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Informatics · Machine learning

Fawwaz Azhima Putra

Informatics undergraduate working on machine learning, computer vision, and materials informatics research.

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Portrait of Fawwaz Azhima Putra
Semarang, ID
01

About me

I am an Informatics undergraduate at Universitas Dian Nuswantoro, working where machine learning meets scientific research. Most of my time goes to the QCMATICS lab, where I study how autoencoder depth affects the prediction of ABX3 perovskite formation energy. Outside the lab I build applied computer vision and data analysis projects. Scientific writing has earned me six national awards so far, on topics ranging from fraud detection to public health.

Currently
Researching how autoencoder depth affects ABX3 perovskite formation-energy prediction at the QCMATICS lab
Based in
Semarang, Indonesia

Education

  • Universitas Dian Nuswantoro

    BSc Informatics Engineering

    Aug 2023 — Mar 2027 (expected) 3.62 / 4.00

  • SMA Negeri 1 Semarang

    Mathematics and Natural Sciences

    Jul 2020 — May 2023 88.24 / 100

Skills

Languages & data

  • Python
  • SQL
  • Pandas
  • NumPy

Machine learning

  • scikit-learn
  • Autoencoder
  • MLP
  • YOLOv11
  • OpenCV

Tooling

  • Git
  • Google Workspace
  • Microsoft Office
  • Canva
02

Experience

Professional

  1. Sept 2026 — Present Semarang, Indonesia

    PT Farelindo Makmur Jaya

    IT / Web Development Engineer Intern

    A premium property developer in Semarang focused on smart home ecosystems and eco-friendly residential projects.

    • Developed, tested, and troubleshot the corporate website and its supporting digital platforms.
    • Managed the database, technical documentation, and system integrations with internal and external parties.
    • Provided technical support for marketing, public relations, and corporate events.

  2. Jun 2026 — Sept 2026 Semarang, Indonesia

    PT Telekomunikasi Indonesia

    Account Manager Support Intern

    Indonesia's largest digital telecommunications company, delivering integrated connectivity, platforms, and enterprise solutions.

    • Managed and analysed Telkom client contract data to keep it accurate, consistent, and operationally efficient.
    • Built internal applications and automation tools that cut manual work and lifted team productivity.
    • Produced data-driven reports and insights that informed contract management decisions.
    • Developed an automated system for the client document bookkeeping process.

Organisation & research

  1. Jan 2024 — Present Universitas Dian Nuswantoro

    QCMATICS — Research Center for Quantum Computing and Materials Informatics

    Research Division

    A multidisciplinary research centre advancing quantum computing and materials science through AI and data-driven optimisation.

    • Runs data-driven research combining AI and quantum computing to optimise materials discovery.
    • Leads a first-author study on how autoencoder depth affects ABX3 perovskite formation-energy prediction.
    • Collaborates across disciplines to advance the materials informatics field.
    • Python
    • Autoencoder
    • Materials informatics

  2. Dec 2025 Semarang, Indonesia

    Community Service Program — SMKN 9 Semarang

    Teaching Assistant

    Introductory artificial intelligence and applied machine learning training for non-IT teachers.

    • Facilitated an AI workshop with QCMATICS lecturers for teachers at SMKN 9 Semarang.
    • Mentored non-IT teachers in building and testing image classification models with Google Teachable Machine.
    • Showed how AI fits into non-technical subjects, including traditional arts education.
    • Google Teachable Machine

  3. Nov 2023 Semarang, Indonesia

    Internet Literacy Webinar — SMPN 9 Semarang

    Committee Member

    A digital literacy webinar teaching junior high students safe and responsible internet use.

    • Planned and coordinated a digital literacy webinar for students at SMPN 9 Semarang.
    • Handled event logistics and moderated the session.
    • Developed learning material tailored to junior high students.
03

Projects

Methalyst landing page with the tagline Transform CO₂ to Green Fuel

2026 Web Development & Data Analyst

Methalyst

Simulating CO₂-to-methanol conversion — turning carbon waste into fuel.

Problem
Converting CO₂ into methanol is one of the most promising routes for putting carbon emissions to use, but finding the optimal reaction conditions and catalysts requires laboratory experiments that are slow and expensive.
Approach
A web application with three modules: Prediction to estimate conversion output, Optimisation to search for the best conditions, and Education to explain the process. TODO — describe the model used, the data source, and which variables the user can adjust.

Outcome

  • Gold Medal — NEIRA 2026
  • Favourite Paper — NEIRA 2026
SafePay.AI landing page with the tagline Spot online fraud before it is too late

2025 Web Development & Data Analyst

SafePay.AI

Spot online fraud before it is too late.

Problem
Digital transaction volume in Indonesia is growing far faster than conventional systems can detect money-laundering patterns within it. Threshold-based rules are easy to evade once an actor knows where the threshold sits.
Approach
An AI-driven fraud and money-laundering detection web app, with a prediction page for checking a single transaction and a dashboard for monitoring patterns. TODO — describe the model, dataset, and evaluation metrics.

