MODEL KECERDASAN BUATAN UNTUK MEMPREDIKSI MALNUTRISI ANAK DAN MENDUKUNG PERANCANGAN KEBIJAKAN MAKANAN BERGIZI GRATIS
DOI:
https://doi.org/10.31004/prepotif.v9i3.53206Keywords:
child malnutrition, random forest, Artificial Intelligence, machine learning, MBG programAbstract
Malnutrisi pada anak masih menjadi permasalahan kesehatan masyarakat yang signifikan di Indonesia, khususnya di wilayah pesisir dan perdesaan. Identifikasi dini kelompok berisiko menjadi langkah krusial untuk meningkatkan efektivitas intervensi gizi, termasuk Program Makanan Bergizi Gratis (MBG). Penelitian ini bertujuan mengembangkan model prediksi risiko malnutrisi anak berbasis kecerdasan buatan menggunakan algoritma Random Forest serta menganalisis determinan utama yang memengaruhi risiko malnutrisi di Kabupaten Pesisir Selatan. Penelitian menggunakan pendekatan kuantitatif deskriptif dengan pengembangan model prediktif berbasis machine learning. Data sekunder diperoleh dari Badan Pusat Statistik (BPS), Dinas Kesehatan, dan Posyandu. Tahapan penelitian meliputi pra-pemrosesan data, pelatihan model Random Forest, dan evaluasi kinerja menggunakan confusion matrix, akurasi, precision, recall, serta K-fold cross-validation. Hasil evaluasi menunjukkan kinerja model yang baik dengan akurasi sebesar 83,5%, precision 0,81, dan recall 0,85. Analisis pentingnya variabel menunjukkan bahwa proporsi masyarakat tidak mampu merupakan faktor paling dominan yang memengaruhi risiko malnutrisi (28,9%), diikuti oleh tingkat partisipasi pendidikan menengah (SMP dan SMA). Pemetaan spasial mengungkapkan konsentrasi risiko tinggi di beberapa kecamatan, terutama Kambang, Salido, dan IV Koto Mudik. Secara keseluruhan, model prediksi berbasis Random Forest terbukti akurat dan aplikatif dalam mengidentifikasi wilayah serta kelompok berisiko, sehingga berpotensi meningkatkan ketepatan sasaran dan efisiensi pelaksanaan Program MBG.References
Ahmed, T., Ireen, S., Ahmed, A. S., Rahman, S., Islam, M. M., Alam, N., Hossain, M. I., Rahman, S. M., Ali, M. M., Choudhury, F. P., & Cravioto, A. (2019). Nutrition of Children and Women in Bangladesh: Trends and Directions for the Future. Journal of Health, Population and Nutrition, 30(1), 1–11. https://doi.org/10.3329/jhpn.v30i1.11268
Ayuni, D. T. (2025). Dilema Pelaksanaan Program Makan Bergizi Gratiss Pada Siswa. 8(8).
Black, R. E., Victora, C. G., Walker, S. P., Bhutta, Z. A., Christian, P., De Onis, M., Ezzati, M., Grantham-McGregor, S., Katz, J., Martorell, R., & Uauy, R. (2013). Maternal and child undernutrition and overweight in low-income and middle-income countries. The Lancet, 382(9890), 427–451. https://doi.org/10.1016/S0140-6736(13)60937-X
Chhetri. (2020). Machine learning approaches for predicting child malnutrition in developing countries. BMC Public Health, 20(1), 1–10.
