Mohamad Rizal Syafi'i
Deep Learning
Batik Banyumasan
This project examines the effectiveness of deep learning in classifying Batik Banyumasan patterns. It compares a custom CNN and the pre-trained VGG16 model, incorporating Canny Edge Detection to enhance feature extraction. The study aims to improve classification accuracy, providing insights into the role of edge detection in deep learning while supporting cultural preservation and automated batik recognition.
Types of Shoes
Deep learning project that classifies different types of shoes using Convolutional Neural Networks (CNN). The model is trained on an image dataset of shoes and deployed as a web application using Flask. Users can upload an image, and the system will predict the shoe category.
Flowzy App
Fun and educational mobile application designed for children aged 3-12 years to learn about different types of flowers. The app utilizes deep learning to classify flowers into five categories, making it an engaging tool for early childhood education.