Policymaking in the Age of AI

Harnessing Deep Learning to Understand Public Sentiments for Enhanced Policies

  • Schedule

    19 – 21 September 2023

    09.00 – 15.00 (WIB)

  • Investment

    Rp. 5.550.000


Hours Course


In today’s digital age, the vast expanse of textual information available on the internet offers a gold mine of public sentiment. Sentiment analysis, now more than ever, is becoming a critical tool that enables organizations, analysts, and policymakers to tap into public opinion on various subjects by discerning between positive and negative nuances within textual data.

Join us for a 3-day immersive onsite workshop designed for beginners, where we will explore the nuances of sentiment analysis using the Keras deep learning library in R. This course promises a harmonious blend of theoretical insights and hands-on exercises, ensuring a rich and interactive learning journey. With the guidance of an expert instructor, accompanied by two adept teaching assistants, participants will be well-supported throughout their learning journey, ensuring they surmount challenges with ease.

Course Syllabus

  • Course Overview: A comprehensive overview of course materials, schedule, and workshop objectives.
  • Introduction to Text Mining & Machine Learning: A foundational understanding of key concepts and their significance.
  • Operational Framework: A structured description of the workshop’s methodology, including toolkits and setup procedures.
  • Introduction to R: Familiarize with the foundational principles of the R programming language.
  • Navigating the R Studio Environment: A guided tour of the R Studio interface and its core functionalities.
  • Utilizing R Markdown: A focus on achieving reproducible research through R Markdown.
  • Data Examination Techniques: Strategies and techniques to understand and assess data structures.
  • The Essence of Neural Networks: A structured overview of neural networks and their operational mechanics.
  • Gradient Challenges Explored: A study into the issues associated with gradients in deep learning.
  • Recurrent Neural Networks (RNN) & LSTM: An analytical examination of RNNs and the advancements of Long Short-Term Memory networks.
  • Text Data Management in R: Procedures and techniques to load, clean, and preprocess text data.
  • Tokenization of Text Data: Methodologies for transforming text data into interpretable token formats.
  • LSTM Architecture Design: Constructing and understanding LSTM models utilizing the Keras interface in R.
  • Model Evaluation Techniques: Methodologies to critically assess and refine the performance of LSTM models.

Course Receivables:

  • Lecturer’s Notes

    Including Course Books (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the assistance of mentorship of our lead instructor and a band of qualified teaching assistants throughout each course.

  • Certification of Completion

    Show current employer hat you’ve completed the course with a signed certificate of completion.

  • Quality Learning Environment

    We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, and small group size.

  • Refreshments & Coffee Break

    In our commitment to delivering a premium experience, we collaborate with leading catering services in Jakarta. Our aim is to ensure that all participants are served delectable lunches and revitalizing coffee breaks.


Courses in this series cater to a diverse audience: from casual learners and working professionals to those venturing into data science and machine learning without a programming background.

We recognize that many students may not have prior expertise in statistics, mathematics, or algebra. Therefore, our courses are designed with a gentle learning curve, placing a strong emphasis on hands-on experience and individualized instruction. Our dedicated team of instructors and teaching assistants ensure personalized guidance every step of the way.

Teaching Methodology:

Students work through tons of real-life examples using sample datasets donated by our mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.