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All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Intelligent Decision Support System (IDSS) Jurnal Bumigora Information Technology (BITe) Jurnal Mandiri IT International Journal of Enterprise Modelling
Rimbawa, H.A Danang
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Journal : Jurnal Mandiri IT

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Artificial intelligence-based hand gesture recognition for sign language interpretation Rais, M. Fazil; AlFatrah, M. Ilham; Noorta, Chadafa Zulti; Rimbawa, H.A Danang; Atturoybi, Abdurrosyid
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.395

Abstract

This paper presents an artificial intelligence-based system for real-time hand gesture recognition to support sign language interpretation for the deaf and hard-of-hearing community. The proposed system integrates computer vision techniques with deep learning models to accurately identify static hand gestures representing alphabetic signs. The MediaPipe framework is employed to detect and track hand landmarks from live video input, which are then processed and classified using a Convolutional Neural Network (CNN) model. The model is trained on a publicly available BISINDO (Bahasa Isyarat Indonesia) gesture dataset retrieved from Kaggle, comprising 312 images across 26 hand gestures captured under multiple background conditions. Preprocessing includes resizing, grayscale conversion, data augmentation, and landmark extraction with specific innovations in preprocessing techniques, such as the use of advanced data augmentation methods and landmark normalization, which significantly enhance gesture identification accuracy and model robustness. Experimental results show that the system achieves an average classification accuracy of 88.03% and maintains stable performance in real-time applications. Despite these promising results, the system exhibits limitations, including challenges with dynamic gesture recognition, background interference, and limited handling of complex hand movements, all of which can be explored in future research to improve the system’s accuracy and generalization. These findings highlight the system’s potential as an inclusive communication tool to bridge language barriers between deaf individuals and non-signers. This research contributes to the development of accessible assistive technologies by demonstrating a non-intrusive, vision-based approach to sign language interpretation. Future development may involve dynamic gesture translation, sentence-level recognition, and deployment on mobile platforms.
Design and development of an IoT-based archive room security system integrating RFID and fingerprint authentication for military document protection Tidar, R Haryo; Madramsyah, Adam; Rimbawa, H.A Danang; Sembali, Tryas Putranto
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.440

Abstract

The objective of this research is to design and implement a secure, IoT-based dual-authentication system for protecting classified military archive rooms, in response to the growing urgency of safeguarding sensitive documents against real threats such as espionage, unauthorized access, and data tampering. Military archives store critical information essential for national defense operations, yet many facilities continue to rely on outdated physical security systems vulnerable to intrusion and lacking auditability. This research presents the design and implementation of a dual-authentication archive security system based on Internet of Things (IoT), integrating Radio Frequency Identification (RFID) and fingerprint biometrics. The system is developed using the Waterfall model, involving sequential stages of requirement analysis, system design, implementation, testing, and evaluation. The NodeMCU ESP32 microcontroller serves as the central controller, enabling real-time data transmission via Wi-Fi and notification delivery through the Telegram API. The RFID module performs initial identification, while the fingerprint sensor confirms biometric authentication. A solenoid lock mechanism provides physical access control, activated only upon successful dual verification. System testing under simulated military archive conditions yielded an average response time of 4.59 seconds and an authentication accuracy of 90.6%. Additionally, the real-time notification feature enhanced situational awareness by informing administrators of all access events—both valid and unauthorized. The results indicate that combining RFID and fingerprint authentication significantly improves system security, auditability, and operational efficiency compared to single-factor or conventional methods. This system demonstrates the potential for scalable, adaptable application in high-security institutional environments. Future development may include integration of backup power supplies, encrypted communication protocols, and expansion toward a more comprehensive digital security architecture. This research contributes to the advancement of smart security systems in military infrastructure, promoting proactive threat mitigation and enhanced document protection.
Co-Authors Abdul Jabar, Jaka Abdurrachman, Fauzi Alam, Sirojul AlFatrah, M. Ilham Anton Nugroho Aritonang, Sovian Atturoybi, Abdurrosyid Madramsyah, Adam Noorta, Chadafa Zulti Rais, M. Fazil Riswantoro, Dadan Shavkat Saputro, Joko W. Sembali, Tryas Putranto Setyowati, Danny Suharjo, Bambang Teddy Mantoro Tidar, R Haryo