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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sistem Cerdas Teknika Algoritme Jurnal Mahasiswa Teknik Informatika MDP Student Conference
Haqiqi, Mokh. Mirza Etnisa
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy 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.
Custom LiDAR Dataset for 3D Object Recognition in Restricted Spaces Using Voxel-RCNN Firman, Firman; Susilawati, Helfy; Setyawan, Arief Suryadi; Haqiqi, Mokh. Mirza Etnisa
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11209

Abstract

Autonomous vehicles play a crucial role in logistics, agriculture, and warehousing, requiring precise object detection and recognition for safe navigation in confined spaces. Traditional 2D sensor-based methods and simple LiDAR applications often struggle with depth perception and classification accuracy, limiting real-time decision-making. This study addresses these challenges by developing a custom LiDAR-based dataset for object recognition within the Voxel-RCNN framework, focusing on six object categories to enhance recognition accuracy. The Voxel-RCNN model was trained on this custom dataset without architectural modifications, assessing its generalization to non-standard data and performance in constrained environments. The training process demonstrated stable convergence, with loss decreasing from 6.09 to 2.37 after 600 epochs. Quantitative evaluations using BEV and 3D Average Precision (AP) revealed strong performance in detecting structured objects like cars (68.14% BEV AP, 55.83% 3D AP in Easy cases) but significant challenges with occluded and irregularly shaped objects such as trees and cyclists. Despite these challenges, the study highlights the potential of Voxel-RCNN for 3D object recognition in autonomous navigation. Future improvements include dataset augmentation, multi-scale feature fusion, and advanced voxelization techniques to enhance recognition performance. These findings contribute to the advancement of LiDAR-based perception systems, supporting safer and more intelligent autonomous vehicle operations.
Restorasi Citra Wajah Terdegradasi Menggunakan Model GAN dan Fungsi Loss Wijaya, Beni; Haqiqi, Mokh. Mirza Etnisa; Satyawan, Arief Suryadi; Susilawati, Helfy
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.11487

Abstract

This study develops a Generative Adversarial Network (GAN)-based model to restore partially degraded facial images by reconstructing missing regions while preserving the structural integrity of the face. The model adopts an encoder-decoder architecture enhanced with skip connections and residual blocks to improve restoration accuracy. The training process utilizes 1,000 paired images, comprising 500 original and 500 occluded facial images, with 200 images allocated for testing. The model was trained over 50 epochs, resulting in a consistent reduction of generator loss from 0.80 to 0.67 and stabilization of discriminator loss at 0.70. Qualitative evaluation indicates the model’s capability to reconstruct facial features such as eyes, nose, and mouth with high visual fidelity, although minor artifacts remain in areas with complex textures. These findings demonstrate the effectiveness of GAN-based approaches in facial image restoration and suggest potential improvements through the exploration of alternative network architectures and more diverse training datasets. The proposed model shows promise for applications in digital forensics and historical image recovery.
Thermal Image-Based Multi-Class Semantic Segmentation for Autonomous Vehicle Navigation in Restricted Environments Fazri, Nurul; Susilawati, Helfy; Haqiqi, Mokh. Mirza Etnisa; Satyawan, Arief Suryadi
Jurnal Sistem Cerdas Vol. 8 No. 1 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i1.489

Abstract

Technological advancements have propelled the development of environmentally friendly transportation, with autonomous vehicles (AVs) and thermal imaging playing pivotal roles in achieving sustainable urban mobility. This study explores the application of the SegNet deep learning architecture for multi-class semantic segmentation of thermal images in constrained environments. The methodology encompasses data acquisition using a thermal camera in urban settings, annotation of 3,001 thermal images across 10 object classes, and rigorous model training with a high-performance system. SegNet demonstrated robust learning capabilities, achieving a training accuracy of 96.7% and a final loss of 0.096 after 120 epochs. Testing results revealed strong performance for distinct objects like motorcycles (F1 score: 0.63) and poles (F1 score: 0.84), but challenges in segmenting complex patterns such as buildings (F1 score: 0.34) and trees (F1 score: 0.42). Visual analysis corroborated these findings, highlighting strengths in segmenting well-defined objects while addressing difficulties in handling variability and elongated structures. Despite these limitations, the study establishes SegNet's potential for thermal image segmentation in AV systems. This research contributes to the advancement of computer vision in autonomous navigation, fostering sustainable and green transportation solutions while emphasizing areas for further refinement to enhance performance in complex environments.
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.
Co-Authors Artemysia, Khaulyca Arva Fazri, Nurul Firman Firman Firman Helfy Susilawati RR. Ella Evrita Hestiandari Samie, Muhammad Ikbal Satyawan, Arief Suryadi Setyawan, Arief Suryadi Shamie, M. Ikbal Sopian, Sani Moch Sopian, Sani Moch. Wiwik Handayani