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All Journal INOVTEK Polbeng - Seri Informatika
Ananda Gilang Ariyanto
Pembangunan Jaya University
Computer Science & IT

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Low-Sugar Diet Recommendations for Bangrajanmuaythai Boxing Athletes Using Collaborative Filtering Ananda Gilang Ariyanto; Prio Handoko
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wxvz1f16

Abstract

Adjusting diet patterns according to nutritional requirements, training intensity, and an athlete's physical condition is often a challenge in implementing a healthy diet, particularly a low-sugar food diet. This study aims to develop an artificial intelligence (AI)-based recommendation system that can help boxing and Muay Thai athletes in implementing a more targeted diet program through food recommendations tailored to their individual behaviors and nutritional needs. The methods used are collaborative filtering with a nutrition scoring approach, athlete preference analysis, and dynamic nutrition planning. The results show that the developed system, namely the Smart Nutrition System, is able to provide recommendations based on similarities among athletes’ preferences and nutritional requirements, thus supporting more effective decision-making in managing athlete diet patterns. Furthermore, the Smart Nutrition System also has the potential to evolve into an “athlete intelligence nutrition platform" that supports the implementation of personalized nutrition for combat sports athletes to support athlete performance.
Co-Authors Prio Handoko