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All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA) International Journal of Electronics and Communications Systems
Yudha Satya Perkasa, Yudha Satya
KK Fisika Nuklir Jurusan Fisika Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung
Astronomy Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering 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.
Reconstruction of High Resolution Medical Image Using General Regression Neural Network (GRNN) Yudha Satya Perkasa; Khoerun Nisa Syaja'ah; Lyana Ismadelani; Rena Denya Agustina
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.
Determining the arm's motion angle using inverse kinematics models and adaptive neuro-fuzzy interface system Palupi, Endah Kinarya; Umam, Rofiqul; Junaidi, Rahmad; Perkasa, Yudha Satya; Sanjaya, W. S. Mada
International Journal of Electronics and Communications Systems Vol. 1 No. 1 (2021): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v1i1.9238

Abstract

Robotics technology is known as a great technology demand to be developed continuesly. One of the important things that need to be considered is the control of the motion of the robot. Movement predictions can be modeled in mathematical equations. Prediction based on learning logic is also very supportive of motion control systems, especially arm motion. In this study, the authors combined the two methods as the main study. The working principle of the arm is to take colored objects detected by the camera. In this study, we made arm four DOFs (Degree of Freedom), but only one DOF is controlled by ANFIS because the other three DOFs only move at two fixed angles. Two methods of determining the arm angle of motion used are inverse kinematics and ANFIS methods. The angle of motion and the position of the red object can be observed in real-time on the monitor with the interface in the MATLAB GUI. The angular output that appears in the MATLAB GUI is sent to Arduino in the form of characters, then, Arduino translates it into servo motion to the coordinates of the object detected by the camera. The results showed that the ANFIS method was more effective than the inverse kinematics model.
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 Ismadelani, Lyana Khoerun Nisa Syaja'ah Lyana Ismadelani Mada Sanjaya WS Nisa Syaja'ah, Khoerun Nur Fitriani, Annisa Palupi, Endah Kinarya Rahmad Junaidi, Rahmad Rena Denya Agustina Rena Denya Agustina Rofiqul Umam Ruly Gumilar, Ruly Siti Salamah Subkhi, M. Nurul Syaja'ah, Khoerun Nisa Ummutafiqoh, Tera