About

The 2025 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS) is a premier event dedicated to advancing the fields of data science and information systems. Scheduled to take place from 3 – 4 February 2025, in Bandung, Indonesia, with a hybrid platform allowing for both in-person and virtual participation, ICADEIS 2025 aims to bring together leading educators, researchers, policymakers, and industry practitioners from around the world.

ICADEIS was first established in 2019, driven by the need to explore and address the evolving challenges at the intersection of data science and information systems. Over the past decade, the conference has grown significantly in scope and impact, becoming a key platform for sharing innovative research and fostering collaboration among global experts.

In its early years, ICADEIS focused on foundational topics in data science and information systems. As the fields have advanced, the conference has expanded to cover emerging trends and technologies, reflecting the rapid evolution of the industry. Notable past editions have featured groundbreaking research, influential keynote speakers, and numerous networking opportunities, contributing to significant advancements in the field.

Each year, ICADEIS attracts a diverse audience of researchers, practitioners, and policymakers, all committed to exploring the latest developments and applications in data science and information systems. The conference continues to build on its legacy of excellence, providing a vital forum for discussion, innovation, and collaboration.

Objectives of ICADEIS 2025:

Join us at ICADEIS 2025 to explore the latest advancements, network with peers, and contribute to the future of data science and information systems. Together, we can drive innovation and sustainability through interdisciplinary collaboration.

Key Areas in Data Science

Data Science encompasses a wide range of topics and technologies. Here are some key areas under this scope:

Big Data Technologies

Explore technologies and techniques for handling and analyzing large datasets.

Machine Learning

Understand algorithms and models that enable computers to learn from and make predictions based on data.

Data Mining & Visualization

Learn methods for extracting meaningful patterns from data and visualizing them effectively.

Optimization Algorithms

Study algorithms designed to find the best solution from a set of possible choices.

Statistical Learning

Explore techniques for statistical modeling and inference in data science.

Granular Computing and Fuzzy Systems

Examine principles of granular computing and fuzzy logic for data analysis and decision-making.

Information System Business Intelligence

Understand how business intelligence integrates with information systems to support strategic decisions.

e-Business

Explore the role of electronic business in data management and analysis.

Big Data Analytics

Learn techniques for analyzing and deriving insights from large data sets.

Graph Analytics

Discover methods for analyzing graph structures and relationships in data.

Real-time Big Data Analysis

Explore strategies for analyzing big data in real-time for timely insights.

Data Models for Big and Smart Data

Understand different data models designed for big and smart data applications.

Business Models for Big Data and Smart Data

Investigate how business models are adapted for big data and smart data environments.

Semantic Web Applications

Learn about the application of semantic web technologies in data science.

Data and Information Quality

Explore methods for ensuring the quality of data and information in various applications.

Information Extraction

Understand techniques for extracting valuable information from large datasets.

Data Integration (Conceptualization, Notation, and Ontologies)

Examine approaches to integrating data using conceptual frameworks, notations, and ontologies.

Data Management for Analytics

Learn about data management practices that support effective analytics.

Web Analytics

Discover techniques for analyzing web data to improve user experiences and performance.

Statistics and Exploratory Data Analysis

Explore statistical methods and exploratory data analysis for uncovering data patterns.

Data Modelling and Visualization

Understand the importance of data modeling and visualization in interpreting data insights.

Data Structures and Data Management

Learn about various data structures and management practices essential for efficient data handling.

City Data Management

Investigate methods for managing data in urban environments.

Deep Learning and Big Data

Explore the intersection of deep learning and big data for advanced analytics.

Social Web Search and Mining

Understand methods for searching and mining social web data.

Big Data as a Service

Examine the concept of big data as a service and its implications for businesses.

Key Areas in Information Systems

Information Systems cover various aspects of technology and its role in organizational processes. Key topics include:

Enterprise Modelling on Data and Information

Explore frameworks and techniques for modeling enterprise data and information systems.

Enterprise System

Understand the integration of core business processes through enterprise systems.

Technology Acceptance Model

Examine the model that explains how users come to accept and use technology.

Business Process Management

Learn about managing and improving business processes to enhance organizational efficiency.

IT Governance

Explore principles and practices for governing IT resources and aligning them with business goals.

E-Business and E-Commerce

Understand the impact of electronic business and commerce on organizations and markets.

E-Government

Learn about the application of information technology in government processes and services.

Data Governance

Examine practices for managing and ensuring the quality of data within organizations.

IT Project Management

Understand techniques and strategies for managing IT projects effectively.

Digital Transformation

Explore strategies for leveraging digital technologies to transform business operations and models.

IT Network

Learn about the design, implementation, and management of IT networks within organizations.

Key Areas in E-Learning

E-Learning cover a wide range of strategies and technologies aimed at enhancing the educational experience. Key topics include:

Gamification and Interactive Learning

Enhance student engagement through game design elements and interactive techniques in education.

Technology-Enhanced Learning

Utilize digital tools and resources to improve and personalize the learning process.

Online Assessment and Learning Analytics

Evaluate student performance and learning outcomes using digital assessments and data-driven insights.

Quality Assurance and Standards in E-Learning

Ensure online education meets quality benchmarks through rigorous standards and practices.

Virtual Laboratories and Simulations

Provide hands-on, computer-based environments for safe and controlled experimentation and learning.

E-Education

Deliver and support educational content through digital technologies for flexible and scalable learning.

Past Conferences

This section showcases the topics of past conferences and includes photo documentation from those events. Explore the significant themes and memorable moments from previous editions of our conference.

Topics from Past Conferences:
  • Data Science Innovations and Applications
  • Advancements in E-Learning Technologies
  • Information Systems and Big Data
  • Machine Learning and Artificial Intelligence
  • Cybersecurity and Data Privacy
Past Conferences Archives: