Seminar Kudang Boro, Damayanthi Evy, Priandana Karlisa, Imantho Harry, Ligar Bonang Waspadadi, Seminar Annisa Utami, Krishnajaya Angga Dwi, Aditya Muhamad Reza, Suherman Muhammad Ilham Hakim, Fillah Ismy Fana
Faculty of Agricultural Engineering and Technology, IPB University, Darmaga Campus, Bogor, West Java, Indonesia.
Faculty of Human Ecology, IPB University, Dramaga Campus, Bogor, West Java, Indonesia.
Front Nutr. 2025 Apr 25;12:1590523. doi: 10.3389/fnut.2025.1590523. eCollection 2025.
The complexity surrounding food selection is attributable to the variability in foods, restaurants, and diners. The diversity of foods, where each dish may have a unique recipe across different restaurants, needs to be accounted for in personalized nutrition. However, personalized food selection poses a combinatorial challenge in selecting the most suitable food at a specific restaurant. The key question is how a diner visiting a particular restaurant can be assisted in selecting optimal foods and beverages based on factors such as sex, age, height, weight, and history of non-communicable diseases (NCDs). In this study, a genetic algorithm (GA) is used to develop a system that can address this issue in the context of Indonesian restaurants. In this system, a database with data on registered diners and foods is maintained. Foods comprise staple foods, side dishes, vegetables, and beverages, each containing its energy and nutrient content for a given restaurant. The nutritional adequacy of a single meal is determined by comparing the energy and nutrient content of the menu with the diner's nutritional needs. The novelty of the proposed system lies in combining scientific nutritional data with individual diner profiles for the selection of the best meal for a diner. This system differs from the existing food recommender applications in Indonesia, which typically do not consider specific diners, personalized nutrition, and NCD history. The proposed system is the first developed application prototype for Indonesian restaurants to overcome the inefficiency of the existing applications. In this study, the structure and chromosome content of the food, its corresponding energy and nutrient contents, and GA operators such as crossover, mutation, and tournament selection for determining the best meal using the defined fitness functions are discussed. The proposed system has been tested at Karimata Restaurant and proved to be highly suitable for the ultimate goal of meal selection for individual diners with different needs, and it can be replicated at other restaurants. Furthermore, user-centered evaluation has revealed that the system (a) increases nutritional understanding and health awareness; (b) is easy to use with comprehensive functions; and (c) promotes user satisfaction with personalized recommendations.
围绕食物选择的复杂性归因于食物、餐厅和用餐者的多样性。食物的多样性，即不同餐厅的每道菜可能都有独特的食谱，这在个性化营养中需要加以考虑。然而，个性化的食物选择在特定餐厅选择最合适的食物时带来了组合挑战。关键问题是，光顾特定餐厅的用餐者如何能基于性别、年龄、身高、体重和非传染性疾病（NCD）病史等因素，得到协助来选择最佳的食物和饮料。在本研究中，使用遗传算法（GA）开发了一个系统，该系统能在印尼餐厅的背景下解决这一问题。在这个系统中，维护了一个包含注册用餐者和食物数据的数据库。食物包括主食、配菜、蔬菜和饮料，每个都包含给定餐厅的能量和营养成分。通过将菜单的能量和营养成分与用餐者的营养需求进行比较，来确定单餐的营养充足性。所提出系统的新颖之处在于将科学营养数据与用餐者个人资料相结合，为用餐者选择最佳餐食。该系统不同于印尼现有的食物推荐应用程序，后者通常不考虑特定用餐者、个性化营养和非传染性疾病病史。所提出的系统是首个为印尼餐厅开发的应用原型，以克服现有应用程序的低效问题。在本研究中，讨论了食物的结构和染色体内容、其相应的能量和营养成分，以及用于使用定义的适应度函数确定最佳餐食的遗传算法算子，如交叉、变异和锦标赛选择。所提出的系统已在卡里马塔餐厅进行了测试，并被证明非常适合满足不同需求的个体用餐者的餐食选择这一最终目标，并且可以在其他餐厅复制。此外，以用户为中心的评估表明，该系统（a）提高了营养理解和健康意识；（b）功能全面且易于使用；（c）提高了用户对个性化推荐的满意度。
