Abdurrahman Fakhrul

Key Skills

Software

Labelbox

Top Subject Matter

3D Visualization & Interior Design Data Labeling

AI Training in Computer Vision for Object Detection & Recognition

Text Generation & NLP Model Evaluation for Multilingual Applications

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Computer Programming Coding

Fine Tuning

Function Calling

Point Key Point

Prompt Response Writing SFT

Freelancer Overview

With 1-3 years of experience in data labeling and AI training, I bring a unique blend of skills in design, computer vision, and natural language processing. My background in 3D visualization and interior design enhances my attention to detail and ability to label complex visual data accurately, particularly in the fields of object detection and recognition. In addition to visual data labeling, I have experience working with large language models (LLMs), evaluating text generation, and supporting multilingual applications, especially in English, Indonesian, Japanese, and Arabic. My technical toolkit includes Adobe Suite, AutoCAD, Blender, and full stack development technologies (React, Next.js, TypeScript), which I use to bridge design with AI workflows. I specialize in creating labeled datasets that enhance AI model accuracy across various domains. This hybrid of design acumen and technical expertise enables me to contribute effectively to AI systems training, particularly in computer vision and NLP tasks. I am eager to leverage my skills to contribute to impactful projects and further refine AI technologies across diverse industries.

Labeling Experience

3D Visualization Data Labeling for Interior Design AI Training

LabelboxImageText GenerationFine Tuning

This project involved creating a labeled dataset to train AI models in recognizing and classifying various elements within 3D interior spaces, such as furniture, lighting, and decor. My role included using bounding boxes and segmentation to accurately label objects within complex room setups, as well as providing classifications for various design elements based on material, style, and function. The project involved over 10,000 images and 3D models, with strict quality control to ensure high accuracy in object recognition, essential for enhancing the model’s ability to perform real-time object detection and classification in virtual design environments.

This project involved creating a labeled dataset to train AI models in recognizing and classifying various elements within 3D interior spaces, such as furniture, lighting, and decor. My role included using bounding boxes and segmentation to accurately label objects within complex room setups, as well as providing classifications for various design elements based on material, style, and function. The project involved over 10,000 images and 3D models, with strict quality control to ensure high accuracy in object recognition, essential for enhancing the model’s ability to perform real-time object detection and classification in virtual design environments.

2022 - 2023

Education

Work History