Comparison of K-Nearest Neighbor, Naive Bayes, Random Forest Algorithms for Obesity Prediction
DOI:
10.33395/sinkron.v9i1.14478Keywords:
Obesity, Data Classification, K-Nearest Neighbor, Naive-bayes, Data MiningAbstract
Obesity is a global health problem that continues to increase and has serious impacts on physical and mental health. This research aims to predict a person's obesity status based on certain attributes using the K-Nearest Neighbor (KNN), Naive Bayes, and Random Forest algorithms. The dataset used was taken from the Kaggle platform with 2,111 data and 16 attributes, including gender, age, weight, height, frequency of consumption of high-calorie foods, physical activity, and water and vegetable consumption patterns. The research process follows the data mining stages, including business understanding, data understanding, data preparation, modeling, evaluation, and documentation. Experiments were carried out using RapidMiner with a cross-validation technique using 10 folds to measure overall model performance. The research results show that the Random Forest algorithm performs best in predicting obesity status compared to K-NN and Naive Bayes. Model evaluation using accuracy, precision, recall, and F1-score metrics shows significant results in distinguishing obesity categories. It is hoped that this research can contribute to the development of a machine learning-based health prediction system that can be used to support decision-making in the prevention and management of obesity.
Downloads
References
Y. Niar, K. Komariah, A. Surip, R. Saputra, and I. Ali, “Implementasi Algoritma Naïve Bayes Untuk Prediksi Persediaan Barang Rotan,” KOPERTIP J. Ilm. Manaj. Inform. dan Komput., vol. 4, no. 1, pp. 28–34, 2022, doi: 10.32485/kopertip.v4i1.112.
A. R. Damanik, S. Sumijan, and G. W. Nurcahyo, “Prediksi Tingkat Kepuasan dalam Pembelajaran Daring Menggunakan Algoritma Naïve Bayes,” J. Sistim Inf. dan Teknol., vol. 3, pp. 88–94, 2021, doi: 10.37034/jsisfotek.v3i3.49.
S. Sinaga, R. W. Sembiring, and S. Sumarno, “Penerapan Algoritma Naive Bayes untuk Klasifikasi Prediksi Penerimaan Siswa Baru,” J. Mach., vol. 1, no. 1, pp. 55–64, 2022, [Online]. Available: https://journal.fkpt.org/index.php/malda/article/view/162%0Ahttps://journal.fkpt.org/index.php/malda/arti cle/download/162/115.
D. Larassati, A. Zaidiah, and S. Afrizal, “Sistem Prediksi Penyakit Jantung Koroner Menggunakan Metode Naive Bayes,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 7, no. 2, pp. 533–546, 2022, doi: 10.29100/jipi.v7i2.2842.
W. Ramdhani, D. Bona, R. B. Musyaffa, and C. Rozikin, “Klasifikasi Penyakit Kangker Payudara Menggunakan Algoritma K-Nearest Neighbor,” J. Ilm. Wahana Pendidik., vol. 8, no. 12, pp. 445–452, 2022.
G. A. Witata and J. Triloka, “Kajian Perbandingan Algoritma KNN Dan SVM Untuk Prediksi Pengangguran Di Provinsi Lampung,” Pros. Semin. Nas. Darmajaya, pp. 218–223, 2023.
R. Toro and S. Lestari, “Perbandingan Algoritma Data Mining Untuk Penentuan Lokasi Promosi Penerimaan Mahasiswa Baru Pada IIB Darmajaya Lampung,” Techno.Com, vol. 22, no. 1, pp. 223–234, 2023, doi: 10.33633/tc.v22i1.7118.
A. Samosir, M. S. Hasibuan, W. E. Justino, and T. Hariyono, “Komparasi Algoritma Random Forest, Naïve Bayesvdan K- Nearest Neighbor Dalam klasifikasi Data Penyakit Jantung,” Pros. Semin. Nas. Darmajaya, vol. 1, no. 0, pp. 214–222, 2021.
M. Kesuma, I. Informatika dan Bisnis Darmajaya, J. Z. Pagar Alam No, and B. Lampung, “Prediksi Penyakit Liver Menggunakan Algoritma Random Forest,” J. Inf. dan Komput., vol. 11, no. 2, p. 2023, 2023.
M. Syukri Mustafa and I. Wayan Simpen, “Implementasi Algoritma K-Nearest Neighbor (KNN) Untuk Memprediksi Pasien Terkena Penyakit Diabetes Pada Puskesmas Manyampa Kabupaten Bulukumba,” Februari, vol. 2019, no. 1, pp. 1–10, 2019.
N. Khairina, T. T. S. Sibarani, R. Muliono, Z. Sembiring, and M. Muhathir, “Identification of Pneumonia using The K-Nearest Neighbors Method using HOG Fitur Feature Extraction,” J. Informatics Telecommun. Eng., vol. 5, no. 2, pp. 562–568, 2022.
R. Dahlia et al., “Penerapan Data Mining Terhadap Data Covid - 19,” J. Inform., vol. 21, no. 1, pp. 44–52, 2021.
S. H. F. Hakim, I. Cholissodin, and A. W. Widodo, “Seleksi Fitur Dengan Particle Swarm Optimization Untuk Pengenalan Pola Wajah Menggunakan Naive Bayes ( Studi Kasus Pada Mahasiswa Universitas Brawijaya Fakultas Ilmu Komputer Gedung A ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 10, pp. 1045–1057, 2017.
L. Ratnawati and D. R. Sulistyaningrum, “Penerapan Random Forest untuk Mengukur Tingkat Keparahan Penyakit pada Daun Apel,” J. Sains dan Seni ITS, vol. 8, no. 2, 2020, doi: 10.12962/j23373520.v8i2.48517.
Widyaningrum, D. A., & Yuliana, F. (2021). Deteksi Dini dan Edukasi Tentang Pencegahan Obesitas Di Masa Pandemi Pada Masyarakat Desa Kuwon Kecamatan Karas Kabupaten Magetan. Jurnal Bhakti Civitas Akademika, 4(2), 23-28.
Sari, S. D., Budiman, M. A., Gogo Harahap, R. E., Qonsolanisota, G., Dawami, R., & Alleandra, T. (2023). Peningkatan Pengetahuan dan Deteksi Dini Obesitas pada Remaja di SMA Muhammadiyah 3 Jakarta. Jurnal SOLMA, 12(1), 256–261.
R. T. Aldisa, S. Alfarisi, and M. A. Abdullah, “Penerapan Metode Naïve Bayes Dalam Mendiagnosa Penyakit Leptospirosis,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 521–526, 2022, doi:10.47065/josyc.v3i4.2205.
I. Oktaviani and T. Triana, “Perancangan Aplikasi BMI Calculator Untuk Memprediksi Tingkat Obesitas Pada Mahasiswa Dengan Metode K-Nearest Neighbor,” Infokes J. Ilm. Rekam Medis dan Inform. Kesehat., vol. 13, no. 2, pp. 83–89, 2023, doi: 10.47701/infokes.v13i2.2790
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2025 Mia Andani, Joko Triloka, Suhendro Yusuf Irianto, Handoyo Widi Nugroho
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
