Penetration Testing and Vulnerability Analysis of SINTA Platform to Strengthen Privacy and Data Protection

2025-03-03T04:07:30+00:00

The increasing reliance on digital platforms for academic and governmental purposes necessitates robust cybersecurity measures. Consequently, identifying vulnerability is critical to ensuring data security and providing actionable recommendations for cybersecurity officers. Platforms like Sinta (Science and Technology Index), which focus on collecting peer-reviewed papers and maintaining researcher’s research records, represents significant governmental contributions in academia. Cybersecurity awareness is demonstrated through events organized to evaluate the vulnerability of the platform, enabling researchers to access its security and report potential issues. This study addresses these concerns by conducting system penetration testing using the OWASP and Burp Suite Framework, focusing on identifying five critical vulnerabilities. The evaluation examines issues, such as sensitive data exposure in API responses, error log disclosures, email enumeration, and improper access to system files. The results reveal that the platform suffers from multiple levels of security vulnerabilities, prompting recommendations for authorities to take actions to mitigate potential risks effectively.

Employee Face Recognition Using You Only Look Once version 5 (YOLOv5): A Case Study at a Transportation-Based University in Indonesia

2025-03-03T13:11:27+00:00

In 2020, Politeknik Perkerapian Indonesia Madiun (PPI Madiun) faced significant challenges due to the COVID-19 pandemic, which led to the implementation of both Work from Office (WFO) and Work From Home (WFH) policies. To support these policies, PPI Madiun utilized the Electronic Attendance and Remote Assignment Monitoring System (SKEMA RAJA) provided by the Indonesian Ministry of Transportation. However, this system lacked an integrated facial recognition feature to ensure the accuracy of attendance data. Facial recognition technology presents a viable solution to enhance the reliability and efficiency of this attendance system. One effective technology for face recognition is You Only Look Once version 5 (YOLOv5), which has been proven to detect objects with high speed and accuracy. This study aims to develop a face recognition system for PPI Madiun employees using YOLOv5. The results demonstrate that YOLOv5 can detect faces from multiple angles—including right, left, top, bottom, and front—with 100% accuracy under optimal conditions. Additionally, YOLOv5 successfully detects faces in real-time from varying distances (10 cm, 20 cm, and 30 cm), achieving 80% accuracy. The system's performance is influenced by factors such as the angle of capture, lighting conditions, and facial expressions.

Journal of Information Technology and Cyber Security

Penetration Testing and Vulnerability Analysis of SINTA Platform to Strengthen Privacy and Data Protection

Employee Face Recognition Using You Only Look Once version 5 (YOLOv5): A Case Study at a Transportation-Based University in Indonesia