IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK DENGAN PRE-TRAINED MODEL MOBILENETV2 UNTUK DETEKSI KOLESTEROL
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Abstract
High cholesterol levels are a major risk factor for heart disease and stroke. Cholesterol is a type of fat that is primarily produced by the liver and absorbed in small amounts from food. The ideal cholesterol level in the human body is less than 200 mg/dl. One way to check cholesterol levels is with a blood test that requires patients to fast for 10 to 12 hours. Given the dangers of high cholesterol levels, there is a need for a practical early screening method to detect high cholesterol levels in the human body. Iridology is the analysis of the iris to detect health conditions and shows the relationship between iris patterns and cholesterol levels. The iris has a unique ability to record the state of all organs, body structure, and psychological state. Therefore, iridology can be an alternative medical analysis. This study proposes the use of a convolutional neural network algorithm using the pre-trained MobileNetV2 model. The iris image dataset used consists of 200 images classified into two classes, namely normal eye images and cholesterol-prone eye images. The results of the study show that the proposed model is able to achieve an F1 score accuracy of 89.6%. This result shows that this model has great potential as a practical and cost-effective tool for detecting cholesterol. Further research is needed with a larger dataset to improve accuracy and validity.
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