Show simple item record

dc.contributor.advisorJaya, Ivan
dc.contributor.advisorNurhasanah, Rossy
dc.contributor.authorSetiayu, Ade Bunga Dwi
dc.date.accessioned2025-07-22T07:34:41Z
dc.date.available2025-07-22T07:34:41Z
dc.date.issued2025
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/106185
dc.description.abstractHypertension is classified as a non-communicable disease and is commonly known as “The Silent Killer” due to its minimal or absent symptoms. This condition is characterized by blood pressure exceeding the normal range. Commonly used methods to determine hypertension conditions currently involve examinations at healthcare facilities or self-monitoring using digital blood pressure monitors. However, both methods have limitations, such as requiring time to visit healthcare centers and the relatively high cost of devices for some segments of the population. As an alternative, the iridology approach offers a more accessible and affordable method for classifying hypertension conditions through visual analysis of iris images. One of the indicators of hypertension in iridology is the appearance of a gray or cloudy ring around the iris, known as the sodium ring. This ring indicates a metabolic imbalance caused by excessive salt consumption. However, further medical examination is necessary to prevent potential complications. This study utilized 300 iris images collected from Prof. Dr. Chairuddin P. Lubis Hospital, USU, and the general public in Padang Bulan Subdistrict, which were evenly divided into three categories comprising normal, prehypertension, and hypertension. Subsequently, the data were divided into training (70%), validation (20%), and testing (10%) subsets. The pre-processing steps included labeling, cropping, resizing, and augmentation. The EfficientNet architecture was chosen for its effectiveness in extracting visual features through compound scaling across depth, width, and resolution. The results confirmed the model’s ability to accurately categorize iris images into three classes, reaching a test accuracy of 93%. This performance was optimized by tuning the hyperparameters with 30 epochs and batch size 32. The developed system has the potential to serve as an alternative solution for hypertension classification and aids in facilitating early detection and intervention.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectHypertensionen_US
dc.subjectIridologyen_US
dc.subjectIrisen_US
dc.subjectImage Classificationen_US
dc.subjectEfficientNeten_US
dc.subjectAndroiden_US
dc.titleImplementasi Arsitektur EfficientNet pada Platform Android untuk Klasifikasi Penyakit Hipertensi Berdasarkan Citra Iris Mataen_US
dc.title.alternativeImplementation of EfficientNet Architecture on Android Platform for Classification of Hypertension Disease Based on Iris Imageen_US
dc.typeThesisen_US
dc.identifier.nimNIM211402008
dc.identifier.nidnNIDN0107078404
dc.identifier.nidnNIDN0001078708
dc.identifier.kodeprodiKODEPRODI59201#Teknologi Informasi
dc.description.pages114 Pagesen_US
dc.description.typeSkripsi Sarjanaen_US
dc.subject.sdgsSDGs 3. Good Health And Well Beingen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record