Artificial Intelligence Lab
November 4, 2020
Thermal & Fluid Science Laboratory
March 15, 2024The Big Data Laboratory at Universitas Multimedia Nusantara (UMN) is a supporting facility for final projects, research, and academic initiatives focused on managing and visualizing large-scale data. It is equipped with two computers and a high-specification server.
In addition, the laboratory features a large display window to present analysis results clearly and comprehensively. This environment enables students and lecturers to develop data-driven solutions efficiently and accurately. Various professional software tools such as Jupyter Notebook, Tableau, Power BI, and Google Colab further support data exploration, making this lab a hub of innovation aligned with industry and societal needs.
The UMN Big Data Lab is designed to support the entire data processing workflow for end-users—from data collection, storage, and processing to visualization. The available facilities include:
High-Performance Computing (HPC) Cluster
Equipped with GPUs to support advanced data analysis involving machine learning and deep learning for forecasting and predictive tasks. The server features multi-core processors and large memory capacity optimized for parallel processing.
Large-Scale Storage Capacity
A storage system with high capacity to accommodate data from various sources. It supports distributed storage systems for efficient data management.
Big Data Frameworks
Platforms such as Anaconda, Jupyter Notebook, Python, VS Code, and RStudio for both real-time data processing and batch processing.
Business Intelligence Tools
Data visualization tools such as Tableau, with interactive dashboards to support dynamic analysis and reporting.
Collaboration and Presentation Space
Equipped with smart displays and comfortable group work areas to support discussions, presentations, and interdisciplinary brainstorming.
Recent Projects:
- Optimizing Retrieval-Augmented Generation Through Agentic RAG Ecosystem Based on Fine-Tuned BERT Cross Encoder and GPT-4 Model
- From Waves to Vision: Transforming Heart Sound Classification with Wav2Vec 2.0 and Vision Transformers
- Discovering Mental Health-Related Communities in Social Networks through Graph-Based Greedy Modularity Detection and BERTopic
- Accelerated Training of Swin Transformer V2 Models for Facial Expression Recognition via Mixed Precision
- U-Net–Based Pixelwise Smoke Detection in Low-Altitude UAV Image
