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All Journal International Journal of Informatics and Computation Jurnal INFOTEL
Sunardi Sunardi
Universitas Ahmad Dahlan, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Klasifikasi Citra Medis Tumor Otak Menggunakan Algoritma Convolutional Neural Network Alwas Muis; Sunardi Sunardi; Anton Yudhana
JURNAL INFOTEL Vol 15 No 3 (2023): August 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i3.964

Abstract

Brain tumor is a disease that is very dangerous for humans where this disease really needs faster and more accurate treatment. This disease requires early detection because it requires fast and accurate medical treatment. Machine learning helps solve problems by leveraging deep learning technology in the branch of machine learning. Deep learning is a technology that can detect, classify, and segment various problems in machine learning. One of the methods used in deep learning is the Convolutional Neural Network. This method is most often used in performing image processing where this method has various types of feature extraction. The purpose of this study was to test the accuracy of using the Convolutional Neural Network method in classifying brain images. The brain image used in this study is an image scanned by Magnetic Resonance Imaging. The dataset in this study was downloaded from the Kaggle website as many as 7023 data consisting of four classes of brain image data, namely glioma, notumor, meningioma, and pituitary classes. The results of this study obtained an accuracy value of 84% so that this research can be used by medical personnel to diagnose brain tumors easily, quickly, precisely, and accurately.
Virtual Reality and Augmented Reality in Sign Language Recognition: A Review of Current Approaches Aris Rakhmadi; Anton Yudhana; Sunardi Sunardi
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.94

Abstract

This paper delivers a comprehensive review of the current approaches to Sign Language Recognition (SLR) using Virtual Reality (VR) and Augmented Reality (AR) technologies. Sign language is essential for communication within the deaf and hard-of-hearing community, and traditional SLR methods have faced several challenges, including limited gesture recognition, lack of context awareness, and scalability. VR and AR, with their immersive and interactive environments, offer promising solutions to overcome these limitations. This review explores how VR and AR can enhance SLR by providing real-time feedback, personalized learning experiences, and more dynamic and engaging systems. It also examines the integration of VR and AR with advanced technologies such as machine learning and computer vision, which have significantly enhanced the accuracy and efficiency of sign language recognition. Despite progress, challenges related to hardware limitations, cultural diversity, and user experience remain. The paper concludes by highlighting future directions, including advancements in AI, increased affordability, and the need for interdisciplinary collaboration to ensure the development of inclusive, scalable, and accessible SLR systems
Co-Authors Alwas Muis Anton Yudhana Aris Rakhmadi