Optimisasi Algoritma Genetika dengan Particle Swarm Optimization (PSO) untuk Sistem Rekomendasi Diet Gizi bagi Penderita Diabetes

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Muhammad Misbahul Munir
Ade Pujianto
Haechal Aulia Muhali Lamuru


Diabetes, especially diabetic nephropathy, is a global health problem that is increasing in prevalence. This disease can cause various serious complications and even death. Despite the high cure rate associated with diabetes, it is important to improve the human body's immune system to reduce the risk of developing diabetes or diabetic nephropathy. One approach that can help is maintaining a diet with good nutritional coverage. This research aims to develop an artificial intelligence (AI) system that can provide recommendations for a good nutritional diet menu for diabetes sufferers. We propose the use of well-known genetic algorithms in decision making. However, to improve the accuracy and efficiency of the genetic algorithm, we will optimize it using the Particle Swarm Optimization (PSO) algorithm. The research method used is an experimental method, where we will conduct experiments to test the performance of the optimized genetic algorithm. It is hoped that the results of this research can be used as a basis for making scientific publications in accredited national journals as well as product patents for food menu recommendation systems for diabetes sufferers. The main contribution of this research is improving the performance of the genetic algorithm through the use of the PSO algorithm, which will help increase the accuracy of the nutritional diet recommendation system. In this way, it is hoped that the results of this research can provide significant benefits in efforts to prevent and manage diabetes and improve the quality of life of diabetes sufferers.

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