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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Teknika Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Artemysia, Khaulyca Arva
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Enhancing Image Quality in Facial Recognition Systems with GAN-Based Reconstruction Techniques Wijaya, Beni; Satyawan, Arief Suryadi; Haqiqi, Mokh. Mirza Etnisa; Susilawati, Helfy; Artemysia, Khaulyca Arva; Sopian, Sani Moch.; Shamie, M. Ikbal; Firman
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1180

Abstract

Facial recognition systems are pivotal in modern applications such as security, healthcare, and public services, where accurate identification is crucial. However, environmental factors, transmission errors, or deliberate obfuscations often degrade facial image quality, leading to misidentification and service disruptions. This study employs Generative Adversarial Networks (GANs) to address these challenges by reconstructing corrupted or occluded facial images with high fidelity. The proposed methodology integrates advanced GAN architectures, multi-scale feature extraction, and contextual loss functions to enhance reconstruction quality. Six experimental modifications to the GAN model were implemented, incorporating additional residual blocks, enhanced loss functions combining adversarial, perceptual, and reconstruction losses, and skip connections for improved spatial consistency. Extensive testing was conducted using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to quantify reconstruction quality, alongside face detection validation using SFace. The final model achieved an average PSNR of 26.93 and an average SSIM of 0.90, with confidence levels exceeding 0.55 in face detection tests, demonstrating its ability to preserve identity and structural integrity under challenging conditions, including occlusion and noise.  The results highlight that advanced GAN-based methods effectively restore degraded facial images, ensuring accurate face detection and robust identity preservation. This research provides a significant contribution to facial image processing, offering practical solutions for applications requiring high-quality image reconstruction and reliable facial recognition.
Optimizing Autonomous Navigation: Advances in LiDAR-based Object Recognition with Modified Voxel-RCNN Firman; Satyawan, Arief Suryadi; Susilawati, Helfy; Haqiqi, Mokh. Mirza Etnisa; Artemysia, Khaulyca Arva; Sopian, Sani Moch; Wijaya, Beni; Samie, Muhammad Ikbal
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2199

Abstract

This study aimed to enhance the object recognition capabilities of autonomous vehicles in constrained and dynamic environments. By integrating Light Detection and Ranging (LiDAR) technology with a modified Voxel-RCNN framework, the system detected and classified six object classes: human, wall, car, cyclist, tree, and cart. This integration improved the safety and reliability of autonomous navigation. The methodology included the preparation of a point cloud dataset, conversion into the KITTI format for compatibility with the Voxel-RCNN pipeline, and comprehensive model training. The framework was evaluated using metrics such as precision, recall, F1-score, and mean average precision (mAP). Modifications to the Voxel-RCNN framework were introduced to improve classification accuracy, addressing challenges encountered in complex navigation scenarios. Experimental results demonstrated the robustness of the proposed modifications. Modification 2 consistently outperformed the baseline, with 3D detection scores for the car class in hard scenarios increasing from 4.39 to 10.31. Modification 3 achieved the lowest training loss of 1.68 after 600 epochs, indicating significant improvements in model optimization. However, variability in the real-world performance of Modification 3 highlighted the need for balancing optimized training with practical applicability. Overall, the study found that the training loss decreased up to 29.1% and achieved substantial improvements in detection accuracy under challenging conditions. These findings underscored the potential of the proposed system to advance the safety and intelligence of autonomous vehicles, providing a solid foundation for future research in autonomous navigation and object recognition.
Implementation of a Computer Network Security System Using the Random Port Knocking Method on the Linux Operating System at Insan Prima Mandiri Vocational School Nurpalaha, Rifki; Zaelania, Moch Zenal; Artemysia, Khaulyca Arva; FahruRoji, Fikri
Journal of Ocean, Mechanical and Aerospace -science and engineering- Vol 69 No 3 (2025): Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Publisher : International Society of Ocean, Mechanical and Aerospace -scientists and engineers- (ISOMAse)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36842/jomase.v69i3.556

Abstract

Technological advancements encourage schools to utilize computer networks for learning, making information security crucial. The purpose of this research is the implementation of a computer network security system using the random port knocking method on the linux operating system at Insan Prima Mandiri Vocational School. SMK Insan Prima Mandiri faces the risk of cyber attacks on local and wireless networks. To address this, a server security system was built using a honeypot, port knocking, and iptables. Tests were conducted before and after the system was implemented using port scanning and brute-force attacks. Results showed that before the system was implemented, the SSH (Secure Shell) port was easily accessible. After implementation, the server was able to detect and block attacks, redirect access to the honeypot, and send notifications to the admin via Telegram.
Design and Implementation of Arduino-Based NRF Remote Control Steering System for Hand Tractors to Enhance Agricultural Efficiency Rukmana, Ade; Firmansyah, Wasep; Oktafiani, Folin; Artemysia, Khaulyca Arva; Nurpadilah, Sifa
Journal of Ocean, Mechanical and Aerospace -science and engineering- Vol 69 No 3 (2025): Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Publisher : International Society of Ocean, Mechanical and Aerospace -scientists and engineers- (ISOMAse)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36842/jomase.v69i3.551

Abstract

Hand tractors are crucial for small-scale farming in Indonesia, but prolonged manual operation leads to operator fatigue, reduced productivity, and safety risks. This study develops a wireless steering control system for a hand tractor using an Arduino microcontroller and NRF24L01 transceiver. The system features a handheld transmitter with joystick input and a tractor-mounted receiver that controls the throttle via a servo motor and steering/clutch mechanisms through solenoids. The design process included system architecture, hardware and power circuit development, software programming, prototype assembly, and testing. Results show that the system operates optimally within 1–2 meters and remains functional up to 8 meters, though with communication degradation (increased delay, packet loss, and reduced actuator accuracy). Key constraints identified include unstable power supply, NRF24L01 limitations, and inadequate solenoid drivers. Recommendations for improvement include using NRF24L01+ PA/LNA modules, separating power rails, and adopting MOSFET-based drivers to enhance reliability and safety.
Co-Authors FahruRoji, Fikri Firman Firmansyah, Wasep Folin Oktafiani, Folin Haqiqi, Mokh. Mirza Etnisa Helfy Susilawati Nurpadilah, Sifa Nurpalaha, Rifki RR. Ella Evrita Hestiandari Rukmana, Ade Samie, Muhammad Ikbal Satyawan, Arief Suryadi Shamie, M. Ikbal Sopian, Sani Moch Sopian, Sani Moch. Wiwik Handayani Zaelania, Moch Zenal