Implementasi Model Convolutional Neural Network (CNN) pada Aplikasi Deteksi Kanker Kulit Menggunakan Expo React Native - Yonismara, Arvie Arvearie; Salam, Abu (Forum Kerjasama Pendidikan Tinggi, 2024)
Skin is the outermost organ of the human body, serving to protect the internal parts from threats such as sunlight exposure. Excessive exposure to sunlight can potentially cause skin cancer. Over the past decade, the number of skin cancer cases in Indonesia has increased. The most common method for detecting skin cancer is biopsy, which is quite expensive and time-consuming. Considering this issue, a skin cancer detection application using Deep Learning technology is needed to identify skin cancer at an early stage. Therefore, this research aims to develop a skin cancer detection application using Expo React Native and implement a CNN deep learning model to classify seven classes of skin lesions based on the HAM10000 dataset. The performance evaluation of the CNN model used shows a high performance score, with an average overall score of 0.98. Given this performance, the model is feasible and ready to be implemented in a mobile application. This study demonstrates that the skin cancer detection application using Expo React Native is capable of implementing the deep learning model and can be used to detect skin cancer. Based on the results of the application testing using the black box testing method, perfect results were obtained with 100% success precentage. From the four parts of the application, namely select image, open camera, predict image, and delete image that were tested, all four parts demonstrated that the functionality and features of the skin cancer detection application work well