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All Journal Indonesian Journal on Computing (Indo-JC)
Muhammad Ilham Hafizha
Universitas Telkom
Computer Science & IT

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Diet and Physical Exercise Recommendation System Using a Combination of K-Means and Random Forest Muhammad Ilham Hafizha; Z. K. A. Baizal
Indonesia Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.959

Abstract

Public health has become a significant focus in this modern era due to the increasing number of people suffering from various diseases. Unhealthy diets and lack of physical activity are often associated with multiple health problems, one of which is obesity. Several studies have been conducted to develop food recommendation systems for individuals with obesity, using K-Means and Random Forest algorithms to provide food recommendations based on user-specific aspects. However, these studies do not provide supporting information, such as physical activity recommendations to address fitness issues or lack of physical activity. This study develops a diet and physical exercise recommendation system for individuals with obesity using a combination of K-Means and Random Forest. The system categorizes and classifies foods and physical exercises and provides customized recommendations based on user data analysis. The accuracy of the system was evaluated using the MAPE metric, with the highest accuracy for dietary food recommendations being 99.03% for the non-vegan lunch diet meal recommendation and the lowest being 70.74% for the vegan morning meal diet recommendation. The MAPE for physical exercise recommendations was consistently at 26.35%, indicating a stable accuracy of 73.65%. The test results show that the system recommends diet and physical exercise accurately.
Co-Authors ZK Abdurahman Baizal