The general public lacks the tools and knowledge to easily identify authentic Indonesian handwoven (tenun) motifs, while traditional local weavers struggle with limited access to modern digital marketplaces to promote their craft.

Ruang Tenun is a mobile-based solution designed to preserve the heritage of traditional Indonesian handwoven fabrics (tenun) while empowering local artisans economically. The platform leverages a Convolutional Neural Network (CNN) model deployed on a cloud API to perform real-time motif classification, helping users identify the authenticity, history, and cultural origin of various fabrics (such as Ulos, Songket Palembang, and Endek Bali) from a simple photo. Additionally, Ruang Tenun bridges the gap between traditional craft and the digital economy by offering a location-aware marketplace. Using interactive map integrations, buyers can locate physical weaving shops nearby, contact the artisans, and purchase authentic products directly, creating a transparent and supportive digital ecosystem for local weavers.

1. / 01Trained a Convolutional Neural Network (CNN) model to recognize 9 distinct Indonesian tenun motifs, and deployed it onto a high-performance cloud server via Flask API to provide instant, remote visual search capability.
2. / 02Robust Mobile-to-Cloud Integration: Built a native Android app using MVVM architecture, integrating Retrofit to communicate with REST APIs for user session management, digital store creation with maps pinning, and automated image-processing pipelines.

• Instant & High-Accuracy Classification: Users can classify traditional motifs and view their cultural origin details through their mobile camera with an average API response time of under 2 seconds.
• Bridge to Digital Market for Local Weavers: Established a central hub showcasing local weaving businesses, mapping physical stores on Google Maps, and increasing digital visibility for authentic traditional craft.

• Authentication
• Inference Image
• Location