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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Haris, H
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Implementation of Machine Learning Classification of Obesity Weight using Dicision Tree Putra, Fajar Rahardika Bahari; Surahmanto, Muhammad; Haris, H
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.354

Abstract

This work presents the application of the Decision Tree algorithm in the classification of obesity status using a machine learning approach. Indonesia is faced with three nutrition problems at once: stunting, wasting, and obesity or overnutrition. Obesity is a condition with excessive accumulation of body fat, which can lead to diseases and reduce quality of life. This study uses a dataset of 500 respondents and aims to classify obesity status early using the Decision Tree algorithm. The findings show that the developed Decision Tree model has an accuracy of 82%, with high precision and recall values, demonstrating the effectiveness of the algorithm in classifying obesity status. In conclusion, this study demonstrates the significant potential of the Decision Tree algorithm in supporting the early detection of obesity and facilitating more focused health interventions.
Implementation of Machine Learning Classification of Obesity Weight using Dicision Tree Putra, Fajar Rahardika Bahari; Surahmanto, Muhammad; Haris, H
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.354

Abstract

This work presents the application of the Decision Tree algorithm in the classification of obesity status using a machine learning approach. Indonesia is faced with three nutrition problems at once: stunting, wasting, and obesity or overnutrition. Obesity is a condition with excessive accumulation of body fat, which can lead to diseases and reduce quality of life. This study uses a dataset of 500 respondents and aims to classify obesity status early using the Decision Tree algorithm. The findings show that the developed Decision Tree model has an accuracy of 82%, with high precision and recall values, demonstrating the effectiveness of the algorithm in classifying obesity status. In conclusion, this study demonstrates the significant potential of the Decision Tree algorithm in supporting the early detection of obesity and facilitating more focused health interventions.
Co-Authors Putra, Fajar Rahardika Bahari Surahmanto