![\"\"](\"https:\/\/fkg.ugm.ac.id\/wp-content\/uploads\/sites\/30\/2026\/07\/Glicovia-1024x576.jpeg\")
<\/figure>\n<\/figure>\n\n\n\nGlicovia was designed to improve public access to early diabetes screening through a technology that is both user-friendly and comfortable to use.\n\u201cWe wanted to develop a screening method that is not only accurate but also more comfortable for users. By utilizing saliva as the testing sample and integrating artificial intelligence with IoT technology, we hope to make early prediabetes detection more widely accessible, allowing the risk of diabetes to be reduced before the disease develops,\u201d Alya said on July 26.<\/p>\n\n\n\n
Unlike conventional blood tests, Glicovia uses saliva as the diagnostic sample, eliminating the need for blood collection. A saliva sample is placed on the device\u2019s sensor, which measures several indicators associated with prediabetes risk, including glucose concentration, salivary fructosamine, and pH level. These measurements are then analyzed by an artificial intelligence system to assess an individual's risk of prediabetes. The results are immediately displayed through a mobile application or digital platform, enabling users to monitor their health more conveniently.<\/p>\n\n\n\n
In addition to its non-invasive approach, Glicovia embraces the concept of green diagnostics. Using saliva as the testing sample has the potential to reduce the consumption of disposable materials commonly required for conventional blood-based testing. The integration of IoT technology also enables digital storage of patients\u2019 screening histories, making long-term health monitoring more effective, sustainable, and supportive of data-driven healthcare services.<\/p>\n\n\n\n
The development of Glicovia is currently about 70 percent complete. The research team has finalized the system design, developed the electrochemical sensor, integrated the device with IoT technology, and built an artificial intelligence model based on a Support Vector Machine (SVM). an artificial intelligence model based on a Support Vector Machine (SVM).\n\u201cWe are currently entering the validation phase using real saliva samples to ensure the accuracy and reliability of the detection results before advancing the device to the next stage of development,\u201d Alya added.<\/p>\n\n\n\n
Through the 2026 PKM-KC funding program, the team aims to advance Glicovia to the National Student Scientific Week (PIMNAS) while continuing its development into a practical, accurate, and accessible prediabetes screening device for both the general public and primary healthcare facilities.\nIn the future, the team hopes that Glicovia will not only strengthen diabetes prevention efforts through early detection but also pave the way for the broader implementation of innovative, homegrown non-invasive diagnostic technologies across Indonesia.<\/p>\n\n\n\n
Author: Alya Ramadhani
Editor: Fajar Budi Harsakti<\/p>","protected":false},"excerpt":{"rendered":"
Empat mahasiswa Universitas Gadjah Mada (UGM) mengembangkan inovasi yang diberi nama Glicovia, sebuah perangkat deteksi dini prediabetes non-invasif berbasis saliva yang mengintegrasikan teknologi sensor elektrokimia, kecerdasan buatan (artificial intelligence), dan Internet of Things (IoT). Inovasi ini dikembangkan sebagai solusi atas meningkatnya beban diabetes melitus yang masih menjadi tantangan kesehatan global, khususnya di Indonesia. Tim ini […]<\/p>\n","protected":false},"author":293,"featured_media":21294,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[136],"tags":[307,289,291,297],"class_list":["post-21292","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-berita-terbaru","tag-sdg-17-kemitraan-untuk-mencapai-tujuan","tag-sdg-3-kesehatan-dan-kesejahteraan-yang-baik","tag-sdg-4-pendidikan-berkualitas","tag-sdg-9-industri-inovasi-dan-infrastruktur"],"gutentor_comment":0,"yoast_head":"\n