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All Journal JURNAL FISIKA JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Journal of Soft Computing Exploration Abdi Masya Abdi Teknoyasa Journal of Multiscale Materials Informatics
Diah Rahayu Ningtias, Diah Rahayu
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Other

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Journal : Journal of Soft Computing Exploration

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Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture Rhomadhon, Syahruu Siyammu; Ningtias, Diah Rahayu
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.372

Abstract

According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. The National Cancer Institute reports that there are approximately 4.4 new cases of brain tumors per 100,000 men and women per year. Brain tumors can be detected using magnetic resonance imaging (MRI), a scanning tool that uses a magnetic field and a computer to record brain images and is able to provide clear visualization of differences in soft tissue such as white matter and gray matter. However, this cannot be done optimally because it still relies on manual analysis, so it cannot classify brain tumor types on larger datasets with the potential for error and a low level of accuracy. To accurately determine the type of brain tumor, a better classification method is needed. The aim of this study is to determine the accuracy of brain tumor calcification using the deep learning model. In this study, the classification of brain tumor types was carried out using the ResNet152V2 convolutional neural network (CNN) model which has a depth of 152 layers. The dataset used in this study was 7,023 MRI images of brain tumors consisting of 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary and 2,000 normal. Research results show an accuracy value of 94.44%, so it can be concluded that the ResNet152V2 model performs well in classifying brain tumor images and can be used as a medium for physicians to more accurately diagnose brain tumor patients more accurately.
Utilization of eye tracking technology to control lights at operating room Permana, Asyraf; Ningtias, Diah Rahayu
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.502

Abstract

The development of technology for control systems is increasing, especially to help people with disabilities and facilitate the performance of health workers. Where it is required to maintain the level of sterilization of equipment in hospitals. Eye tracking technology in the last few decades has developed very rapidly. This control system using eye tracking technology can be done with eye movements for those who experience mobility problems. This research aims to develop a light control system through eye activity using the Mediapipe framework from Google. In this study, 2 lamps (A and B) were used, each with a light intensity of 10W. In lamp A, the light intensity can be controlled by turning the light on or off using the blink of the right eye and the blink of the left eye, while lamp B can adjust the intensity of the light by opening both eyes (right and left). Research on a lighting control system using the eye tracking method with an image processing system has been successfully carried out. All data generated is based on activity, distance, eye position on the camera and differences in participant backgrounds. Apart from that, a system that can work well means consistent results are obtained. However, based on distance, the system can read with precision at distances of 50 cm and 60 cm.
Co-Authors Akrom, Muhamad Aulia, Andika Bayu Wahyudi Brahmantya Aji Pramudita Harsoyo, Imam Tri Kusumaningtyas, Pramesti Permana, Asyraf Rhomadhon, Syahruu Siyammu Rofi’i, Mohammad S. Susilo, S. Sutikno Madnasri zulfa, nely