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Designing UI/UX Smart Mobile Healthcare for Early Skin Cancer Detection with The Integration of Convolutional Neural Network Model Fatihatuzzakia, Shafira; Kurniawan, Cipto; Nadifah, Umi
International Journal Scientific and Professional Vol. 4 No. 3 (2025): June-August 2025
Publisher : Yayasan Rumah Ilmu Professor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56988/chiprof.v4i3.101

Abstract

The rising number of skin cancer cases in recent decades and the limitations of early detection methods, such as costly and less accessible biopsies, highlight the need for an affordable, accessible solution. This study aims to design a smart mobile healthcare application integrating a Convolutional Neural Network (CNN) to detect skin cancer early through digital imaging. Using a dataset of 5,100 images categorized into melanoma, non-melanoma, and normal skin, the CNN model based on VGG16 architecture was trained and evaluated using accuracy, precision, recall, and F1-score. The model achieved 93.14% testing accuracy, 86.93% average training accuracy, and the F1-score 0.89. The UI/UX design follows the design thinking approach, emphasizing a user-friendly, fast, and interactive interface. Core features include user login, skin image scanning, classification results, and AI-based consultation. The application is intended to serve as an effective, accessible tool for early skin cancer detection, supporting timely clinical diagnosis for all users.
IMPLEMENTASI TEKNOLOGI BLOCKCHAIN UNTUK PENGELOLAAN DATA KESEHATAN MENGGUNAKAN METODE SMART CONTRACT Kurniawan, Cipto; Putrawansyah, Angga; Sutabri, Tata
JUTECH : Journal Education and Technology Vol 5, No 2 (2024): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v5i2.4174

Abstract

The development of information technology has had a significant impact on various sectors, including the health sector. This research aims to implement blockchain technology with smart contracts in health data management to create a secure, transparent, and efficient system. The methods used include needs analysis, smart contract development, and system simulation using the Ethereum platform and IPFS-based off-chain storage. Data was collected through literature studies, interviews, observations, and system simulations. The results show that the smart contract successfully automates the process of granting and revoking data access permissions, with an average transaction time of under 2 seconds. The system ensures data security through encryption, private key-based authentication, and immutable transaction records. Off-chain storage overcomes blockchain's capacity limitations for big data such as medical imaging results, without compromising privacy. However, this research faces the challenges of high transaction fees on public blockchains and scalability of the system at large user scales. This research contributes to the development of innovative solutions for health data management with full control by patients, while improving the transparency and efficiency of the system.