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All Journal Jurnal Informatika EDUMATIC: Jurnal Pendidikan Informatika
Sobari, Bahar
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Computer Science & IT Education

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Monitoring dan Pencegahan Serangan Judi Online (Slot Gacor) pada Website Alim, Endi Sjaiful; Nuroji, Nuroji; Rizkiawan, M. Asep; Anhari, Tirta; Sobari, Bahar
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25267

Abstract

A website is a complex tool for presenting information. The security of a website is very important to support the reputation of the website. The rise of online gambling has now penetrated the website, the most dangerous is when the website has been infiltrated with an online gambling page. The purpose of the research is to prevent and monitor the website against online gambling hacker attacks. This research uses a descriptive method approach, namely by monitoring and observing the website, we try to provide an in-depth explanation of monitoring and preventing online gambling attacks (Slot Gacor) by utilizing Cloudflare services. Cloudflare is implemented as a security system on the website. In this research, the website that became a case study was the bpti.uhamka.ac.id website with the research location in the UHAMKA data center room. Data collection techniques and data analysis are carried out by direct observation through the cloudflare website. The data analyzed in the form of graphs and money url paths have been blocked. Research results Within 8 days there were 126 url paths that could be blocked by cloudflare and all of them contained online gambling with most of them being gacor slots. Cloudflare has successfully blocked hackers by filtering path URLs from the originating website.
Pose Analysis and Classification in Shooting Sport Using Convolutional Neural Network and Long Short-Term Memory Sobari, Bahar; Moedjiono, Moedjiono; Rizkiawan, M. Asep
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25566

Abstract

Shooting sport requires high accuracy and speed, making training evaluation essential for athlete performance improvement. Conventional evaluation methods are often limited, thus the application of Artificial Intelligence (AI) and Computer Vision provides an effective alternative. This research aims to analyze and classify shooting sport poses using Deep Learning methods. A dataset consisting of several thousand pose images was collected from both field recordings and publicly available sources, followed by preprocessing for coordinate extraction. Convolutional Neural Network (CNN) was employed to extract coordinate data from shooting pose images, while Long Short-Term Memory (LSTM) was applied for pose classification. Experimental results demonstrated 94% accuracy, 95% Percentage of Correct Keypoints (PCK), and 4 mm Mean Per Joint Position Error (MPJPE), with training conducted at a learning rate of 0.0001 over 150 epochs on 5% test data, involving a total of 596,642 parameters. These results indicate that the proposed CNN–LSTM model provides a reliable approach for pose analysis and classification in shooting sport. The contribution of this study lies in presenting a novel dataset and framework for AI-based shooting sport evaluation, which can enhance training feedback and broaden AI applications in sports. 
Co-Authors Alim, Endi Sjaiful Anhari, Tirta M. Asep Rizkiawan Moedjiono Moedjiono Nuroji Nuroji