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All Journal Agrointek Food ScienTech Journal
David Satria Alamsyah
Universitas Negeri Malang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Immunology & microbiology

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Allergen Detection with Optimized Logistic Regression in Indonesian Cuisine to Enhance Food Safety Ahmad 'Ammar Musyaffa'; Aji Prasetya Wibawa; David Satria Alamsyah; Aldy Rahmat Yulianto; Adil Zakaria; Agung Bella Putra Utama
Food ScienTech Journal Vol 8, No 1 (2026)
Publisher : University of Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33512/fsj.v8i1.36462

Abstract

Traditional Indonesian dishes often contain peanuts, coconut milk, and shrimp which are common allergens. However, allergen information is frequently absent from food vendors and digital recipe sites, posing potential health risks for individual with food allergies. This study presents an automated allergen detection system in Indonesian cuisine that uses a Logistic Regression model and has been trained on 14 primary allergen categories defined by the European Union. Each recipe is converted into a fixed-dimension binary vector using a bag-of-ingredients feature representation. As the evaluation results, the hyperparameter tuning approach significantly improved the model's performance. The model that was not fine-tuned only performed well in Scenario 1 (0 and 1) where it achieved an accuracy of 0.9995. In the Scenario 2 (0 - 3) Grid Search CV improved accuracy to 0.9997. In the Scenario 3 (0 - 14) Random Search achieved the best values with an accuracy of 0.9990 and a balanced precision-recall rate of over 0.97. Compared to the other methods, Random Search appears to be more adaptable to complex data distributions as these results show. Furthermore, this method has the potential to be widely applied to various culinary contexts like oriental and continental cuisines, which often uses high-allergen ingredients such as fermented soy products and dairy-gluten rich dishes. This system contributes to the advancement of food safety and public health through the integration of artificial intelligence in allergen detection.
Allergen Classification and Ingredient Substitution Strategy for Indonesian Cuisine Using Clustered Random Forest Aldy Rahmat Yulianto; Aji Prasetya Wibawa; Ahmad ‘Ammar Musyaffa’; Adil Zakaria; David Satria Alamsyah; Agung Bella Putra Utama
AGROINTEK Vol 20, No 3 (2026)
Publisher : Agroindustrial Technology, University of Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/agrointek.v20i3.32625

Abstract

The lack of standardized recipes in Indonesian traditional cuisine hinders Micro, Small, and Medium Enterprises (MSMEs) from complying with food safety regulations, specifically allergen labeling. Current manual identification methods are inefficient, prone to bias, and prohibitively expensive for routine industrial screening. This study proposes a cost-effective, automated computational framework to detect allergen risks and support product reformulation. Utilizing a dataset of 9,921 recipes extracted from the Cookpad portal, the research applies a semi-supervised approach combining K-Means Clustering for risk labeling and Random Forest for classification, mapped to 14 EU allergen standards. To ensure industrial reliability, Data Augmentation and Random Over Sampling (ROS) were implemented to address class imbalances common in traditional recipes. The experimental results demonstrate that the model, optimized via Bayesian Optimization, achieves near-perfect accuracy (0.999) and high sensitivity (Recall 0.997), effectively minimizing the risk of false negatives critical for food safety. Furthermore, Bayesian Optimization proved to be 6.2 times faster than Grid Search, offering a computationally efficient solution for scalable implementation. Beyond detection, this study introduces an innovative "Ingredient Substitution Advisor" based on clustering patterns to assist food technologists in formulating hypoallergenic products. This framework contributes to the digitalization of food safety systems, providing an accurate, low-cost alternative to support HACCP implementation and certification for the agro-industry.
Co-Authors Adil Zakaria Agung Bella Putra Utama Ahmad 'Ammar Musyaffa' Ahmad ‘Ammar Musyaffa’ Aji Prasetya Wibawa Aldy Rahmat Yulianto