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All Journal JOURNAL SCIENTIFIC OF MANDALIKA (JSM)
Discha Zahra Amanina
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Humanities Languange, Linguistic, Communication & Media Other

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Perancangan Alat Deteksi Alergi Berbasis Sensor Dengan Kecerdasan Buatan Giulia Salzano Badia; Intan Safira; Liala Syarifah Wahdani; Discha Zahra Amanina; Aris Febriyanto; Aripin
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 6 No. 11 (2025)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol6iss11pp4303-4312

Abstract

This research aims to develop a non-invasive allergy detection tool using artificial intelligence technology, specifically the Convolutional Neural Network (CNN) method. This tool is designed to detect allergic reactions caused by food through sensors applied to human skin. The research methodology includes literature study, data collection, design creation, system design, tool creation, and testing stages. This tool uses a camera to detect allergic reactions on the skin, which are then analyzed using an image processing algorithm with the CNN method integrated in a minicomputer. Data processing on skin reaction samples to allergic substances is divided into four classes, including atopic, angioedema, normal skin, and urticaria. The CNN algorithm used consists of several layers, including convolutional layers, pooling, and fully connected layers. The data collection process is carried out with 2 data, namely primary data and secondary data. Primary data collection is done by taking images of normal and allergic patient skin. Secondary data is obtained from Kaggle. The results of the study show that this tool prototype is able to detect changes in the skin surface due to allergic reactions, such as redness or swelling, quickly and accurately. Testing of this device yielded an accuracy rate of 92%, indicating its high accuracy in detecting allergic reactions
Co-Authors Aripin Aris Febriyanto Giulia Salzano Badia Intan Safira Liala Syarifah Wahdani