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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Ziani, Said
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Computer Science & IT Electrical & Electronics Engineering

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Combination of Multidistance Signal Level Difference and Time Domain Features for Epileptic Seizure Classification Amalia, Qoriina Dwi; Beu, Donny Setiawan; Rizal, Achmad; Ziani, Said
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2692

Abstract

Epileptic seizures are neurological disorders characterized by abnormal electrical activity in the brain, causing a series of seizures or episodes of temporary loss of consciousness. This research aims to develop a method of detecting and classifying epileptic seizures using one-dimensional EEG signals with the Multidistance Signal Level Difference (MSLD) approach and time domain feature extraction. The goal is to improve accuracy in distinguishing normal, interictal, and ictal conditions in EEG signals. The dataset from Bonn University consists of one-dimensional EEG signals that include normal, interictal, and ictal states. The analysis method includes extracting time domain features from EEG signals, such as Integrated EMG (IEMG), Mean Absolute Value (MAV), and others. The next step is the application of three classification algorithms, namely linear SVM, quadratic SVM, and cubic SVM, to classify the three conditions. Testing is done by measuring the accuracy of the classification results. The results of this study show that by using 14-time domain features and the MSLD approach, the most significant classification accuracy achieved was 98.7%. This result demonstrates the effectiveness of the proposed method in distinguishing normal, interictal, and ictal conditions. This research provides a foundation for further study in developing EEG signal classification analysis models. Future research can expand the scope by considering larger datasets, using more sophisticated feature extraction techniques, and exploring more complex classification algorithms to improve the accuracy and reliability of the model in real-world applications, particularly in the medical field for the diagnosis of epileptic seizures.
Modification of grey-level co-occurrence matrix for epileptic electroencephalogram signal classification Setiawan Beu, Donny; Rizal, Achmad; Ziani, Said; Triwiyanto, Triwiyanto
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9105

Abstract

Texture analysis is a fundamental approach in image processing for identifying specific patterns or structures. One widely used method is the grey-level co-occurrence matrix (GLCM), which computes the frequency of pixel value pairs at certain distances and angles. This study adapts the GLCM method for 1D electroencephalogram (EEG) signal analysis, focusing on extracting features such as contrast, energy, homogeneity, correlation, and entropy. EEG signals are normalized to the range 0–255, and the extracted features are classified using a support vector machine (SVM). Experimental results show that combining features across multiple distances (d=1 to 20) achieves classification accuracy of 78.8% for five classes (Z/O/N/F/S), 94.0% for three classes (O/F/S), and 94.3% for another three-class group (Z/N/F). The method shows strong performance for short to medium distances and fewer class combinations. However, performance declines when dealing with more complex class sets and longer distances, where texture features become less effective. The drop in accuracy for Z/O/N/F/S beyond d=5 underscores the challenges of maintaining feature robustness at extended distances. Despite this, GLCM remains a promising approach for EEG signal classification. Future work should focus on optimizing distance parameters and feature combinations to further enhance classification performance.
Solar panels overheating protection: a review Laabab, Imad; Ziani, Said; Benami, Abdellah
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp49-55

Abstract

Reduced energy consumption is a direct result of building optimization. Solar panels and heat detectors can provide the necessary power. After years of refinement and development, methods for regulating solar panels' output are finally finding their way into everyday life, where they are having a dramatic impact on the foundations of sustainability. This document provides an up-to-date assessment of several strategies for preventing solar panels from overheating, all of which serve to boost their efficiency and prolong their service life. It begins with a brief background on protection technologies before moving on to a survey of recently filed patent applications that address this topic. The study concludes with a proposed assessment of the strengths and shortcomings of these safeguards. We anticipate that the information included in this review paper will be an invaluable tool for scholars and researchers.
Co-Authors Achmad Rizal Achmad Rizal Amalia, Qoriina Dwi Benami, Abdellah Beu, Donny Setiawan Laabab, Imad Setiawan Beu, Donny