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All Journal Journal of Physics and Its Applications Jurnal Listrik, Instrumentasi, dan Elektronika Terapan
Darma, Panji Nursetia
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Astronomy Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics

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Classification of CT Scan Images of Stroke Patients and Normal Brain Based on Histogram, GLCM, and GLRLM Texture Features using K-Nearest Neighbor Azizah, Fitria Kholbi; Putri, Diana Salsabila; Permana, Riyan; Sumarti, Heni; Darma, Panji Nursetia
Journal of Physics and Its Applications Vol 7, No 4 (2025): November 2025
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v7i4.27259

Abstract

Stroke is a major neurological disorder requiring rapid and accurate diagnosis for effective treatment. Computerized Tomography (CT) scanning provides detailed brain imaging but requires expert interpretation. This study aims to develop an automated classification system to distinguish between normal and stroke-affected brain CT scan images using texture feature analysis, providing enhanced accuracy and robustness compared to existing single-feature approaches. A total of 200 CT scan images (100 normal, 100 stroke cases) from the Kaggle database were analyzed. Texture features were extracted using Histogram, Gray Level Co-occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRLM) analysis. The KNN algorithm was evaluated using percentage split validation, with the training set ranging from 50% to 70% of the data. The KNN classifier achieved optimal performance with 93% accuracy, 91% precision, and 96% recall using a 50% training set, demonstrating its potential as a diagnostic support tool for healthcare professionals to facilitate faster diagnosis and treatment decisions. The integration of multiple texture analysis methods showed superior performance compared to individual feature extraction techniques. Histogram features contributed significantly to classification accuracy by enhancing the detection of tissue heterogeneity. Texture analysis revealed significant differences between normal and stroke images in entropy, contrast, and correlation parameters. The proposed method successfully classifies CT scan images of normal and stroke-affected brains with high accuracy, demonstrating potential for clinical implementation in automated stroke screening and diagnostic support.
Exoskeleton Intervention in Car Assembly Line to Minimize the Potential of Musculoskeletal Disorder Risk Chedana, Saskara B'tari; Redi, Anak Agung Ngurah Perwira; Darma, Panji Nursetia
Jurnal Listrik, Instrumentasi, dan Elektronika Terapan Vol 7, No 1 (2026)
Publisher : Departemen Teknik Elektro dan Informatika Sekolah Vokasi UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/juliet.v7i1.113187

Abstract

Musculoskeletal Disorders (MSDs) remain one of the most prevalent occupational health issues worldwide, commonly caused by repetitive motions, awkward postures, and excessive physical load. In Indonesia, more than 40% of workers from various industries report MSD symptoms. Although ergonomic control measures aligned with international standards (e.g. ISO) have been implemented, these strategies are often insufficient to ensure safer postures sustained during work activities. Exoskeletons have been developed as a promising supplemental ergonomic intervention through its biomechanical mechanisms. This study examined the effectiveness of a passive shoulder-support exoskeleton adoption in one of the car assembly activities. Ten young adults performed repetitive bolt-tightening under two conditions: with and without exoskeleton assistance. MSD symptoms were assessed using the Borg CR-10 exertion scale, while postural risk was evaluated using the Rapid Upper Limb Assessment (RULA). Additionally, task completion and recovery times were recorded to evaluate task efficiency. Subjective perception regarding acceptability and safety perception towards the device were assessed through a questionnaire. Based on the experimental results, the exoskeleton reduced perceived muscle exertion mainly in the wrist region and reduced task completion time by 13.6%. A notable reduction in post-task recovery time (19.08%) was also observed under the exoskeleton condition. The overall RULA score decreased from 7 (high risk) to 5.5 (medium risk), indicating a reduction in ergonomic risk. Furthermore, questionnaire responses revealed positive perceptions related to task speed and mobility support, although some usability challenges were noted. Overall, these findings suggest that passive shoulder-support exoskeletons have the potential to enhance worker safety, comfort, and task efficiency in repetitive elevated automotive assembly tasks.
Co-Authors Anak Agung Ngurah Perwira Redi Azizah, Fitria Kholbi Chedana, Saskara B'tari Permana, Riyan Putri, Diana Salsabila Sumarti, Heni