Backend systems
APIs, data models, and architecture designed for reliability under real-world pressure.
I turn complex business problems into clear, dependable software — from robust backend systems to AI-powered products people enjoy using.
Good software should make difficult things feel obvious. I bridge engineering depth and product judgment to build systems that are maintainable behind the scenes and effortless in the hands of users.
APIs, data models, and architecture designed for reliability under real-world pressure.
Useful intelligence woven into products through LLMs, RAG, and machine-learning workflows.
Thoughtful interfaces backed by pragmatic technical decisions from idea to launch.
Built across web, mobile, data, and machine learning — always with the same obsession for clarity.
A selection of systems, products, and experiments — each shaped around a real constraint.
View case study ↗Centralizing inventory, receivables, and book-closing for a trading company.
View case study ↗A vanilla-stack marketing site built for speed, SEO, and easy handoff.
View case study ↗Turning handwritten cash books into QRIS-powered digital transactions.
View case study ↗Helping boarding-house students track money and tasks in one place.
View case study ↗A CNN model that spots sick plants before a human has to guess.
View case study ↗Learning React Native the hard way — two weeks, one Figma file, no shortcuts.
View case study ↗A Gen-Z digital invitation, built as a birthday gift for my sister.
My path through engineering, competitions, and teaching — the environments that sharpened how I solve problems.
A self-directed internship focused on developing a scalable, enterprise-grade Human Resource Information System (HRIS). Worked across the full stack — building responsive, dynamic user interfaces with Vue.js, and designing secure backend APIs and business logic with the .NET framework. Implemented features to handle complex HR workflows, secure data separation for multi-tenant environments, and optimized database queries for high performance and reliable data processing.
Participated in the Datavidia machine learning competition, which challenged participants to apply predictive models to structured data. Though the team did not place in the final rankings, the competition provided hands-on experience designing end-to-end machine learning workflows — including feature engineering, ensemble learning, and dimensionality reduction — sharpening practical ML skills in a competitive setting.
Competed in the Data Slayer deep learning competition, focused on computer vision tasks. Collaborated with a team to develop and optimize convolutional neural networks (CNNs) using Python and TensorFlow. The team placed in the top 20 out of more than 200 participating groups, demonstrating strong model performance and effective teamwork under pressure.
Served as a tutor for the Algorithm and Programming course during the odd semester of 2024. Facilitated weekly tutorial sessions to help first-year students grasp fundamental programming concepts using C, explaining algorithmic logic and guiding students through hands-on coding exercises. The role sharpened both technical communication skills and personal understanding of core computer science principles.
Open to software engineering roles, AI product collaborations, and focused freelance work.
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