PENDEKATAN NON-INVASIF UNTUK DETEKSI KOLESTEROL MENGGUNAKAN PENGOLAHAN CITRA IRIS DAN MACHINE LEARNING - Rifki Rohidin; Rizal Rachman (Department of Science and Technology Ibrahimy University, 2026)
High cholesterol levels are a major risk factor for cardiovascular disease, making early detection highly important. However, limited access to healthcare services and the high cost of laboratory examinations hinder routine screening within the community. This study aims to develop an Artificial Intelligence (AI)-based cholesterol detection system through iris image analysis as a practical and efficient solution. The Gray-Level Co-occurrence Matrix (GLCM) method is used to extract texture features from iris images, while Support Vector Machine (SVM) is applied to classify cholesterol levels. The system is designed as a web-based platform to improve accessibility, especially for communities in areas with limited healthcare facilities. Uploaded iris images are analyzed to produce cholesterol level classifications along with follow-up recommendations when high-risk conditions are detected. The expected outcomes of this research include a scientific article published in a nationally accredited Sinta 3 journal and Intellectual Property Rights in the form of copyright protection for the web application. The targeted Technology Readiness Level (TRL) is level 3 through experimental testing. This research is expected to contribute to improving access to innovative and sustainable early cholesterol detection.