IDENTIFIKASI SEL DARAH ABNORMAL PADA PENYAKIT POLYCYTHEMIA VERA MENGGUNAKAN DEEP LEARNING: IDENTIFICATION OF ABNORMAL BLOOD CELLS IN POLYCYTEMIA VERA USING DEEP LEARNING - Valentino Tinambunan; Aldo Hia Aldo; Rianto Sahputra Berutu; Nurmala Sari; Saut Dohot Siregar (LPPM Universitas Abdurrab, 2026)
Polycythemia Vera is a blood disorder characterized by an excessive number of red blood cells. This study aims to identify abnormal blood cells using a deep learning method based on convolutional neural networks (CNN). The dataset used consists of 1,200 blood cell images, including 600 normal images and 600 abnormal images. The steps in the study include data pre-processing, training a convolutional neural network model, model testing, and implementing a web-based application using Streamlit. The research findings show that the CNN model can classify blood cell images very effectively, with a training accuracy of 98.33%, a validation accuracy of 97.92%, and a testing accuracy of 100% with a loss value of 0.0136. In addition, the application created is able to classify blood cell images quickly and automatically. Based on these results, the CNN method is proven to be effective in identifying abnormal blood cells in cases of Polycythemia Vera.