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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Scientific Journal of Informatics
Cinantya Paramita
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Development of ViScan: A Mobile Application for Skin Cancer Detection Using Ionic Framework and YOLOv10x Haresta, Alif Agsakli Haresta; Cinantya Paramita; William Dwiputra Tjahtjono
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9426

Abstract

Skin cancer is a common global health issue, with the number of cases continuing to rise worldwide. Early detection is crucial for improving patient outcomes, but traditional detection methods often require significant time, cost, and medical expertise. To address this challenge, this research focuses on developing a mobile application that leverages deep learning, specifically the YOLOv10x model, to enable fast and accurate detection of skin lesions. This application aims to provide an easy-to-use platform for self-monitoring skin health, particularly for individuals in remote areas with limited access to medical facilities. The system uses the HAM10000 dataset, which consists of a diverse collection of dermoscopy images of skin lesions, to train the YOLOv10x object detection model for real-time detection on mobile devices. By leveraging TensorFlow.js and Node.js, the model processes skin images and provides real-time results with precision and efficiency. The mobile application, developed using the Ionic Framework, ensures cross-platform compatibility and a responsive, intuitive user interface. System performance was evaluated using key metrics such as Precision (84.2%), Recall (86.3%), mAP (89.2%), and F1 Score (85.2%), demonstrating its effectiveness in early skin cancer detection. The potential of this application extends beyond detection, contributing to society by raising awareness and offering an accessible, low-cost screening solution.
Comparative Analysis of YOLOv5 and YOLOv8 Cigarette Detection in Social Media Content Cinantya Paramita; Catur Supriyanto; Amalia; Khalivio Rahmyanto Putra
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.2808

Abstract

Purpose: Addresses the pressing public health concern of tobacco product portrayal on social media, which significantly influences the younger demographic by glamorizing smoking culture. The purpose is to compare the capabilities of YOLOv5 and YOLOv8 models in detecting and censoring cigarette-related imagery on social media platforms, aiming to reduce exposure among children and teenagers. Methods: Employing a dataset of 2,188 images collected from Twitter, this research undertook a comprehensive methodology involving data preprocessing, YOLOv5 and YOLOv8 model training, and rigorous evaluation. The study utilized mean Average Precision (mAP) and F1-Score metrics to evaluate the performance of YOLOv5 and YOLOv8 models, focusing on their precision, recall, and efficacy in detecting cigarette and cigarette pack objects. Result: The analysis highlighted YOLOv8's superiority, with a marginally higher mAP value of 0.933 compared to YOLOv5's 0.919, alongside enhanced precision and recall rates. This result underscores YOLOv8's advanced object detection capabilities, owing to its architectural innovations and anchor-free detection system. Additionally, the study confirmed the absence of significant overfitting or underfitting issues, indicating robust learning processes of the models. Novelty: Innovates in digital public health by using YOLOv5 and YOLOv8 models to automatically censor tobacco-related content on social media, effectively reducing youth exposure to such imagery. YOLOv8, in particular, exhibits marginally superior detection capabilities. The evaluation results surpass those of previous research on cigarette and cigarette burning detection, underscoring the study's significant contribution to future research and public health initiatives.
Co-Authors Amalia Catur Supriyanto Haresta, Alif Agsakli Haresta Khalivio Rahmyanto Putra William Dwiputra Tjahtjono