Jakarta, Indonesia – The Machine Learning for Official Statistics workshop, held from February 3-7, 2025, successfully concluded in Jakarta, bringing together statisticians and data scientists from across the Asia-Pacific region. Organized by the Statistics Indonesia (BPS), United Nations Statistics Division (UNSD), and the Statistical Institute for Asia and the Pacific (SIAP), the workshop aimed to enhance statistical capabilities through machine learning techniques.
A Step Towards Modernized Official Statistics
With the increasing role of Big Data and Artificial Intelligence, the workshop provided a crucial platform for participants to explore machine learning applications in official statistics and Sustainable Development Goal (SDG) monitoring. Experts from BPS-Statistics Indonesia, UNSD, and SIAP guided the sessions, covering key topics such as:
- Fundamentals of data science and machine learning.
- Supervised and unsupervised classification methods.
- Advanced regression techniques and practical exercises using R.
- Tree-based models, including random forests and decision trees.
- Ethical considerations and bias identification in machine learning models.
Engaging Participants and Field Visits
The workshop was attended by 23 participants from National Statistical Offices (NSOs) across several Asia-Pacific countries, including Indonesia, Malaysia, Kazakhstan, Timor-Leste, Thailand, Türkiye, and Vietnam.
On the first day, all participants visited the Badan Pusat Statistik (BPS) headquarters in Pasar Baru, Jakarta, where they were warmly welcomed by Acting Deputy for Methodology and Statistical Information, Puji Ismartini, and her team. The visit began with a tour of the Integrated Statistics Center (PST), followed by a session at the BPS Command Center. After lunch, participants returned to Politeknik Statistika STIS to commence the training sessions.
Expert-Led Instruction and Practical Learning
The training was delivered by experienced mentors, including Christophe Bontemps from UNSIAP and Sean Lovell from UNSD. Participants explored essential concepts and definitions of data science and machine learning, classification methods, and regression techniques. In addition to theoretical material, the mentors provided interactive demonstrations and hands-on practice materials to facilitate better understanding. which can be accessed on
Workshop AgendaMaterialsAlongside instructors from UNSIAP and UNSD, BPS experts also contributed to the sessions. Lya Hulliyyatus Suadaa, presented a case study on the classification of businesses at BPS, while Arie Wahyu Wijayanto discussed use cases of the Random Forest algorithm.
Strengthening Regional Collaboration
The event successfully fostered collaboration among national statistical offices, allowing attendees from various countries to share experiences and best practices. Discussions also focused on challenges in implementing machine learning models, particularly in accessing high-quality data and adapting models to varying institutional contexts.
Commitment to Future Innovation
As the workshop concluded, participants expressed a strong commitment to applying the knowledge gained within their respective statistical offices. Plans for further capacity-building initiatives and follow-up workshops were discussed to ensure continuous learning and innovation in the use of machine learning for official statistics.
The successful completion of this workshop marks a significant step forward in modernizing statistical systems through advanced data science and machine learning methodologies, paving the way for more data-driven decision-making across the Asia-Pacific region.
