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All Journal ELKHA : Jurnal Teknik Elektro JFA (Jurnal Fisika dan Aplikasinya) Jurnal Pengabdian Magister Pendidikan IPA Indonesian Applied Physics Letters Indonesian Community Journal
Franky Chandra Satria Arisgraha
Biomedical Engineering Study Program, Department Of Physics, Faculty Of Science And Technology, Universitas Airlangga, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Public Health Social Sciences Other

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Journal : Indonesian Applied Physics Letters

 1 
Application of Artificial Neural Network Method as A Detection of Blood Fat Disorders in Images of Complete Blood Examination Catharina Natasa Bella Fortuna; Franky Chandra Satria Arisgraha, S.T., M.T.; Puspa Erawati
Indonesian Applied Physics Letters Vol. 2 No. 2 (2021): December
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v2i2.31510

Abstract

Based on various epidemiological studies, it is stated that blood lipids are the main risk factor for atherosclerosis that leads to coronary heart disease. In patients with blood lipid disorders, red blood cells undergo deformability so that their shape is flatter than normal red blood cells, which are round. The research entitled Application of Artificial Neural Network Method as Detection of Blood Fat Abnormalities in Image of Complete Blood Examination Results was conducted to help facilitate laboratory examinations. This research hopes that it will provide appropriate early detection to support the expert diagnosis. This research consists of two stages. The first stage is digital image processing to obtain area, perimeter, and eccentricity features. These three features will be used as input to the Backpropagation Neural Network program as the second stage. At this stage, blood lipid abnormalities are detected from features that have been obtained from image processing. The accuracy of detecting blood lipid abnormalities with ANN Backpropagation is 85%.
Classification of Pneumonia from Chest X-ray Images Using Keras Module TensorFlow Franky Chandra Satria Arisgraha, S.T., M.T.; Riries Rulaningtyas; Miranti Ayudya Kusumawardani
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48241

Abstract

Pneumonia is a respiratory disease caused by bacteria and viruses that attack the alveoli, causing inflammation of the alveoli. This study aims to examine the ability of the Convolutional Neural Network (CNN) model to classify pneumonia and normal x-ray images. The method used in this research is to construct a CNN model from scratch by compiling layers one by one with the help of the Keras TensorFlow module, which consists of a Convolution layer, MaxPooling layer, Flatten layer, Dropout layer, and Dense layer. Data used in this research is from Guangzhou Women and Children Medical Center, Guangzhou, China. The total data used is 200 images divided into 160 test data, 20 training data, and 20 validation data. From the results of the research conducted, the model has the fastest processing speed of 9.6ms/epoch with a total of 20 epochs. The model has the highest accuracy value of 77% in the training process and an accuracy value of 80% in the testing process. The highest sensitivity value is 1.54 in training and 1.6 in testing. The highest specificity value is 0.77 in training and 0.8 in testing. It can be said that the model can do good classification.
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 Agathon, Daniel Alfian Pramudita Putra Ama, Fadli Andiani, Dhiela Huriyah Catharina Natasa Bella Fortuna Endah Purwanti Fitriyatul Qulub Habibullah, Alamah Ulzanati Kusumo, Wirotomo Bayunoto Prono M. Arief Bustomi Mardhiyah, Ajeng W. Miranti Ayudya Kusumawardani Osmalina Nur Rahma PN, M. Rifqi Aditya Prihartini Widiyanti Pujiyanto Pujiyanto Puspa Erawati Riries Rulaningtyas Solly Aryza Wijaya, Verlita Wiratama, Dyo Yahya, M. Hafi Yunardi, Riky Tri