A National Program
by Ministry of Communication and Digital Affair
The AI ββTalent Factory is one of the flagship programs of the Ministry of Communication and Digital, which brings together digital talents in the AI ββfield to develop and implement AI in various fields.
Nezar Patria
Deputy Minister of Communication and Digital of the Republic of Indonesia
The Artificial Intelligence Talent Factory (AITF) is a national program for sustainable AI talent development from the Indonesian Ministry of Communication and Digital Affairs, through the Komdigi BPSDM Digital Talent Development Center. It aims to develop top AI talent through collaboration between the government, academia, and industry. The program focuses on real-life case studies that encourage participants to apply theory and develop AI-based solutions. The AITF serves as the government's primary strategy to strengthen AI sovereignty, develop AI innovation and ecosystems, and achieve a sovereign and dignified Indonesia in the global AI technology arena.
The AITF program is specifically designed for higher education institutions as strategic partners in building a strong and sustainable AI ecosystem within the academic environment. Through this collaboration, universities not only develop AI talent but also strengthen AI research and innovation capacity on campus. Universities interested in joining can register their institutions through the Artificial Intelligence and Data Science Collaboration Potential Mapping Survey form.
Building an ecosystem for sustainable AI talent development. This ecosystem serves as a catalyst for driving innovation in the AI ββsector in Indonesia.
To develop AI talent who not only possess strong technical skills, but also have experience in the study and development of AI solutions, and are capable of applying AI technologies to solve real-world challenges across various sectors.
To produce practical solutions through AI research and innovation to address challenges in national priority programs as well as industry.
Enhancing experience, fostering active participation, and expanding the digital talent portfolio through engagement in priority programs across government institutions and the private sector.
Enhancing the quantity and quality of digital talent within the National Digital Talent Pool (DTP), managed by the Human Resources Development Agency (BPSDM) of the Ministry of Communication and Digital Affairs.
π Competencies Developed
Mastery of End-to-End AI Solution Development
Participants are equipped with comprehensive knowledge and skills covering the entire AI solution pipeline.
Practical Skills for Industry
Participants are trained through real-world case study experiences aligned with industry needs, enabling them to build applied AI product portfolios and develop the ability to critically understand business contexts and user requirements.
π― Target Participants
AITF participants are organized into collaborative teams consisting of active university students and a supervising lecturer (Technical Tutor) from the same institution. Students are preferably from fields related to Artificial Intelligence (AI), Data Science (DS), Informatics, Statistics, or Mathematics. Selection is based on technical competencies, foundational AI knowledge, and commitment to completing the proposed solution.
Each use case is derived from real-world challenges across various national priority sectors, ensuring that the resulting solutions are relevant, applicable, and implementable. Unlike conventional training programs, AITF emphasizes hands-on development based on real use cases to produce outstanding AI practitioners who are ready to enter the industry and emerge as future innovation leaders.
PMO (Project Management Officer) in charge of monitoring, coordinating, and ensuring program continuity, ensuring implementation adheres to the approved timeline, objectives, and the strategic direction set by Komdigi.
Active third- or fourth-year undergraduate (Bachelor’s) students or Master’s students, preferably from fields related to Artificial Intelligence (AI), Data Science (DS), Informatics, Statistics, or Mathematics. Selection is based on technical competencies, foundational understanding of AI, and commitment to completing the proposed solution
Expert Tutors are appointed by the AITF program implementation team based on the specific needs of each use case. They consist of senior academics, professional researchers, or industry practitioners—both domestic and international—who provide advanced mentorship, strategic input, and technical validation of the use cases developed by the team.
Each lecturer serving as a Technical Tutor may mentor a maximum of five (5) students within one team. The lecturer, who comes from the same study program as the students, acts as a day-to-day technical mentor throughout the program implementation
AITF plays a strategic role in producing AI Practitioners and AI Specialists to possess skills relevant to industry needs and global competitiveness, through a real use case based approach.
