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All Journal ComEngApp : Computer Engineering and Applications Journal Jupiter Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Engagement: Jurnal Pengabdian Kepada Masyarakat
Alvi Syahrini Utami
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Allergen detection based on food packaged products for eczema patients using optical character recognition method Rini, Dian Palupi; Mardilah, Riska Tri; Rizqie, Muhammad Qurhanul; Indah, Dwi Rosa; Utami, Alvi Syahrini; Morgan, Jovanic
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.9739

Abstract

Eczema, also known as dermatitis, is a chronic skin condition that causes recurring episodes of dry and itchy skin. It can be managed through medication and by avoiding triggers like stress and certain foods. To help patients avoid food-related triggers, researchers conducted a study to detect allergenic food compositions in packaged products using optical character recognition (OCR) techniques, specifically open computer vision (OpenCV) and Tesseract. The study involved analyzing 120 images of food labels. The process included several steps: preprocessing the images by converting them to a text-friendly format (gray scaling, denoising, and thresholding), using Tesseract for text detection, followed by case folding and tokenization. The results showed that the system achieved an average text detection accuracy of 61.88% and an average allergen detection accuracy of 83.06%. The highest accuracy for text detection was 78.52%, and the highest accuracy for allergen detection was 100%. These findings suggest that OCR techniques can be a useful tool for helping eczema patients manage their diet and minimize flare-ups.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Abdiansyah, Abdiansyah Adi Kurniawan Ahmad Fali Oklilas Aisyah Filza Aliyah Ari Wedhasmara Armenia Yuhafiz Arya Yunanta Darmawahyuni, Annisa Desty Rodiah Dewi Sartika Dian Palupi Rini Dian Palupi Rini Dwi Rosa Indah Endang Lestari Ruskan Febrivia, Pretty Fujianti Harisatul Aulia Herman, Adelwin Kanda Januar Miraswan Mardilah, Riska Tri Mastura Diana Marieska Meylani Utari Mgs Afriyan Firdaus Morgan, Jovanic Mudinillah, Adam Muhammad Fachrurrozi Muhammad Qurhanul Rizqie Muhammad Qurhanul Rizqie Naura Qatrunnada Novi Yusliani Oktadini, Nabila Rizky Oktaviani, Elvira Osvari Arsalan Pratama, M Wahyu Rizki Kurniati Samsuryadi Samsuryadi Wiwik Anum Puspita YUNITA