De Sanctis, V., Soliman, A., Alaaraj, N., Ahmed, S., Alyafei, F., & Hamed, N. (2021). Early and Long-term Consequences of Nutritional Stunting: From Childhood to Adulthood: Early and Long-term Consequences of Nutritional Stunting. Acta Bio Medica Atenei Parmensis, 92(1), 11346. https://doi.org/10.23750/abm.v92i1.11346
Dirjen Kesehatan Lanjutan. (2023). Mengenal Lebih Jauh tentang Stunting. https://keslan.kemkes.go.id/view_artikel/2657/mengenal-lebih-jauh-tentang-stunting
Gebregergis, C. M. (2023). Socioeconomic determinants of Child Malnutrition: Evidence from Ethiopia. In Review. https://doi.org/10.21203/rs.3.rs-3253399/v1
Grehenson, G. (2025, May 6). Program MBG Dinilai Belum Efektif Cegah Stunting, Diminta Libatkan Ahli Gizi. Universitas Gadjah Mada. https://ugm.ac.id/id/berita/program-mbg-dinilai-belum-efektif-cegah-stunting-diminta-libatkan-ahli-gizi/
Kawo, M. A., Idris, M. N., Haruna, K., & Kaita, F. U. (2024). Predicting Undernutrition Risk Factors Using Machine Learning Techniques In Nigerian Under Five Children. International Journal of Computer Science and Mobile Computing, 13(7), 56–70. https://doi.org/10.47760/ijcsmc.2024.v13i07.006
Lestari, T. R. P. (2025). Evaluasi Kebijakan dan Strategi Penurunan Angka Stunting pada Tahun 2024. 16(1). https://doi.org/10.46807/aspirasi.v16i1.4800
Oumer, A., Girum, T., Fikre, Z., Bedewi, J., Nuriye, K., & Assefa, K. (2022). Stunting and Underweight, but not Wasting are Associated with Delay in Child Development in Southwest Ethiopia. Pediatric Health, Medicine and Therapeutics, Volume 13, 1–12. https://doi.org/10.2147/PHMT.S344715
Panteli, D., Adib, K., Buttigieg, S., Goiana-da-Silva, F., Ladewig, K., Azzopardi-Muscat, N., Figueras, J., Novillo-Ortiz, D., & McKee, M. (2025). Artificial intelligence in public health: Promises, challenges, and an agenda for policy makers and public health institutions. The Lancet Public Health, 10(5), e428–e432. https://doi.org/10.1016/S2468-2667(25)00036-2
Pemkab Pesisir Selatan. (2024). Upaya Pesisir Selatan Dalam Percepatan Penurunan Angka Stunting. https://www.pesisirselatankab.go.id/rberita/detail/upaya-pesisir-selatan-dalam-percepatan-penurunan-angka-stunting
Ramadhan, S. A., Widayani, S. T., Mahmudiono, T., Olivia, A. F., Hargiyanto, E. D., Permatasari, F. I., Azzahra, C. N., Sahila, N., Atmaka, D. R., Febrianto, E. C., Astuti, R. D. P., Mulia, A., Pratiwi, D. A., Nadhiroh, S. R., Simangunsong, T. T., Anif, A., Ismail, W. I., Talib, S. S. B., Sabri, N. B., & Keah, L. S. (n.d.). THE EFFECT OF NUTRITIONAL EDUCATION ON PARENTAL KNOWLEDGE OF BALANCED NUTRITION IN CHILDREN AT YAYASAN AN - NUSYUR AENG PANAS, SUMENEP MADURA.
Rokhanawati, D., Khofiyah, N., & Puspitasari, E. (2025). Maternal knowledge and Determinant Factors Related to Cognitive Development of Stunted Toddlers in Bantul Regency, Yogyakarta. Jurnal Kesehatan, 16(2), 112–122. https://doi.org/10.35730/jk.v16i2.1323
Ruel, M. T., & Alderman, H. (2013). Nutrition-sensitive interventions and programmes: How can they help to accelerate progress in improving maternal and child nutrition? The Lancet, 382(9891), 536–551. https://doi.org/10.1016/S0140-6736(13)60843-0
Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random Forest Algorithm Overview. Babylonian Journal of Machine Learning, 2024, 69–79. https://doi.org/10.58496/BJML/2024/007
UNICEF. (2025). Malnutrition in Children. UNICEF DATA. https://data.unicef.org/topic/nutrition/malnutrition/
Victora, C. G., Christian, P., Vidaletti, L. P., Gatica-Domínguez, G., Menon, P., & Black, R. E. (2021). Revisiting maternal and child undernutrition in low-income and middle-income countries: Variable progress towards an unfinished agenda. The Lancet, 397(10282), 1388–1399. https://doi.org/10.1016/S0140-6736(21)00394-9
WHO. (2024). Fact sheets—Malnutrition. https://www.who.int/news-room/fact-sheets/detail/malnutrition
Xu, X., Cai, H., Zhang, J., & Xia, T. (2024). The Effects of Parental Food Education on Children’s Food Literacy: The Mediating Role of Parent–Child Relationship and Learning Motivation. Nutrients, 16(15), 2564. https://doi.org/10.3390/nu16152564
Zhu, T. (2020). Analysis on the Applicability of the Random Forest. Journal of Physics: Conference Series, 1607(1), 012123. https://doi.org/10.1088/1742-6596/1607/1/012123
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