Outcome

  • 1st place — FS2T Research and Innovation Challenge (FRIC) 2025
  • Best Paper Award — FRIC 2025
  • Best Poster Award — FRIC 2025
StuntSense dashboard showing national stunting prevalence statistics

2025 TODO — fill in your role

StuntSense

Early detection of child stunting status, with reasoning a health worker can actually read.

Problem
Stunting has a narrow intervention window — after age two, some of its effects cannot be reversed. National prevalence was still 19.8% in 2024, above the government target of 18.8% for 2025, with a wide gap between provinces: Bali at 8.7%, East Nusa Tenggara at 37.0%.
Approach
A Decision Tree application that predicts stunting status from height, age, weight, and sex, referenced against WHO Child Growth Standards. An Explainable AI (XAI) approach makes each prediction traceable — which matters because the users are health workers and parents, not data scientists.

Outcome

  • Model accuracy above 95%
  • Four prediction categories: Normal, Stunted, Severely Stunted, Tall
  • Best Paper Award — KATALIS Scientific Writing Competition 2025
  • Python
  • Streamlit
  • Decision Tree
  • Explainable AI
04

Research

  1. Under review 2026

    Impact of Architectural Depth in Autoencoders for Predicting ABX3 Perovskite Formation Energy

    Fawwaz Azhima Putra

    TODO — jurnal atau konferensi tujuan

    An ablation study of how autoencoder architectural depth affects formation-energy prediction accuracy for ABX3 perovskites, and at what point added depth stops paying off. TODO — replace with the actual abstract.

    • Autoencoder
    • ABX3 Perovskite
    • Formation Energy
    • Materials Informatics
  2. First page of the MLP stunting paper in the JAIC journal
    Published 2025

    Application of Artificial Neural Network (MLP) for Multivariate Analysis of Stunting Causes in Indonesia

    Muhammad Diva Irnanda, Ananta Surya Pratama, Fawwaz Azhima Putra, Sugiyanto Sugiyanto

    Journal of Applied Informatics and Computing (JAIC) · Vol. 9 No. 4, hlm. 1785–1792 · e-ISSN 2548-6861

    Stunting is a major public health challenge in Indonesia, primarily caused by prolonged malnutrition and recurrent infections during the First 1,000 Days of Life. This study utilizes the Multi-Layer Perceptron (MLP) neural network model to predict stunting, offering a new dimension in the analysis of complex data and identification of patterns influencing stunting. With its capabilities, the MLP model provides higher precision in detecting contributing factors to stunting. The evaluation results of the model show RMSE of 0.7231, MAE of 3.0313, and an R² value of 0.9463. The Food Security Index (IKP), feature X9, had the highest feature importance, followed by X5 (Lack of Clean Water) and X1 (NCPR). This study presents a novel approach to predicting stunting percentages and offers more objective insights to support evidence-based and effective health policies aimed at reducing stunting prevalence in Indonesia.

    • Food Security Index
    • Multi-Layer Perceptron (MLP)
    • Stunting
    DOI 10.30871/jaic.v9i4.10205
05

Awards & certifications

Awards

2026

National Empowerment, Innovation, Research, and Art (NEIRA)

Two awards at once: a gold medal and the participants’ favourite paper.

  • NEIRA 2026 Gold Medal certificate
    Awards

    Gold Medal

  • NEIRA 2026 Favourite Paper certificate
    Awards

    Favourite Paper

  • NEIRA 2026 event
    Documentation

    TODO — photo caption

  • Team documentation at NEIRA 2026
    Documentation

    TODO — photo caption

2025

FS2T Research and Innovation Challenge (FRIC)

Swept three awards with an AI-driven fraud and money-laundering detection framework — the project that became SafePay.AI.

  • FRIC 2025 first place certificate
    Awards

    1st place

  • FRIC 2025 Best Paper certificate
    Awards

    Best Paper

  • FRIC 2025 Best Poster certificate
    Awards

    Best Poster

  • FRIC 2025 event documentation
    Documentation

    TODO — photo caption

  • FRIC 2025 event documentation
    Documentation

    TODO — photo caption

2025

KATALIS Scientific Writing Competition

Best Paper for a toddler stunting-risk prediction innovation — the origin of StuntSense.

  • KATALIS 2025 Best Paper certificate
    Awards

    Best Paper

  • KATALIS 2025 event documentation
    Documentation

    TODO — photo caption

  • KATALIS 2025 event documentation
    Documentation

    TODO — photo caption

2025

FIK Awards — Universitas Dian Nuswantoro

Most Outstanding Student nomination for academic achievement, AI-driven research, and community engagement.

  • FIK Awards 2025 nominee certificate
    Awards

    Outstanding Student Nominee

Certifications

06

Contact

Open to research collaboration, internships, and data analysis projects.

If you are working on something around machine learning, computer vision, or materials informatics — or just want to talk through your research — send me an email. I usually reply within a day or two.

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