Wisebites Mobile
A native Android application that utilizes Kotlin and Firebase to help users discover, bookmark, and learn how to cook various recipes based on the categories and recipe names.
I am a Computer Science graduate and a passionate Software Engineer. My journey is driven by a constant desire to learn, innovate, and build systems that effectively solve modern problems.
I thrive at the intersection of diverse technologies. From engineering resilient backend architectures and developing intuitive website & mobile applications to integrating predictive machine learning models, I am driven by a passion for delivering smart, end-to-end solutions.
"The more deeply you understand the problem, the more likely you are to land on an elegant and effective solution."
When I'm not writing or debugging code, I enjoy exploring the broader spectrum of computer science—from learning new programming technologies to diving deep into competitive programming algorithms. To unwind and find fresh inspiration, I immerse myself in reading manga and manhwa or enjoying my favorite J-pop playlists.
A collection of my most noteworthy projects.
A native Android application that utilizes Kotlin and Firebase to help users discover, bookmark, and learn how to cook various recipes based on the categories and recipe names.
A cross-platform financial management app built with Flutter and Firebase, designed to simplify tracking income and expenses through real-time data synchronization, analyze spending habits, and monitor daily transactions.
A highly scalable RESTful API made with Hapi.js for managing music, albums, playlists, and user interactions that utilizes PostgreSQL for reliable data storage, JWT for secure authentication, Joi for payload validation, Redis for caching, and RabbitMQ for message brokering.
A financial tracker website built with PHP and PostgreSQL that efficiently handles user accounts, record categorized transactions into income and expenses, and accurately calculate financial summaries.
A personalized Anime Recommendation System built with machine learning, integrating content-based filtering and deep learning-powered collaborative filtering to accurately predict user preferences.
An engaging multiple-choice quiz application, built in both Kotlin and Flutter versions, featuring immediate feedback, a responsive interface, and seamless state preservation.
A secure and scalable Hapi.js RESTful API for note management and collaboration, integrating PostgreSQL, JWT, Joi, Redis, and RabbitMQ to ensure high performance and reliable asynchronous processing.
A responsive desktop application built with Java and MySQL that streamlines daily productivity through intuitive task management, user authentication, and efficient filtering.
A Java-based desktop POS application designed to streamline minimarket operations, featuring comprehensive product and transaction CRUD management alongside a simple checkout system with automated change calculation.
A comprehensive machine learning project utilizing K-Means clustering and KNN/SVM classification models to group global airports and predict traffic categories for data-driven aviation planning.
A sentiment analysis pipeline comparing multiple machine learning models using TF-IDF and Bag of Words to classify Google Play reviews and uncover user satisfaction trends for Mobile Legends.
A computer vision project utilizing Convolutional Neural Networks (CNN) to accurately classify vegetable images, featuring deployment-ready models exported in TF-Lite and TFJS for real-time agricultural applications.
A predictive analytics project utilizing Exploratory Data Analysis alongside SVM and Random Forest regression models to determine how study hours and extracurricular activities impact a student's performance index.
A scalable ETL pipeline that extracts fashion industry data via web scraping and efficiently loads the transformed data into PostgreSQL, Google Sheets, and flat files for analysis.
An end-to-end deep learning pipeline built to predict stock prices for ten major Indonesian companies, complete with data processing and evaluation workflows.
A production-ready machine learning system for diabetes prediction, featuring automated CI workflows, MLflow for experiment tracking and hyperparameter tuning, and real-time alerting via Prometheus and Grafana.
An automated end-to-end MLOps pipeline built with TensorFlow Extended (TFX) that seamlessly handles data ingestion, evaluation, and the deployment of a deep neural network to accurately classify fake news.
An end-to-end TFX-based MLOps pipeline featuring a deep neural network for personality classification, fully deployed on Railway with real-time Prometheus monitoring.
The core technologies and tools I use to build my projects.
Telkom Indonesia | Medan, Indonesia
Sep 2022 - Apr 2026
GPA: 3.98 / 4.00 (Cum Laude)
Completed a final undergraduate thesis focused on developing a lightweight YOLOv8n model for lettuce phenotype estimation using RGB-D imagery.
Active member of the Contemporary Insights Department at IMILKOM USU, coordinating events such as the Imilkom Mini Bootcamp.
Have a project in mind or just want to say hi? Feel free to reach out to me. I'm always open for new opportunities and connections.