AI Specialist:
Possesses deep knowledge at the research level, innovates, produces scientific publications, leads teams, and builds frameworks and methodologies that can serve as references for Artificial Intelligence development.
AI Practitioner:
Technical mastery of integrating AI into real solutions (end-to-end AI), capable of innovating, building and fine-tuning LLM/Generative AI Models, including creating Minimum Viable Products (MVP).
AI Developer:
Technical mastery at intermediate and advanced levels including training, evaluation, hyper-parameter tuning, deployment of Discriminative AI Models. As well as expertise in using existing Generative AI models to increase productivity.
AI Beginner:
Basic understanding of AI concepts, machine learning (ML), and deep learning (DL). At this stage, one masters the basics of mathematics, statistics, and Python programming for simple machine learning applications.
Program Benefits & Activities
Core Process in Developing AI Practitioner Talent
National & International Training Programs
Access to Coursera
Soft Skills Training
National (SKKNI) & International/Global Certification
GPU & Cloud Infrastructure Support
Access to the Latest LLM APIs
Expert Tutors (Diaspora & Komdigi)
Expert Lecturers (Industry/Companies, Global Tech Firms, Overseas Universities)
Technical Tutors from Universities
Mentors from Key Stakeholders
Program Benefit
Registration through partner universities collaborating with the AITF Program and BPSDM Komdigi
Pengarah Komdigi
Bonifasius Wahyu Pudjianto, Ph.D
Head of BPSDM Komdigi
Tutor Ahli & Penanggung Jawab Program
Said Mirza Pahlevi, D.Eng.,Β M.Eng.
Head of Center for Digital Talent Development (BPSDM Komdigi) Senior Data Scientist & GenAI Engineer
Tutor Ahli Diaspora
Louis Owen
AI Researcher | AI Research Engineer, Author of Hyperparameter Tuning with Python
Cahya Wirawan
System Engineer at CTBTO Austria | System Engineer, Software Engineer, AWS Community
Adhiguna Surya Kuncoro
Research Scientist | NLP, Multilingual AI, Deep Learning
Tutor Ahli Industri
Winton
Business-Driven Technology Leader at IBM Indonesia | AI, Cybersecurity and Hybridcloud Technologist
Muhammad Maliqi Akbar
Solution Architect at Alibaba Cloud Indonesia | Technology Enthusiast, Data & AI Expert
Tutor Ahli Universitas Luar Negeri (Sungkyunkwan University)
Murugarj Odiathevar, PhD.
Research Business Foundation Sungkyunkwan University, Republic of Korea
University of Tokyo (MatsuoΒ Lab)
Shunsuke Kamiya, Ph.D.
Researcher at Matsuo-Iwasawa Lab | University of Tokyo
Dr. Zihui Li (Irene)
Project Lecturer at Matsuo-Iwasawa Lab | University of Tokyo
University of Twente
Dr. Alexia Briassouli
Assistant Professor on Artificial intelligence
Program Timeline
The initial phase of AITF participant registration, encompassing administrative screening, written examinations, and interviews conducted by the Ministry of Communication and Digital Affairs and the University of Brawijaya
Participants undergo a comprehensive onboarding process followed by specialized Associate Data Scientist training
Participants undergo a technical briefing and Associate Data Scientist certification as a prerequisite for the subsequent stages.
Initial briefing on Large Language Model (LLM) concepts and data collection preparation for advanced training processes. Furthermore, participants undertake Deep Learning training as an advanced continuation of the Associate Data Scientist curriculum.
Briefing on Continued Pre-Training (CPT) and Supervised Fine-Tuning (SFT) preparations using curated datasets. Additionally, participants will undergo Advanced Data Analytics training as a progression from the Deep Learning phase
Evaluation of CPT and SFT outcomes, alongside preparations for transforming LLMs into a Minimum Viable Product (MVP). Participants will also continue their ongoing Advanced Data Analytics training
Participants will present their Proof of Concept (PoC) innovations utilizing LLMs and receive comprehensive briefings on the real-world implementation and maintenance of LLM systems
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