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All Journal International Journal of Informatics and Computation Jurnal INFOTEL Jurnal Jaringan Komputer dan Keamanan (JJKK)
Anton Yudhana
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
Restorative Network Forensics for Mitigating GoldenEye DDoS Attacks on OwnCloud Services Using the KUAD Method Hero Wintolo; Petrus Kanisius Dhado; Imam Riadi; Anton Yudhana; Sudaryanto Sudaryanto; Aris Rakhmadi
Jurnal Jaringan Komputer dan Keamanan Vol 7 No 2 (2026): Jurnal Jaringan Komputer dan Keamanan |JJKK| [In progress]
Publisher : Institute of Information Technology and Social Science (IITSS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61346/jjkk.v7i2.380

Abstract

GoldenEye Distributed Denial-of-Service (DDoS) attacks on OwnCloud services can cause increased network traffic, unstable server responses, and disruptions to the availability of private cloud services. This research aims to implement the Knowledge Understanding Assessment Defense (KUAD) method as a restorative network forensics approach to detect, validate, analyze, and mitigate GoldenEye DDoS attacks against OwnCloud services. The research object was an OwnCloud service running on Ubuntu Server 22.04, with Apache2 serving as the web server. The equipment used included a MikroTik CCR1016-12G router, a monitoring computer running Kali Linux 2025.1, and several tools, including Wireshark, Ettercap, Apache access logs, nload, and Iptables. The KUAD method was implemented through the stages of attack detection, evidence acquisition, cross-source data validation, attack-impact analysis, mitigation, and documentation of the incident-handling results. Validation was performed by correlating Wireshark data with access.log records based on IP addresses and timestamps. The results showed that the attacks originated from three IP addresses: 140.213.174.152, 140.213.172.203, and 36.72.217.84. All three addresses sent HTTP GET requests to the /OwnCloud/index.php endpoint, resulting in the HTTP 400 Bad Request status code. All 10 attack records were successfully validated, resulting in a digital evidence validation rate of 100%. The IP address 140.213.172.203 accounted for the largest share of attack activity at 40%, while 140.213.174.152 and 36.72.217.84 each accounted for 30%. The average timestamp difference was 0 seconds, the attack rate reached 0.4 requests per second, and the HTTP 400 status code accounted for 100% of the recorded responses. Following mitigation with iptables, attack activity was reduced by 100%. Overall, the KUAD method effectively supported a network forensics process that was not only investigative but also restorative and quantitatively measurable.
Co-Authors Alwas Muis Aris Rakhmadi Imam Riadi Petrus Kanisius Dhado Sudaryanto Sudaryanto Sunardi Sunardi Wintolo, Hero