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All Journal Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Control Systems and Optimization Letters
Wulandari, Annastasya Nabila Elsa
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health

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Understanding Generative Adversarial Networks (GANs): A Review Purwono, Purwono; Wulandari, Annastasya Nabila Elsa; Ma'arif, Alfian; Salah, Wael A.
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.170

Abstract

Generative Adversarial Networks (GANs) is an important breakthrough in artificial intelligence that uses two neural networks, a generator and a discriminator, that work in an adversarial framework. The generator generates synthetic data, while the discriminator evaluates the authenticity of the data. This dynamic interaction forms a minimax game that produces high-quality synthetic data. Since its introduction in 2014 by Ian Goodfellow, GAN has evolved through various innovative architectures, including Vanilla GAN, Conditional GAN (cGAN), Deep Convolutional GAN (DCGAN), CycleGAN, StyleGAN, Wasserstein GAN (WGAN), and BigGAN. Each of these architectures presents a novel approach to address technical challenges such as training stability, data diversification, and result quality. GANs have been widely applied in various sectors. In healthcare, GANs are used to generate synthetic medical images that support diagnostic development without violating patient privacy. In the media and entertainment industry, GANs facilitate the enhancement of image and video resolution, as well as the creation of realistic content. However, the development of GANs faces challenges such as mode collapse, training instability, and inadequate quality evaluation. In addition to technical challenges, GANs raise ethical issues, such as the misuse of the technology for deepfake creation. Legal regulations, detection tools, and public education are important mitigation measures. Future trends suggest that GANs will be increasingly used in text-to-image synthesis, realistic video generation, and integration with multimodal systems to support cross-disciplinary innovation.
Perancangan Sistem Monitoring Tukang Parkir Liar secara Real-Time Menggunakan YOLOv11 Ashari, Imam Ahmad; Hidayat, Rachman; Wulandari, Annastasya Nabila Elsa; Arkananta, Edgina Rangga; Trivilia, Indah
Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat 2025 Prosiding Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat (SNPPKM 2025)
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/snppkm.v4i1.1391

Abstract

The widespread presence of illegal parking attendants in various urban areas has caused serious problems in terms of traffic order, public safety, and potential economic losses. Illegal parking attendants often create traffic congestion, increase the risk of criminal activity, and cause inconvenience for road users. This situation demands a technology-based solution capable of performing monitoring and detection quickly and accurately. This study proposes the design of a real-time monitoring system for illegal parking attendants by utilizing You Only Look Once (YOLOv11). The YOLOv11 algorithm was selected due to its ability to detect objects with high accuracy and optimal processing speed. The system is designed with the integration of Closed-Circuit Television (CCTV) cameras directly connected to the YOLOv11 detection model, enabling automatic surveillance of public areas. The detection results can be processed and displayed in real time, facilitating authorities in taking appropriate actions. With this system, it is expected that the supervision of illegal parking activities can be carried out more efficiently, while also providing a significant contribution to improving security, order, and comfort in urban public spaces.
Diskusi dan Demonstrasi Sistem Monitoring Tukang Parkir Liar Berbasis Computer Vision bersama Dinas Perhubungan dan Koordinator Parkir Kabupaten Banyumas Ashari, Imam Ahmad; Hidayat, Rachman; Wulandari, Annastasya Nabila Elsa; Arkananta, Edgina Rangga; Trivilia, Indah
Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat 2025 Prosiding Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat (SNPPKM 2025)
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/snppkm.v4i1.1392

Abstract

The issue of illegal parking in Banyumas Regency has caused negative impacts such as traffic congestion, road user inconvenience, and disruption of traffic order. To support enforcement efforts, a technology-based solution capable of real-time monitoring is required. This community service activity aims to introduce and discuss an illegal parking monitoring system based on Computer Vision in collaboration with the Department of Transportation and regional parking coordinators in Banyumas. The implementation method includes system concept presentations, technology demonstrations, and discussion forums to gather input related to technical needs and field policy considerations. The results of the activity indicate interest from the Department of Transportation and parking coordinators in utilizing this technology, particularly in supporting the effectiveness of supervision and the enforcement of parking regulations. This activity is expected to serve as an initial step toward collaboration between academia and local government in applying smart technology to improve order, safety, and convenience in Banyumas Regency.
Artificial Intelligence in Biomedical Psychology: A Systematic Review of Clinical and Cognitive Applications Wulandari, Annastasya Nabila Elsa; Agung Budi Prasetio; Baballe, Muhammad Ahmad; Taraknath Paul
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2214

Abstract

Biomedical psychology emphasises psychological and neurocognitive assessment through the integration of biological, neurophysiological, and quantitative behavioural data to support clinical decision-making. However, conventional assessment approaches remain limited by issues of objectivity, scalability, and longitudinal monitoring, prompting the utilisation of artificial intelligence (AI) as a computational tool in clinical and cognitive contexts. This systematic review synthesises the application of AI in biomedical psychology with an explicit focus on assessment functions, rather than intervention or therapy, following the PRISMA 2020 guidelines through a systematic search of four major databases. The included studies cover a variety of clinical and cognitive applications with variations in psychological constructs, data modalities, and AI methods. The synthesis results show that AI is most often used for diagnostic classification, risk screening, and continuous estimation of cognitive functions and dimensional constructs. Differences in assessment objectives between clinical and cognitive domains reveal consistent methodological trade-offs related to model selection, validation strategies, and overfitting risks. As a key contribution, this review presents an assessment-oriented cross-domain synthesis and proposes fit-forpurpose design principles as a conceptual framework for developing robust, interpretable, and clinically relevant AI-based assessment systems
Co-Authors Agung Budi Prasetio Alfian Ma’arif Arkananta, Edgina Rangga Baballe, Muhammad Ahmad Hidayat, Rachman Imam Ahmad Ashari, Imam Ahmad Purwono Purwono, Purwono Salah, Wael A. Taraknath Paul Trivilia, Indah