the EEG signal. The study results indicate differences in the PSD values ​​obtained in the autistic and normal EEG signals. The PSD value obtained in the autistic EEG signal is higher than the normal EEG signal in all frequency sub-bands. From the study results, the highest PSD value obtained by the autistic EEG signal is in the delta sub-band, which is 54.06 dB/Hz, while the normal EEG signal is only 33.14 dB/Hz at the same frequency sub-band. And in the Alpha and Beta sub-bands, the normal EEG signal increases the PSD value, while in the autistic EEG signal, the PSD value decreases in the Alpha and Beta sub-bands. In addition, FIR and ICA methods can also reduce noise and artifacts contained in autistic and normal EEG signals.
Performance Analysis of H2O and H2O with HCl Material Image Classification Using Inception V3, VGG19, DenseNet201, and Otsu Segmentation Yunidar Yunidar; Melinda Melinda; Mauliza Putri; Muhammad Irhamsyah; Nurlida Basir; Alfita Khairah
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1253

Abstract

Challenges in classifying signals with fluctuations remain a focus in the field of image and signal processing. Deep learning technology, especially CNN (Convolutional Neural Network), has proven effective for complex visual classification; however, its performance can still be improved, particularly for signal nonlinearity distributions that are not evenly distributed. This study develops a system for classifying signals that exhibit high fluctuations using a merged Otsu segmentation and deep learning ensemble approach with InceptionV3, VGG19, and DenseNet201 models. The methodology employed is a quantitative study based on a deep learning ensemble. H?O and H?O with HCL signal datasets were processed using Otsu segmentation and then extracted using three CNN architectures, which were then combined with the methods of soft voting and stacking. Evaluation is conducted through the analysis of accuracy, precision, recall, loss, and a confusion matrix. DenseNet201 records the highest accuracy of 95%, precision of 0.90, recall of 0.86, and f1-score of 0.95. InceptionV3 achieves equivalent accuracy (95%) but with a recall of 0.83. VGG19 noted an accuracy of 91%, a precision of 0.82, and a recall of 0.78. The ensemble results show improvement in stability classification, especially in class H?O segmentation. However, the classification class HCL segmentation still shows more mistakes. The integration of Otsu segmentation and deep learning ensemble models has been proven effective in increasing the accuracy of classifying signal fluctuations. Segmentation helps highlight the importance of spatial features, while ensemble enhances model generalization. Research furthermore recommended exploring method segmentation and adaptive data augmentation to handle more complex and unbalanced distributions.
Deep Learning–Based ASD Detection from EEG Signals: A Comparison of InceptionTime and XceptionTime Architectures Rachmawati Rachmawati; Arsy Febrina Dewi; Melinda Melinda; Razita Nadhira; Aufa Rafiki; Nurlida Basir
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 4, November 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by persistent social-communication impairments and restricted or repetitive behaviors. While clinical assessment remains the diagnostic gold standard, the demand for objective and scalable screening tools has motivated EEG-based automated classification. However, under controlled experimental settings, the relative contributions of preprocessing choices and deep learning architecture selection to ASD detection performance remain insufficiently quantified. This study systematically benchmarks artifact-aware preprocessing and model architecture by comparing InceptionTime and XceptionTime across four end-to-end processing schemes that isolate the effects of independent component analysis (ICA) and classifier design under identical segmentation and evaluation protocols. A public King Abdulaziz University EEG dataset comprising 16 subjects (8 ASD, 8 controls) was used. Signals were bandpass-filtered using a fourth-order Butterworth filter (0.5–45 Hz), optionally denoised via ICA, and segmented into 4-s windows with 50% overlap. Models were evaluated using 8-fold subject-wise cross-validation. Performance was assessed using accuracy, precision, sensitivity, specificity, and F1-score, and statistical significance was tested with the Wilcoxon signed-rank test. The Butterworth+ICA+InceptionTime pipeline achieved the best results, with a mean accuracy of 0.9886 ± 0.0046 and an F1-score of 0.9879 ± 0.0049. ICA inclusion and architecture choice yielded significant improvements in accuracy (  for both, ). These findings indicate that structured artifact suppression and multi-scale temporal modeling jointly enhance EEG-based ASD classification, supporting their use in robust clinically oriented screening systems.
Co-Authors . Melinda Alfita Khairah Arsy Febrina Dewi Aufa Rafiki I Ketut Agung Enriko Mauliza Putri Muhammad Furqan Muhammad Irhamsyah Muhammad Irhamsyah Rachmawati Rachmawati Razita Nadhira Yunidar Yunidar