IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK DENGAN PRE-TRAINED MODEL MOBILENETV2 UNTUK DETEKSI KOLESTEROL
DOI:
https://doi.org/10.36341/rabit.v9i2.4732Keywords:
Cholesterol, Iridology, Convolutional Neural Network, Transfer Learning, MobileNetV2Abstract
One of the main risks for heart disease and stroke is cholesterol. Cholesterol is a type of fat produced primarily by the liver and absorbed in small amounts from food. The ideal cholesterol level in the human body should be less than 200 mg/dl. One way to check cholesterol levels is through a blood sugar test that requires the patient to fast for 10 to 12 hours. Given the dangers posed by high cholesterol levels, there is a need for an early, practical screening method to detect high cholesterol levels in the human body. Iridology is an analysis of the iris of the eye to detect health conditions and show the relationship between iris patterns and cholesterol levels. The iris has its own uniqueness because it can record the condition of all organs, body structures, and psychological states. Therefore, iridology can be an alternative medical analysis. This study proposes the use of a convolutional neural network algorithm using a pre-trained MobileNetV2 model. The iris image dataset used consists of 200 images classified into two classes: normal eye images and cholesterol eye images. The results of the study show that the proposed model can achieve an accuracy of 95%. These results indicate that this model has great potential as a practical and cost-effective tool for detecting cholesterol. Further research is needed with larger datasets to improve accuracy and validity.
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