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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science
Revanza Akmal Pradipta
Politeknik Perkapalan Negeri Surabaya
Computer Science & IT

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Using Bayesian Ridge Algorithm to Predict Effectiveness of Body Fat Measurement Rachma Yuni Andari; Revanza Akmal Pradipta; Denny Oktavina Radianto
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 1 (2023): MALCOM April 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i1.717

Abstract

Body fat is an important aspect in understanding and managing one's physical condition. Accurate measurement of body fat percentage is essential to help accurately plan future health plans. Currently, the method of measuring body fat is still traditional and quite difficult, so what is needed is a more effective method. The Bayesian Ridge Algorithm is a linear regression technique that uses Bayesian inference to estimate the parameters of the model. In this study, it was used to predict the effectiveness of measuring body fat, which is a method often used to evaluate a person's overall health and physical condition. This algorithm takes into account factors such as age, gender, and body mass index (BMI) to make predictions about a person's body fat percentage. The results from this study can be used to improve the accuracy of body fat measurement and help individuals better understand and manage their health. The results of this study indicate that the model has very high accuracy (more than 99%).
Co-Authors Rachma Yuni Andari Radianto, Denny Oktavina