Deep Learning untuk mendeteksi gangguan lambung melalui citra iris mata

Harun Mukhtar; Baidarus; Eggy Aryanto; Yandiko Saputra Sy

doi:10.37859/coscitech.v4i3.6392

Harun Mukhtar Universitas Muhammadiyah Riau
Baidarus Universitas Muhammadiyah Riau
Eggy Aryanto Universitas Muhammadiyah Riau
Yandiko Saputra Sy Universitas Muhammadiyah Riau

https://doi.org/10.37859/coscitech.v4i3.6392

Keywords: Deep Learning, Image processing, Classification of iris images of patients with gastric disorders

Abstract

The stomach is one of the essential organs of the human digestive system. If the stomach organ cannot work typically, it will cause problems. This is a disease that occurs in the stomach organs. Gastric disease also occurs due to a lack of knowledge about stomach disease, so people ignore the symptoms that arise. Gastric disease is a disease that is considered very serious. If left alone, it can cause other diseases to occur. Generally, finding out the presence of stomach disease is still done manually, and several tests are carried out when stomach disease has recurred. Gastric disorders were classified using 360 iris images taken manually via a digital camera and a web database of iris images. The author used the Radial Basis Function Neural Network (RBFNN) method to classify iris images of patients with gastric disorders in this study. The results obtained from this research can organize the iris images of people with gastric disturbances. Classification of iris images of patients with gastric disorders achieved a training accuracy rate of 65.00%.

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References

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