Summary of "WEBINAR NASIONAL: AI dan IoT untuk Dunia Modern:Inovasi, Implementasi, dan Dampaknya bagi Masa Depan"

Summary of the Webinar:

Title: WEBINAR NASIONAL: AI dan IoT untuk Dunia Modern: Inovasi, Implementasi, dan Dampaknya bagi Masa Depan

Date: Wednesday, May 28, 2025

Organizer: Bachelor of Computer Systems Study Program, University of Computer Science and Engineering Semarang (Tekom University)

Format: Presentations by 10 expert speakers, followed by Q&A

AI (Artificial Intelligence) and IoT (Internet of Things) are no longer futuristic concepts but integral to modern life across sectors like industry, health, education, transportation, and government.
The webinar AIms to bridge theory and practice by sharing knowledge and encouraging critical thinking about the ethical, policy, and workforce impacts of these technologies.
Collaboration among universities and institutions is emphasized for advancing innovation and application.

Participants must mute microphones during presentations.
Questions can be submitted via Zoom chat or YouTube comments with clear identification.
Attendance forms must be filled for digital certificates.
Presentations are time-limited (~10 minutes each) with possible extensions during Q&A.

Topic: AI and IoT Architecture & Data Processing Optimization for Real-Time Smart Transportation

Challenges in transportation: traffic congestion, accidents, pollution.
AI + IoT provide real-time, adaptive data-driven solutions using Edge Computing (processing near data source instead of cloud).
Key concepts:
- IoT: network of interconnected smart devices.
- AI: analytics and automatic decision-making.
- Edge Computing: local data processing for low latency.
Architecture includes IoT+sensors+for+smart+home&tag=dtdgstoreid-21">IoT sensors embedded in vehicles, edge devices running AI models for Machine Learning and Deep Learning.
Case studies: smart traffic lights that reduce wAIting time by 30%, smart public transport monitoring speed, driver behavior, and route optimization.
Advantages: low latency, reduced bandwidth use, improved privacy/security through local data processing.
Challenges: limited Edge Computing power, system modularity, data security (encryption/authentication).
Future: integration with 5G, autonomous vehicles, scalable and energy-efficient systems.
Closing note: humans remAIn creators and controllers of AI systems.

Topic: Towards the Hyperconnected Era: How AI and IoT Reshape the Modern Ecosystem

Hyperconnected era: billions of IoT devices communicating in real-time creating smart ecosystems.
AI acts as the brAIn analyzing data collected by IoT (the nervous system).
Applications in smart cities (traffic optimization), health (heart monitoring), industry 4.0 (predictive mAIntenance), agriculture (smart farming), retAIl (cashier-less stores).
Examples: Tesla’s autonomous cars, Singapore’s Smart Nation platform reducing carbon emissions by 35%.
Challenges: data security (43% cyberattacks target IoT), infrastructure costs (5G, AI servers), ethics and privacy concerns (mass surveillance).
Future: AI processing at device level (edge AI), faster networks (5G/6G), green energy smart grids.
Conclusion: AI and IoT are foundational to future civilization; success depends on global collaboration and ethical management.

Topic: Integration of AI+and+IoT&tag=dtdgstoreid-21">Ontology with AI and IoT for Semantic Interoperability

AI+and+IoT&tag=dtdgstoreid-21">Ontology: formal representation of knowledge to enable shared understanding among heterogeneous IoT devices from different manufacturers.
Addresses semantic interoperability, ambiguity (same term different meanings), and synonymy (different terms same meaning).
AI+and+IoT&tag=dtdgstoreid-21">Ontology enables automated, intelligent data processing and device interaction without human intervention.
Example: smart campus where temperature sensors and AC units from different vendors understand each other’s data via AI+and+IoT&tag=dtdgstoreid-21">Ontology.
AI+and+IoT&tag=dtdgstoreid-21">Ontology modeling involves defining entities (students, classrooms, sensors), relationships, and properties using semantic web languages (e.g., OWL, SPARQL).
This facilitates energy saving, automation, and improved comfort.
Challenges: data confidentiality, reluctance to share data, complexity in building ontologies.
Emphasizes the importance of AI+and+IoT&tag=dtdgstoreid-21">Ontology for scalable, smart IoT systems.