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Inkka Kavita
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Computer Science & IT Electrical & Electronics Engineering

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Prediksi Risiko Obesitas Remaja Mengacu pada Konsumsi dan Olahraga Dengan Random Forest Inkka Kavita; Marselo Charly; Ajai Shan; Dirga Arefa Wibowo
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 2 (2025): Juli: Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i2.1250

Abstract

Teenager’s obesity is public health issue that needs attention because of the increasing risk of other diseases such as diabetes. This study purposes to build a system that can predict the risk of teenager’s obesity. The data is in the form of a secondary dataset that has 17 features that include eating habits, physical activity, and others. Random Forest is used because of its ability to handle high-dimensional data and produce accurate classifications. This system is console-based, where users can add their lifestyle data and get results in the form of obesity predictions with low, medium, and high levels. The results of the model evaluation show very good results, which 90% accurate and high consistency, recall, and f1-score values. This method shows stable and competitive performance compared to other algorithms such as Decision Tree and KNN. So the results are expected to be used as a learning and prevention tool to detect the risk of obesity in adolescents early on.
Prediksi Risiko Obesitas Remaja Berdasarkan Pola Makan dan Aktivitas Fisik Menggunakan Algoritma Random Forest Inkka Kavita; Marselo Charly; Ajai Shan; Dirga Arefa Wibowo
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 1 (2025): Maret-Juni : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i1.1264

Abstract

Obesity in adolescents is a growing public health concern due to its association with various chronic diseases such as type 2 diabetes, hypertension, and metabolic disorders. This study aims to develop an obesity risk prediction system for adolescents based on dietary patterns and physical activity data using the Random Forest algorithm. The data was obtained from a secondary dataset available on the Kaggle platform, comprising 2,111 entries and 17 features covering dietary habits, physical activity, and anthropometric characteristics. The Random Forest method was chosen for its ability to handle high-dimensional data and produce accurate classifications. The developed system is a console-based application where users can input their lifestyle data and receive obesity risk predictions in three levels: low, moderate, and high. Model performance evaluation showed excellent results with an accuracy of 95%, as well as consistently high precision, recall, and F1-score values. Compared to other algorithms such as KNN and Decision Tree, Random Forest demonstrated competitive and stable performance. The results of this study are expected to be utilized as an educational and preventive tool for early detection of obesity risk in adolescents.
Co-Authors Ajai Shan Dirga Arefa Wibowo Marselo Charly