Klasifikasi Status Gizi Balita Menggunakan Algoritma Random Forest
Abstract
Nutritional problems in children under five remain a major challenge in Indonesia's health development as they can affect growth, cognitive abilities, and future productivity. The Indonesian Nutritional Status Survey (SSGI) serves as an important data source to understand the nutritional condition of children under five. This study aims to classify the nutritional status of children under five using the Random Forest algorithm with a Knowledge Discovery in Database (KDD) approach. The research stages include data cleaning, preprocessing, feature selection, modeling, and model performance evaluation. The data were obtained from Puskesmas Gang Sehat.The results indicate that Random Forest can classify the nutritional status of children under five with high accuracy for the majority class (Normal Nutrition), while performance for the minority class (Abnormal Nutrition) can still be improved. This demonstrates that the Random Forest algorithm is effective for classifying nutritional status, although optimizing data imbalance and adding supporting variables can enhance results for the minority class. This study is expected to contribute to the development of technology-based solutions for addressing nutritional issues in children under five.
Downloads
References
K. Khotimah, Permasalahan gizi balita masih menjadi salah satu tantangan utama dalam pembangunan kesehatan di Indonesia karena berdampak pada pertumbuhan fisik, perkembangan kognitif, serta produktivitas anak di masa depan., JISP (Jurnal Inov. Sekt. Publik), vol. 2, no. 1, pp. 113132, 2022.
Y. Nurdin and N. Khairun Saddami, Pengenalan Praktis Supervised Machine Learning: Dengan Jupyter Notebook. USK Press, 2025.
R. Irfannandhy, L. B. Handoko, and N. Ariyanto, Analisis Performa Model Random Forest dan CatBoost dengan Teknik SMOTE dalam Prediksi Risiko Diabetes, Edumatic J. Pendidik. Inform., vol. 8, no. 2, pp. 714723, 2024.
N. A. RIZKINA, KLASIFIKASI TINGKAT OBESITAS BERDASARKAN INDEKS MASA TUBUH MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINES (SVM), 2024, UNIVERSITAS PGRI SEMARANG.
L. F. V. Gunasari, N. E. Januariana, V. Lestari, and S. A. Marcello, Penilaian Dan Manajemen Permasalahan Gizi Balita. Penerbit Adab.
I. P. Putri, T. Terttiaavini, and N. Arminarahmah, Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak: Comparative Analysis of Machine Learning Algorithms for Predicting Child Stunting, MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 1, pp. 257265, 2024.
P. Handayani, A. C. Fauzan, and H. Harliana, Machine Learning Klasifikasi Status Gizi Balita Menggunakan Algoritma Random Forest, KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 6, pp. 30643072, 2024.
M. R. Akbar Ariyadi, S. Lestanti, and S. Kirom, KLASIFIKASI BALITA STUNTING MENGGUNAKAN RANDOM FOREST CLASSIFIER DI KABUPATEN BLITAR, JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 38463851, 2024, doi: 10.36040/jati.v7i6.7822.
P. W. Rahayu et al., Buku ajar data mining. PT. Sonpedia Publishing Indonesia, 2024.
T. Gori, A. Sunyoto, and H. Al Fatta, Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa, J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 1, pp. 215224, 2024.
A. Adrian and I. Verawati, Analisis Performa Logistic Regression dan Random Forest dalam Klasifikasi Kelayakan Penerimaan Kredit, Indones. J. Comput. Sci. Res., vol. 4, no. 2, pp. 148158, 2025.
W. Wijiyanto, A. I. Pradana, S. Sopingi, and V. Atina, Teknik K-fold cross validation untuk mengevaluasi kinerja mahasiswa, J. Algoritm., vol. 21, no. 1, pp. 239248, 2024.
D. Aprilianto and E. Rizal, Klasifikasi Penyakit Kanker Paru Menggunakan Algoritma Random Forest Berbasis Streamlit, METIK J. (AKREDITASI SINTA 3), vol. 9, no. 2, pp. 275283, 2025.
F. Paramudita, M. I. Zulfa, and A. Taryana, Implementasi Devops Pada Pengembangan Aplikasi Android Pendeteksi Kualitas Beras Berbasis Machine Learning, Transm. J. Ilm. Tek. Elektro, vol. 26, no. 3, pp. 105113, 2024.
A. Jalil, A. Homaidi, and Z. Fatah, Implementasi Algoritma Support Vector Machine Untuk Klasifikasi Status Stunting Pada Balita, G-Tech J. Teknol. Terap., vol. 8, no. 3, pp. 20702079, 2024.
S. Masripah and L. Ramayanti, Pengujian Black Box Pada Sistem Informasi Penerimaan Siswa Baru Berbasis Web, Inf. Syst. Educ. Prof. J. Inf. Syst., vol. 4, no. 1, pp. 1-, 2019.
M. I. U. Rosyidi and N. Rochmawati, Teknik Bagging Pada Algoritma Klasifikasi Decision Tree dan SVM Untuk Klasifikasi SMS Berbahasa Indonesia, J. Informatics Comput. Sci., vol. 5, no. 02, pp. 265271, 2023.
A. A. Irfita and M. Muttaqin, Implementasi Convolutional Neural Network (CNN) untuk Klasifikasi Jenis Jerawat Berbasis Web Menggunakan Streamlit, J. Nas. Teknol. Komput., vol. 5, no. 3, pp. 296311, 2025.
A. W. Septyanto and H. L. Hariyanto, Perbandingan Teknik Klasifikasi Catatan Medis untuk Indeks Antropometri Status Gizi Balita, J. Teknol. Dan Sist. Inf. Bisnis, vol. 6, no. 1, pp. 229235, 2024.
N. Nurussakinah, Implementasi Algortima Random Forest dan teknik SMOTE untuk klasifikasi penyakit diabetes, 2024, Universitas Islam Negeri Maulana Malik Ibrahim.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under an Attribution 4.0 International (CC BY 4.0) that allows others to share — copy and redistribute the material in any medium or format and adapt — remix, transform, and build upon the material for any purpose, even commercially with an acknowledgment of the work's authorship and initial publication in this journal.
