Article Per Year (5 Year)
p-Index From 2021 - 2026
1.048
P-Index
This Author published in this journals
All Journal Indonesian Physical Review Indonesian Applied Physics Letters Jurnal Info Sains : Informatika dan Sains Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Control Systems and Optimization Letters
Ama, Fadli
Unknown Affiliation
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics Public Health Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Indonesian Applied Physics Letters

 1 
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. 2 (2024): Volume 5 No. 2 – December 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.
DEVELOPMENT OF A HEMOGLOBIN LEVEL PREDICTION MODEL BASED ON PHOTOPLETHYSMOGRAPH DATA USING EXTREME GRADIENT BOOSTING Arisgraha, Franky Chandra Satria; Ama, Fadli; Kusumo, Wirotomo Bayunoto Prono
Indonesian Applied Physics Letters Vol. 6 No. 1 (2025): Volume 6 No. 1 – June 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i1.84086

Abstract

Anemia is a growing global health concern, driving the need for non-invasive detection methods. This research develops a non-invasive hemoglobin (Hb) level prediction model utilizing Photoplethysmography (PPG) signals and the Extreme Gradient Boosting (XGBoost) algorithm, addressing the limitations of conventional invasive and time-consuming approaches. PPG signals, captured by optical sensors (red 660 nm and infrared 880 nm) on the fingertip, monitor blood volume changes that correlate with Hb levels based on the Beer-Lambert Law. Following pre-processing of secondary data from 68 subjects (including missing value handling and gender encoding) and average Systolic Peak feature extraction, the XGBoost model was trained and evaluated. To enhance performance and overcome data limitations, data augmentation was implemented, expanding the sample to 204. Evaluation results demonstrate significant improvement: on the original data, the model achieved an MAE 0,0769, RMSE 0,1117, and R² 0,4954. For the post-augmentation, performance drastically improved to an MAE 0,0190, RMSE 0,0254, and the R² of 0,9724. This increased R² indicates the model's ability to capture 97,24%; of hemoglobin variability, while reduced MAE and RMSE signify higher prediction accuracy and better generalization, making this model reliable for non-invasive Hb prescreening and potentially supporting anemia diagnosis.
Co-Authors Agustiyanto, Frans R Alfian Pramudita Putra Alvie Aditya, Shabrina Arifianto, Deny Fitriyatul Qulub Franky Chandra Satria Arisgraha Hatta, Agus Muhamad Katherin Indriawati Kusumo, Wirotomo Bayunoto Prono Nurul Fitriyah Perkasa, Sigit Dani Prisma Megantoro Riries Rulaningtyas Samian Solly Aryza Supadi Supadi Suryani Dyah Astuti Usamah, Shofi Afghania Winarno Winarno