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All Journal Indonesian Applied Physics Letters Jurnal Info Sains : Informatika dan Sains Control Systems and Optimization Letters
Ama, Fadli
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Detection of Throat Disorders Based on Thermal Image Using Digital Image Processing Methods Arisgraha, S.T., M.T., Franky Chandra Satria; Rulaningtyas, Riries; Purwanti, Endah; Ama, Fadli
Indonesian Applied Physics Letters Vol. 5 No. 1 (2024): June 2024
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v5i1.57073

Abstract

Throat disorders are often considered trivial for some people, but if they are not treated immediately they can result in more severe conditions and require a longer time to cure this disorder. Objective, safe and comfortable detection of throat disorders is important because throat disorders are an indication of inflammation which, if not treated immediately, can have negative consequences. This research aims to detect throat disorders based on thermal images using digital image processing methods. Image capture was carried out with the same color pallete range on the camera, namely 33°C-38°C. The image obtained is then cropped in the ROI, then the image is threshold with a gray degree of 190. Pixels that have a gray degree above 190 are converted to white, while those below the threshold are converted to black. Next, the percentage of each white and black area is calculated compared to the total ROI area. If the percentage of white area is greater than 38% compared to the area of "‹"‹the throat then it is identified as having a throat disorder, whereas if the percentage of white is less than 38% then it is identified as not having a throat disorder. The detection program created provides an accuracy of 87.5% on sample data of 8 test data.
Design Of A Fiber Optic Sensor-Based Respiration Monitoring System Qulub, Fitriyatul; Alvie Aditya, Shabrina; Ama, Fadli; Pramudita Putra, Alfian
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 03 (2024): Informatika dan Sains , Edition July - September 2024
Publisher : SEAN Institute

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Human breathing rate is an essential marker for assessing one's health, especially regarding respiratory issues. Precise breathing measurements are vital in medicine as they help detect problems early and devise effective treatment plans. The use of fiber optic sensors to monitor breathing offers excellent potential in health monitoring, both medically and independently. Such sensors have advantages such as ease of manufacturing, high sensitivity, compact size, and affordable cost. In this study, a Singlemode-Multimode-Singlemode (SMS) optical fiber-based breathing sensor was designed by fitting it as a belt around the abdomen to measure abdominal breathing. This SMS sensor has variations in multimode length and wavelength used. Tests were conducted in sitting and standing positions, and the results showed the best performance of the SMS sensor at a multimode length of 3.5 cm with an accuracy rate of 99.2525%, linearity of 0.9997, and sensitivity of 2.9725 Hz/dBm. In addition, the standing body position provides 96.5% accuracy with a multimode length of 3.5 cm, while the sitting position provides 96.8% accuracy with a multimode length of 2 cm.
Optimized Photoplethysmography-Based Classification of Calf Muscle Fatigue Using Particle Swarm Optimization with Logistic Regression Perkasa, Sigit Dani; Ama, Fadli; Megantoro, Prisma
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.182

Abstract

This study investigates photoplethysmography (PPG) as a non-invasive, cost-effective alternative for real-time muscle fatigue monitoring, addressing limitations inherent to conventional methods like electromyography (EMG) and blood lactate testing. A PPG-based system was developed to classify fatigued versus non-fatigued states of the calf muscle using a DFRobot SEN0203 sensor at a 1000 Hz sampling rate. The raw PPG signals were segmented into 1-second intervals and processed to compute first and second derivatives—yielding vascular (VPG) and arterial (APG) photoplethysmograms—which enabled extraction of key features including heart rate (HR), heart rate variability (HRV), peak systolic and diastolic voltages, maximum systolic slope (u), minimum diastolic slope (v), and arterial stiffness indicators (b–a and c–a ratios). A Particle Swarm Optimization (PSO) algorithm was employed to optimize both feature selection and hyperparameters within a Logistic Regression (LR) model, achieving perfect classification accuracy (1.0) with training and prediction times of 0.0053 s and 0.0016 s, respectively. Notably, HRV and the minimum diastolic slope—reflecting autonomic regulation and vascular compliance—emerged as the most influential features with weights of 12.3747 and 23.9367. Comparative analyses revealed that although LightGBM matched the PSO-LR accuracy, neural network approaches performed poorly (0.50 accuracy), likely due to overfitting and limited training data. These findings underscore the viability of PPG for muscle fatigue monitoring, with promising applications in sports science, rehabilitation, and occupational health.
Co-Authors Alfian Pramudita Putra Alvie Aditya, Shabrina Fitriyatul Qulub Franky Chandra Satria Arisgraha Perkasa, Sigit Dani Prisma Megantoro Riries Rulaningtyas Solly Aryza