Rizky Maulana
Saputra.

Undergraduate thesis project comparing ShuffleNet, EfficientNet, ResNet, and MobileNet for freshwater fish disease classification using deep learning and computer vision.

Computer vision system that recognizes sign language gestures using a MobileNet-based CNN model. Designed as a prototype for real-time sign language translation.

Fitness-focused chatbot application deployed using Gradio and HuggingFace Spaces.

Computer vision model for recognizing workout exercises from images using a convolutional neural network. Deployed as an interactive demo using Gradio on HuggingFace Spaces.

NLP pipeline analyzing Play Store reviews of the MyTelkomsel app using lexicon-based labeling, multiple feature extraction methods, and machine learning models.

End-to-end ETL pipeline that scrapes data from a web source, performs transformation, and loads it into structured datasets with automated testing for reliability.