Summary of "WEBINAR NASIONAL:Sinergi AI & Machine Learning untuk Efisiensi & Pengambilan Keputusan Berbasis Data"

Summary of the Webinar

“Synergy of AI & Machine Learning for Efficiency & Data-Driven Decision Making in Digital Transformation” Date: November 7, 2025 Organizer: Informatics Engineering Undergraduate Study Program, Tekom University Semarang Format: National webinar with 10 expert speakers from various Indonesian universities and research institutions Moderator: Mrs. Clina Helena Panjaitan, SKOM, MKOM

Main Ideas and Concepts

1. Opening and Context Setting

The webinar emphasized the synergy between Artificial Intelligence (AI) and Machine Learning (ML) as key drivers for innovation, operational efficiency, and data-driven decision-making within digital transformation initiatives.

AI is broadly defined as systems performing human-like tasks.
ML is a subset of AI enabling systems to learn and improve from data.
The synergy aims to:
- Foster innovation (new products, services, business models).
- Enhance efficiency through automation and optimized processes.
- Support decision-making based on data and predictive analytics, moving away from intuition-based decisions.

2. Presentations by Speakers

Dr. Dedi Sugiarto – AI Integration in Knowledge Management

Knowledge management involves creating and sharing knowledge, transitioning from tacit (individual) to explicit (documented) knowledge.
The SECI model (Socialization, Externalization, Combination, Internalization) explains knowledge flow.
AI and ML enhance knowledge management by embedding data into documents and enabling natural language queries for retrieval.
Platforms like OpenAI, Gemini, and open-source tools support AI-powered knowledge systems.
AI extends the SECI model by facilitating human-machine and potentially machine-machine knowledge exchange.

Dr. Surya Mikrandi Nasution – Competitive Machine Learning for Traffic Classification

Focused on intelligent transportation systems, specifically traffic condition classification in Bandung.
Utilized 29 ML models including neural networks, KNN, logistic regression, decision trees, and random forest.
Applied majority voting (weighted and non-weighted) to select the best model.
Achieved accuracy around 69-70%, with weighted voting performing better.
Potential application: real-time traffic management and congestion reduction.

Mr. Muhammad Zain Fawas Nuruddin Siswantoro – AI for Software Defect Detection

Software defects are errors appearing in production software; manual detection is costly and slow.
AI automates defect detection using NASA MDP datasets with features like lines of code and cyclomatic complexity.
Addressed class imbalance with data balancing and dimensionality reduction (PCA).
Used SVM with hyperparameter optimization (Gray Wolf Optimization combined with random search) to improve accuracy (~87% average).
AI enables early detection, reducing costs and improving software quality.

Mr. Jefri Andika Putra – The Silent Revolution: Innovation, Efficiency, and Data-Based Decisions

Digital transformation is more than digitizing processes; it involves cultural, product, and process changes.
Data abundance (“data tsunami”) is underutilized due to human limitations and resistance to change.
AI and ML accelerate decision-making speed, crucial in competitive environments.
Innovation driven by generative AI enables hyper-personalization and new business models.
Challenges include organizational inertia, cultural resistance, and demographic knowledge gaps.

Dr. Romeyin Perdana Putra – AI in Smart Cities and Smart Defense of IKN (Indonesia’s New Capital)

Smart defense combines hard power (military), soft power, and smart power (technology).
AI and ML are critical for cybersecurity and defense strategies amid global geopolitical tensions.
Research shows growing global interest in smart defense technologies but limited in-depth studies.
IKN development includes smart green city initiatives (solar panels, smart water management).
Future work involves integrating AI in defense command centers and enhancing cybersecurity.

Mr. Rizal Rahman – Analysis of Public Service Satisfaction Using AI (K-Means Clustering)

Studied public satisfaction with services in West Java’s Ministry of Communication and Information.
Used AI clustering (K-means) to categorize satisfaction levels and identify service deficiencies.
Found clusters indicating inadequate internet and incomplete information services.
Developed a prototype dashboard to visualize satisfaction clusters.
AI clustering helps optimize public service delivery by identifying problem areas.

Mr. Abdul Kadir – Smart Decisions in Motion: AI & ML to Accelerate Digital Transformation

Provided a historical overview of AI development from the 1950s to present.
AI applications include recommendation systems, digital assistants, facial recognition, and medical diagnosis.
Presented a case study on group decision support systems (GDS) for early childhood education accreditation.
Emphasized AI’s role in augmenting human decision-making, not replacing humans.
Future AI integration involves big data and adaptive decision systems.

Dr. Adi Kusnadi – Synergy of AI and ML for Innovation, Efficiency, and Data-Based Decision Making: Case Study on Deepfake Video Detection

Addressed the challenge of detecting deepfake videos using AI.
Used CNN (ResNet-50) for spatial feature extraction and LSTM for temporal sequence analysis.
AI detects subtle manipulations like inconsistent lighting and unnatural eye movements.
Automated large-scale content analysis supports content moderation and digital trust.
Continuous improvement needed to keep up with advancing deepfake technology.

