OPTIMASI MODEL DETEKSI ALERGEN PADA PRODUK PANGAN DENGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN ADAPTIVE BOOSTING (ADABOOST)
DOI:
https://doi.org/10.47111/jti.v19i2.21316Keywords:
Allergen, Data Mining, Support Vector Machine, AdaBoost, Hand-Out Validation, Split Validation, Cross ValidationAbstract
One important aspect that needs to be considered in food production is food safety. The implementation of this food safety aspect includes food products that avoid contamination of chemical, physical, and biological substances that can be harmful to human health. In the implementation of the Makan Bergizi Gratis (MBG) program, problems were found related to allergies in the recipients of this assistance program. According to the World Health Organization (WHO), food allergies are ranked as the fourth most serious public health problem, and the only effective treatment for allergy sufferers is to avoid foods that contain allergens. Allergens themselves are compounds or food ingredients that cause allergies and/or intolerances. Laboratory tests of food products for allergen testing that are still carried out traditionally require a lot of time and money, making food producers reluctant to carry out product testing. A way to detect allergen content in food products that is easier, more practical, and more accurate is needed. The research conducted aims to build a prediction model that can be used to detect allergen content in food ingredients through the implementation of the Support Vector Machine (SVM) data mining algorithm optimized with the Adaptive Boosting ensemble learning boosting algorithm (AdaBoost). The research conducted obtained a model that produces the most optimal performance, namely SVM optimized with the AdaBoost algorithm with the split validation method.
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