Journey

How I got here since 2017, and where I am heading next.

Timeline

From the first repository to the classroom.

  1. 2017

    Started writing code on GitHub

    Opened a GitHub account in November 2017, and it has held my code ever since.

  2. 2019

    Started studying informatics

    Began a bachelor's in informatics engineering at STIKOM Poltek Cirebon, while working as a computer lab assistant.

  3. 2020

    Moved to UNU Cirebon

    Continued my bachelor's at Universitas Nahdlatul Ulama Cirebon from the third semester, worked there as an IT support specialist, and joined Dicoding.

  4. February 2022

    Co-founded AWATARA

    Co-founded AWATARA Research and Education Solution as co-founder and full stack engineer.

  5. 2023

    Graduated, joined Mahardika

    Earned my bachelor's in informatics engineering from UNU Cirebon with a thesis on RSA cryptography against book counterfeiting. In December I joined Universitas Insan Mahardika, then Institut Teknologi dan Kesehatan Mahardika, on the IT staff.

  6. 2024

    Graduate studies

    Studied for a Master of Computer Science in Information Systems at STMIK LIKMI Bandung, while teaching as part of a teaching team.

  7. April 2025

    Earned a Master of Computer Science

    Completed graduate studies with the M.Kom. degree, and graduated in May 2025.

  8. May 2025

    Permanent informatics lecturer

    Appointed permanent lecturer in the Informatics Study Program, Universitas Insan Mahardika.

  9. 2026

    AI engineering and a research grant

    Completed the Pijak AI Engineer program with IBM SkillsBuild in full, took part in PEKERTI Batch 30, and began leading the NetA project under a Beginner Lecturer Research grant.

  10. Now

    Teaching, leading, and building

    Secretary of the Informatics Study Program and IT staff at Universitas Insan Mahardika, co-founder of AWATARA Research and Education Solution, and lecturer at FILKOM UNU Cirebon.

What comes next

I am moving from web developer to AI engineer without leaving the classroom. Here is my five-year plan, how I work with AI, and what I am studying right now.

Plan for 2026 to 2030

  1. 2026In progress

    From web developer to AI engineer

    • Completed the Pijak AI Engineer program with IBM SkillsBuild (July 2026)
    • Release a web application with a machine learning model, targeting accuracy above 80 percent
    • NetA research: a network agent built on IoT and a large language model
    • Write AI course material aligned with the study program's cloud and big data focus
  2. 2027

    AI research and publication

    • Publish AI research in accredited national journals
    • A cloud-based analytics platform for digital entrepreneurs
    • AI and big data for personalized learning
    • Cloud-based virtual laboratories
  3. 2028

    AI inside campus systems

    • AI features running fully in the campus academic system
    • Predictive technology for education with big data
    • Serverless architecture for small business applications
  4. 2029

    Transparent, secure AI

    • Explainable AI for transparent educational recommender systems
    • Blockchain and cloud for data security in digital small businesses
    • Green computing: energy-efficient deep learning at the edge
  5. 2030

    Sharing more widely

    • Bring intelligent systems to other institutions
    • Be a go-to AI reference on campus and a seminar speaker
    • Earn advanced certifications

How I work with AI

Foundations first, AI second

I build course material from journals and sound literature. That foundation gives me a yardstick to check what AI says instead of simply accepting it.

Compare, never take it whole

I test the same prompt on several models. Where the answers differ, I have something to weigh, and that weighing is what keeps critical thinking sharp.

People make the final call

AI is a predictive model, not a reasoner. It speeds up the work, but I still write and analyze complex system logic myself, because only trained judgment catches a model's hallucinations.

Currently studying

  • Linear algebra, calculus, and statistics for machine learning
  • The Python data science stack: Pandas and TensorFlow
  • Integrating machine learning models into web and cloud architectures
  • Pedagogy for teaching AI
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