M. Daffa Raygama
Informatics Student at Telkom University
Passionate software engineer and student specializing in machine learning, software development, and LLM integration in software development lifecycles.
Mastered Technologies
Go
Backend development with high performance
Java
Enterprise application development
Spring
Java framework for building robust applications
Dart
Programming language for Flutter development
Flutter
Cross-platform mobile app development
Docker
Containerization for applications
Python
Data science, machine learning, and automation
Cloud
Cloud infrastructure and deployment
SQL
Database design and management
And More
Additional technologies and frameworks in my toolkit
My Projects
Machine Learning Projects
Military Land Mines Classifier using KNN
A machine learning project that achieves 100% accuracy in classifying land mines based on magnetic anomaly data using the K-Nearest Neighbors algorithm.
Deposit Customer Predictions using KNN
A classification model that predicts potential deposit customers for a banking institution based on marketing campaign data, addressing challenges of imbalanced datasets.
Malaria Cell Image Classification
An ongoing deep learning project using convolutional neural networks to classify microscopic cell images as infected or uninfected with malaria parasites.
Software Engineering Projects
Microservice with Golang and Docker
An exploration of microservice architecture implemented with Go and Docker, featuring message queuing with RabbitMQ and containerized deployment using Docker Swarm.
Inventory Management Web Application
A full-stack inventory management system developed during my internship at the Advanced Software Engineering Laboratory, built with Go, Gin framework, ReactJS, and MySQL.
Featured Project
Sunkatsu App Project
Sunkatsu App is an app dedicated to a tenant in one of the Cafetaria in Telkom University. It features in-app chat, orders, and chatbot to help users with their experience. Technologies used are Spring-Boot, ReactJS, Tailwind, and Flutter for the mobile app.
- Tracking, updating, and management of menus only for specified user role
- Authentication and security with JWT
- In-app chat feature between users using Websockets and StompJS
- Chatbot to help users with their experience using LLM model: Qwen-2.5:3B
My Research
LLM Integration in Software Development Life Cycle
My research focuses on integrating Large Language Models (LLMs) into the Software Development Life Cycle, with a particular emphasis on managing Technical Debt. This work explores how AI can assist developers in identifying, prioritizing, and addressing technical debt throughout the development process.
The research aims to establish frameworks and methodologies that leverage the capabilities of LLMs to improve code quality, reduce maintenance costs, and enhance overall software development efficiency.
LLM Analysis
Automated code analysis using large language models
Code Quality
Improving code quality through AI-assisted refactoring
Debt Management
Strategies for identifying and managing technical debt
SonarQube
SonaQube and LLM integration for Technical Debt management