Analisis Faktor Risiko Stunting pada Balita di Desa Kesetnana Menggunakan Metode Random Forest
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Stunting is a growth disorder triggered by chronic malnutrition, impacting the physical and cognitive development of young children. Kesetnana Village in South Central Timor Regency records a high prevalence of stunting. This study aims to classify stunting status using the Random Forest Classifier algorithm and assess its performance. The quantitative analysis was conducted on secondary data from 1,451 toddlers obtained through total sampling from the Health Center and Kesetnana Village Office in 2023. The variables analyzed include birth weight and height, measurement age, as well as Z-scores for Height/Age, Weight/Age, and Weight/Height. Data were processed using Python on the Google Colaboratory platform, with 75% allocated for training and 25% for testing. The findings indicate that birth weight, measurement age, and height are the primary factors in stunting classification. The model achieved 97% accuracy, with high precision and recall values, demonstrating its effectiveness in classifying stunting. This model can be utilized by health professionals and policymakers to identify stunting risk at an early stage and design targeted nutritional interventions in high-prevalence areas.
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Arnita, S., Rahmadhani, D. Y., & Sari, M. T. (2020). Hubungan pengetahuan dan sikap ibu dengan upaya pencegahan stunting pada balita di wilayah kerja Puskesmas Simpang Kawat Kota Jambi. Jurnal Akademika Baiturrahim Jambi, 9(1), 7. https://doi.org/10.36565/jab.v9i1.149
Islam, M. M., Shoukot Jahan Kibria, N. M., Kumar, S., Roy, D. C., & Karim, M. R. (2024). Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms. PLoS ONE, 19(12), e0315393. https://doi.org/10.1371/journal.pone.0315393
Iswahyudi, S. N. S., & Putra, R. E. (2025). Sistem Deteksi Stunting pada Balita Berbasis Web Menggunakan Metode Random Forest. Journal of Informatics and Computer Science (JINACS), 6(03), 755-764. https://doi.org/10.26740/jinacs.v6n03.p755-764.
Koro, S., Hadju, V., As’ad, S., & Bahar, B. (2018). Determinants of 6-24 Months Child Stunting in South Central Timor District, East Nusa Tenggara Province. Health Information : Jurnal Penelitian, 10(1), 1–10. https://doi.org/10.36990/hijp.v10i1.1
Landi, S., Timo, B., & Liufeto, M. (2024). Hubungan Pola Asuh Ibu Dan Riwayat Penyakit Infeksi Dengan Kejadian Stunting Pada Balita di Wilayah Kerja Puskesmas Nulle Kabupaten Timor Tengah Selatan Tahun 2024. Jurnal Pangan Gizi Dan Kesehatan, 13(2), 89-105. https://doi.org/10.51556/ejpazih.v13i2.367
Lestari, S. (2024). Prediction of Stunting in Toddlers Using Bagging and Random Forest Algorithms. Sinkron: jurnal dan penelitian teknik informatika, 8(2), 947-955. https://doi.org/10.33395/sinkron.v8i2.13448
Likhar, A., & Patil, M. S. (2022). Importance of maternal nutrition in the first 1,000 days of life and its effects on child development: a narrative review. Cureus, 14(10), e30083. https://doi.org/10.7759/cureus.30083
Mahesh, B. (2020). Machine learning algorithms: A review. International Journal of Science and Research (IJSR), 9(1), 381–386. https://doi.org/10.21275/art20203995
Putra Tupu Djoru, A., & Yulianto, S. (2025). Pendekatan machine learning untuk deteksi stunting pada balita menggunakan K-Nearest Neighbors (Skripsi, Universitas Kristen Satya Wacana). Perpustakaan Universitas Kristen Satya Wacana. https://repository.uksw.edu/handle/123456789/36443
Sahamony, N. F., Terttiaavini, T., & Rianto, H. (2024). Analisis perbandingan kinerja model machine learning untuk memprediksi risiko stunting pada pertumbuhan anak. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(2), 413–422. https://doi.org/10.57152/malcom.v4i2.1210
Sambriong, M., Herwanti, E., & Aty, Y. M. V. B. (2022). Edukasi gizi cegah stunting bagi orang tua di Desa Kesetnana, Kabupaten Timor Tengah Selatan. Poltekita: Jurnal Pengabdian Masyarakat, 3(4), 898–906. https://doi.org/10.33860/pjpm.v3i4.1284
Sevilla, J., Nugroho, A., & Turymshayeva, A. (2024). The effectiveness of accelerating stunting reduction policy. Journal of Sustainable Development and Regulatory Issues (JSDERI), 2(2), 132–147. https://doi.org/10.53955/jsderi.v2i2.31
SSGI. (2021). Survei Status Gizi Indonesia 2021. [Dataset].
Vianney Bita Aty, Y. M., Herwanti, E., & Mochsen, R. (2021). Simultaneous factors affecting toddler nutritional status. Jurnal Ilmu Kesehatan, 19(1), 64–72. https://doi.org/10.31965/infokes.Vol19i1.64
Wardani, L. K., Aulia, V., Hadhikul, M., & Kardila, M. (2023). Risks of stunting and interventions to prevent stunting. Journal of Community Engagement in Health, 6(2), 79–83. https://doi.org/10.30994/jceh.v6i2.528.