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All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika
Syaja'ah, Khoerun Nisa
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Astronomy Earth & Planetary Sciences Education Materials Science & Nanotechnology Physics

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Reconstruction of High Resolution Medical Image Using General Regression Neural Network (GRNN) Perkasa, Yudha Satya; Syaja'ah, Khoerun Nisa; Ismadelani, Lyana; Agustina, Rena Denya
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol 10, No 2 (2020)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v10n2.p137-145

Abstract

Low image resolution has deficiencies in the diagnostic process, this will affect the quality of the image in describing an object in certain tissues or organs, especially in the process of examining patients by doctors or physicians based on the results of imaging medical devices such as CT-scans, MRIs and X-rays. Therefore, this study had developed a General Regression Neural Network (GRNN) type artificial neural network system to reconstruct a medical image so that the image has a significant resolution for the analysis process. The GRNN input layer uses grayscale intensity values with variations in the image position coordinates to produce an optimal resolution. There are four layers in this method, the first is input layer, the second is hidden layer, the third is summation, and the last layer is output. We examined the two parameters with different interval values of 0.2 and of 0.5. The result shows that the interval value of 0.2 is the optimal value to produce an output image that is identical to the input image. This is also supported by the results of the intensity curve of the RGB pattern matched between target and output.
Early Study in Automatic Identification of Epilepsy in Neonatal Using EEGLAB and One Dimensional Convolutional Neural Network Through the EEG Signal Nadyah, Izaz; Syaja'ah, Khoerun Nisa; Waryono Sunaryo, Mada Sanjaya
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol. 13 No. 1 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v13n1.p1-15

Abstract

In detecting epileptic activity, medical experts examine the visual result of Electroencephalography signals. The visual analysis will take a lot of time and effort, due to a large amount of data. Furthermore, there are some errors in concluding the analysis result. One of the ways to analyze this quickly is to use Machine Learning (ML) methods. This study aims to evaluate the performance of 1D-CNN in identifying the given data. First, the signal will go through pre-processing using EEGLAB Toolbox which is then classified to identify epilepsy and non-epilepsy with the 1D-CNN algorithm. The results showed that the proposed method obtained high accuracy values, respectively 99,078% for the training data and 82,069% for the validation results. From the evaluation by a confusion matrix, an average accuracy of 99,31% was obtained. Based on this evaluation, the proposed model can be used as an efficient method in the process of automatic classification, detection, or identification of epileptic activity.
Simulasi Produksi Neutron pada LINAC Radioterapi menggunakan metode Monte Carlo FLUKA-FLAIR Ramdani, Ridwan; Sutrisna, Irma Wati; Syaja'ah, Khoerun Nisa; Aliah, Hasniah
Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika Vol 9, No 1 (2023)
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30870/gravity.v9i1.18979

Abstract

The use of high-energy photon beams in radiotherapy aims to increase the effectiveness of the radiation beam so that it can reach tumors that are deeper than the surface of the skin. However, linac aircraft operated above 8 MV can cause photonuclear interactions. Neutrons, which are highly avoided in medical physics, can be generated from the interaction of high-energy photons with materials with high atomic numbers (Z) in linac heads. The study focused on simulating the production of linac 10 MV aircraft contaminant neutrons using Fluka-Flair software based on the Monte Carlo method to find out where the contaminant neutrons come from and their dose contribution to the water phantom. The simulated linac aircraft is a linac head consisting of target components, primary collimator, Flattening filter, ion chamber, Secondary Collimator, and Phantom. The simulation results show that neutrons are generated at the target component, primary collimator, Flattening filter, ion chamber, secondary collimator, and water phantom. Tungsten is the target material with the most excellent 55,08% neutron fluence due to its highest atomic number, Primary Collimator 23,45%, Flattening Filter 10,67%, Ion Chamber 7,58%, Secondary Collimator 3,07% and Phantom 0,15 %.
Severity Classification of Non-Proliferative Diabetic Retinopathy Using Support Vector Machine (SVM) Salamah, Siti; Syaja'ah, Khoerun Nisa; Perkasa, Yudha Satya
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol. 12 No. 2 (2022)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v12n2.p167-179

Abstract

Diabetic Retinopathy (DR) is an eye disease that is the main cause of blindness in developed countries. Treatment of DR and prevention of blindness depend heavily on regular monitoring, early-stage diagnosis, and timely treatment. Vision loss can be effectively prevented by the automated diagnostic system that assists ophthalmologists who otherwise practice manual lesion detection processes which are tedious and time-consuming. Therefore, the purpose of this research is to design a system that can detect the presence of DR and be able to classify it based on its severity. In this proposed, the classification process is carried out based on image discovery by extracting GLCM texture features from 454 retinal fundus images in the IDRID database which are classified into 4 severity levels, namely normal, mild NPDR, moderate NPDR, and severe NPDR. The features obtained from each image will be used as input for the classification process using SVM. As a result, the classification system that has been trained is able to classify 4 levels of DR severity with an average accuracy of 89.55%, a sensitivity of 81.03%, and a specificity of 92.89%. Based on the results of the evaluation of the performance of this classification system, it can be concluded that the specificity value is higher than the specificity value, this indicates that the system that has been trained has a good ability to identify negative samples or those that indicate a class.
Co-Authors Hasniah Aliah Ismadelani, Lyana Nadyah, Izaz Rena Denya Agustina Ridwan Ramdani Siti Salamah Sutrisna, Irma Wati Waryono Sunaryo, Mada Sanjaya Yudha Satya Perkasa, Yudha Satya