Universitas Gadjah Mada, Indosat Ooredoo Hutchison, and NVIDIA Strengthen AI Collaboration through Technical Meeting
Yogyakarta, February 10, 2026 – Universitas Gadjah Mada (UGM), in collaboration with Indosat Ooredoo Hutchison (IOH) and NVIDIA, held a Technical Meeting & Tech Sharing Session on February 9–10, 2026, as a follow-up to the initiative of establishing an AI Center (Joint Center) in Indonesia. This meeting marked a strategic step in strengthening research–industry collaboration to build an integrated, scalable, and impactful national artificial intelligence (AI) ecosystem. The discussions focused on the progress of the UGM AI Center and the deep dive into three priority use cases: SmartAgri (Precision Agriculture), eNose-TB (Healthcare AI), and Tech4Disaster (AI for Resilience).
SmartAgri: Multimodal AI for Precision Agriculture
UGM presented the development of SmartAgri as a solution to challenges such as climate variability, limited data integration, and low agricultural efficiency. The technologies include Machine Learning for yield prediction and irrigation needs, Computer Vision (YOLO) for crop monitoring, multispectral UAVs, and decision support systems for precision fertilization.
Implementation through the SIPASI (Irrigation Modernization) program has demonstrated around a 25% increase in water efficiency and an improvement in the cropping index from 132% to 188%, indicating readiness for national-scale deployment.
Collaboration with NVIDIA and Indosat Ooredoo Hutchison focuses on developing multimodal AI using Jetson for edge inference, model training with NVIDIA TAO, orchestration via Metropolis, and 5G connectivity support for remote farm deployment.
eNose-TB: Fast and Non-Invasive TB Screening
In response to the high burden of Tuberculosis in Indonesia (approximately 1,060,000 cases annually), UGM introduced eNose-TB, a screening system based on Volatile Organic Compounds (VOC) using a Hybrid Edge–Cloud AI approach.
The system utilizes NVIDIA Jetson for real-time edge inference and IOH Sovereign Cloud with NVIDIA T4 GPUs for deep learning training and continuous learning. Internal models have shown approximately 86% sensitivity, with further improvements targeted through dataset harmonization and full migration to deep learning.
Tech4Disaster: AI for Disaster Resilience
In disaster mitigation, Tech4Disaster has deployed 37 sensor nodes and developed AI analytics for detection systems. Connectivity challenges are being addressed through optimization of IOH’s 5G network and integration of edge AI into autonomous drone systems.
Academic-to-Industry (A2I) Strategy
NVIDIA outlined the A2I roadmap as a platform to transition academic research into industrial adoption, including the finalization of the Joint Center, formal launch, and subsequent scaling and expansion phases. The meeting concluded with the establishment of technical action items for each project as concrete steps toward joint implementation.
This collaboration is expected to serve as a strategic model of academic–industry partnership in accelerating AI-driven digital transformation in Indonesia.
