Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
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
Unknown Affiliation
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

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Control Systems and Optimization Letters

 1 
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.
Understanding Large Language Models: A Review Wulandari, Annastasya Nabila Elsa; Purwono, Purwono; Ma’arif, Alfian; Basil, Noorulden; Marhoon, Hamzah M.
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.292

Abstract

Large Language Models (LLMs) have experienced rapid development and have been established as the dominant paradigm in modern Natural Language Processing (NLP), with high performance demonstrated across various language understanding and generation tasks. Increasing architectural complexity has led to the need for a structured conceptual framework to explain how architectural design, training paradigms, and inference mechanisms are collectively associated with model behavior. A conceptual and analytical review of LLMs is presented in this article through an examination of the relationship between Transformer-based architectures, multi-stage training processes, and the resulting capabilities and limitations. Encoder-only, decoder-only, and encoder–decoder architectural variants are examined in relation to structural characteristics and functional implications. The roles of pretraining, supervised fine-tuning, and instruction tuning are analyzed to clarify how output characteristics are shaped during model development. This study emphasizes how architectural and training strategies causally influence generative capabilities and inherent limitations. Fundamental issues, including hallucination, bias, data dependency, computational cost, and evaluation challenges, are critically examined as consequences of the probabilistic modeling paradigm adopted in LLMs. This review contributes a structured analytical perspective for evaluating LLMs design choices and their operational consequences, supporting more informed development and deployment practices.
Co-Authors Agung Budi Prasetio Alfian Ma’arif Arkananta, Edgina Rangga Baballe, Muhammad Ahmad Basil, Noorulden Hidayat, Rachman Imam Ahmad Ashari, Imam Ahmad Marhoon, Hamzah M. Purwono Purwono, Purwono Purwono, Purwono Salah, Wael A. Taraknath Paul Trivilia, Indah