the journal of food Gıda 1300-3070 1309-6273 The Association of Food Technology 10.15237/10.15237/gida.GD24063 Food Nutritional Balance Gıda ve Beslenme Dengesi OPTİMİZASYON VE YAPAY ZEKÂ ALGORİTMALARI KULLANARAK MENÜ PLANLAMA YAZILIMI GELİŞTİRİLMESİ DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM https://orcid.org/0000-0001-5351-1865 Tarlak Fatih GEBZE TECHNICAL UNIVERSITY 10 10 2024 49 5 833 846 06 12 2024 09 17 2024 Copyright © 1976, The Journal of Food 1976 The Journal of Food

Günümüzde sağlık ve beslenme bilinci giderek artmakta, bu da kişiye özel beslenme önerilerinin ve gıda israfının azaltılmasının önemini vurgulamaktadır. Bu çalışmada, kullanıcılardan demografik veriler ve gıda tercihleri toplanarak yapay zeka modelleri ile analiz edilmiştir. Random Forest algoritması kullanılarak geliştirilen bir model, kullanıcıların gelecekteki tercihlerini tahmin etmek ve menü planlamasına rehberlik etmek üzere eğitilmiştir. Yapılan testler, yapay zeka ve optimizasyon tekniklerinin birleştirilmesinin kullanıcı odaklı menüler oluşturduğunu, memnuniyeti artırdığını ve gıda israfını azalttığını göstermiştir. Çalışma ayrıca, veri setinin boyutuyla ilgili zorluklara dikkat çekerek, daha nitelikli verilere olan ihtiyacı ortaya koymuştur. Geliştirilen model, toplu yemek hizmeti sunan catering şirketleri ve diğer kurumlar için yenilikçi çözümler sunarak çalışan memnuniyetini artırırken israfı da minimize etmektedir. Gelecek araştırmalar, modelin daha geniş uygulamalar için geliştirilmesini hedeflemektedir.

In today's world, awareness of health and nutrition is growing, emphasizing the need for personalized nutrition recommendations and reducing food waste. This study collected demographic data and food preferences from users and analyzed them using artificial intelligence models. A model developed with the Random Forest algorithm was trained to predict users' future preferences and guide menu planning. Tests showed that combining AI with optimization techniques successfully creates user-focused menus, enhancing satisfaction and reducing food waste. The study also highlighted challenges related to the dataset's size, pointing to a need for more qualitative data. The developed model provides innovative solutions for catering companies and institutions offering mass dining, improving employee satisfaction while minimizing waste. Future research aims to refine the model for broader applications.

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