Mr. Bagus Sudirman – Driving the Future of Business with Artificial Intelligence

AI as a catalyst for business transformation and digital revolution.
Applications include virtual assistants, chatbots, personalized marketing, and content creation.
AI accelerates decision-making, automates complex tasks, and uncovers hidden business opportunities.
Challenges include algorithmic bias, data privacy, skills gap, and regulatory uncertainty.
Steps for businesses to adopt AI:
- Implement AI chatbots.
- Use generative AI tools.
- Invest in digital skills training.
AI is essential for competitiveness and innovation in 2025 and beyond.

Dr. Ejo Imandeka – Digital Transformation in the Era of AI: New Paradigm of Data-Driven Decision Making

Digital transformation reshapes organizations beyond technology adoption.
Success depends on integrating social (people, culture) and technical (systems, processes) dimensions.
Many organizations struggle due to lack of IT skills, financial constraints, and organizational inertia.
Emphasized data as the “new oil” — critical for insight and competitive advantage.
Data governance and ethical AI use are essential.
Digital transformation enables adaptation, competitiveness, customer satisfaction, and opportunity capitalization.
Frameworks like socio-technical systems approach are useful for managing transformation.

Methodologies and Instructions Highlighted

Knowledge Management with AI:
- Enhance SECI model stages with AI embedding and natural language querying.
- Use AI platforms such as OpenAI, Gemini, and Hugging Face for document embedding and retrieval.
Traffic Condition Classification:
- Collect traffic data from CCTV and APIs.
- Train multiple ML models (Neural Network, KNN, Logistic Regression, Decision Trees, Random Forest).
- Use majority voting (weighted and non-weighted) to select the best performing model.
Software Defect Detection:
- Use NASA MDP datasets.
- Balance classes (defective vs non-defective).
- Apply dimensionality reduction (PCA).
- Train SVM with hyperparameter optimization (Gray Wolf Optimization + random search).
Public Service Satisfaction Analysis:
- Collect survey data.
- Apply K-means clustering to categorize satisfaction levels.
- Visualize clusters via dashboards for decision-making.
Deepfake Detection:
- Extract spatial features using CNN (ResNet-50).
- Analyze temporal sequences using LSTM.
- Detect inconsistencies indicating manipulation.
Business AI Adoption Steps:
- Start with AI chatbots for customer service.
- Use generative AI tools for content creation.
- Invest in employee digital skills training.
Digital Transformation Framework:
- Consider socio-technical system approach (social + technical dimensions).
- Focus on data governance, ethical AI, and organizational culture change.
- Address skills gaps and financial planning.

Key Lessons and Takeaways

AI and ML are complementary technologies that, when synergized, drive innovation, improve efficiency, and enable data-driven decision-making.
Digital transformation involves cultural, process, and product innovation, not just technology adoption.
AI applications span diverse fields: knowledge management, traffic systems, software quality, public services, defense, business, and digital content security.
Challenges include data quality, resistance to change, skills shortages, ethical concerns, and regulatory gaps.
Successful AI integration requires clear problem identification, tailored solutions, and continuous adaptation.
Collaboration between academia, industry, and government is vital for advancing AI research and implementation in Indonesia.

Speakers and Sources Featured

Mrs. Clina Helena Panjaitan, SKOM, MKOM – MC and moderator, Lecturer at Tekom University Semarang
Dr. Dedi Sugiarto, S.Si., M.M., M.Kom – Lecturer, Trisakti University Jakarta
Dr. Adi Kusnadi, ST., M.Si., SI – Head of Master Informatics Study Program, Nusaputra University Sukabumi
Associate Prof. Dr. Surya Mikrandi Nasution, ST, MT – Lecturer, Telkom University Bandung
Mr. Muhammad Zain Fawas Nurudin Siswantoro, SKOM, MKOM – Lecturer, Semen Indonesia International University, Gresik
Mr. Jefri Andika Putra, ST, MM, M. Engineering – Lecturer, Janabadra University Yogyakarta
Dr. Romeyin Perdana Putra, S.Sos, M.M., MBA – Researcher, BRIN (Indonesian National Research and Innovation Agency)
Mr. Rizal Rahman, S.Si., M.M., M.Kom – Lecturer, Ardi Jasa Reswara Sanjaya University, Bandung
Mr. Abdul Kadir, SKOM, MKOM, PhD – Lecturer, Sari Mulia University, Banjarmasin
Mr. Bagus Sudirman, SKOM, MKOM – Lecturer, Tekom University Semarang
Dr. Ejo Imandeka, ST., M.TI, TI – Lecturer, Indonesian Aid Polytechnic, Depok

This summary encapsulates the key themes, methodologies, and insights shared during the national webinar on AI and machine learning synergy for enhancing efficiency and data-driven decision-making in Indonesia’s digital transformation journey.

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