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All Journal ELKHA : Jurnal Teknik Elektro JFA (Jurnal Fisika dan Aplikasinya) Martabe : Jurnal Pengabdian Kepada Masyarakat 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 Arts Humanities 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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Application of Artificial Neural Network Method as A Detection of Blood Fat Disorders in Images of Complete Blood Examination Fortuna, Catharina Natasa Bella; Arisgraha, S.T., M.T., Franky Chandra Satria; Erawati, Puspa
Indonesian Applied Physics Letters Vol. 2 No. 2 (2021): Indonesian Applied Physics Letters - December 2021
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 Arisgraha, S.T., M.T., Franky Chandra Satria; Rulaningtyas, Riries; Kusumawardani, Miranti Ayudya
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): Indonesian Applied Physics Letters - June 2023
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.
Co-Authors Agathon, Daniel Alfian Pramudita Putra Ama, Fadli Amma, Fadli Andiani, Dhiela Huriyah Endah Purwanti Endah Purwanti Erawati, Puspa Fitriyatul Qulub Fortuna, Catharina Natasa Bella Habibullah, Alamah Ulzanati Khusnul Ain Kusumawardani, Miranti Ayudya Kusumo, Wirotomo Bayunoto Prono M. Arief Bustomi Mardhiyah, Ajeng W. Osmalina Nur Rahma PN, M. Rifqi Aditya Pramudita, Alfian Prihartini Widiyanti Pujiyanto Pujiyanto Riries Rulaningtyas Solly Aryza Wardhani, Inten Firdhausi Wijaya, Verlita Wiratama, Dyo Yahya, M. Hafi Yunardi, Riky Tri