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Stunting sebagai dampak dari kekurangan gizi kronis, memiliki dampak yang signifikan terhadap kesehatan dan perkembangan anak di Indonesia. Penelitian ini mengembangkan model deteksi dini stunting pada anak dengan memanfaatkan indikator antropometri menggunakan pendekatan Machine Learning. Berbeda dengan metode tradisional yang bergantung pada penilaian manual atau pemeriksaan klinis yang memakan waktu, pendekatan ini menawarkan keunggulan berupa deteksi yang lebih cepat dan akurat. Data antropometri, seperti tinggi badan, berat badan, usia, dan jenis kelamin, digunakan dalam algoritma Machine Learning: Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, dan Support Vector Machine (SVM). Setiap model dievaluasi berdasarkan akurasi, confusion matrix, dan ROC Analysis untuk menentukan performa terbaik. Hasil penelitian menunjukkan bahwa Random Forest memiliki akurasi tertinggi sebesar 92,70%, lebih unggul dibandingkan dengan KNN yang memiliki akurasi 91,40%. Algoritma Random Forest dipilih sebagai model terbaik untuk deteksi dini stunting karena kemampuannya yang tinggi dalam meminimalkan kesalahan prediksi. Penerapan model ini diharapkan dapat meningkatkan efektivitas skrining cepat stunting di lapangan, memungkinkan deteksi lebih awal bagi anak-anak berisiko tinggi, dan mendukung intervensi yang lebih tepat sasaran dalam program kesehatan masyarakat.
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References
- M. L. W. Kinshella, S. E. Moore, and R. Elango, “The missing focus on women’s health in the First 1,000 days approach to nutrition,” Apr. 01, 2021, Cambridge University Press. doi: 10.1017/S1368980020003894.
- A. J. Prendergast and J. H. Humphrey, “The stunting syndrome in developing countries,” Paediatr Int Child Health, vol. 34, no. 4, pp. 250–265, Nov. 2014, doi: 10.1179/2046905514Y.0000000158.
- Kepala Biro Komunikasi dan Pelayanan Publik, “Membentengi Anak Dari Stunting.” [Online]. Available: https://link.kemkes.go.id/mediakom
- M. A. B. Ndetu, P. Weraman, and P. Romeo, “Factors Associated with Stunting in Children Under Five: A Cross-Sectional Study from Ritaebang Health Center, West Solor, East Flores, Indonesia,” Journal of Health Promotion and Behavior, vol. 8, no. 1, pp. 1–7, 2023, doi: 10.26911/thejhpb.2023.08.01.01.
- A. Khomsan et al., “Supplemental feeding and nutrition education to reduce stunting in Indonesian toddlers - The DASHAT programme,” Progress in Nutrition, vol. 26, no. 1, Mar. 2024, doi: 10.23751/pn.v26i1.15270.
- Diskominfo Pringsewu, “6 Pemkab Pringsewu Bentuk Jejaring Skrining Layak Hamil^J ANC^J dan Stunting,” p. 1, Aug. 2024, Accessed: Nov. 15, 2024. [Online]. Available: https://pringsewukab.go.id/portal/berita/read/pemkab-pringsewu-bentuk-jejaring-skrining-layak-hamil-anc-dan-stunting
- “7 Laporan Penyelengaraan Percepatan Penurunan Stunting Propinsi Lampung,” Jul. 2023, Accessed: Nov. 15, 2024. [Online]. Available: https://aksi.bangda.kemendagri.go.id/emonev/assets/uploads/laporan_pro/laporan_pro_18_periode_5_1691987988.pdf
- G. María Pita-Rodríguez et al., “Anthropometric indicators of nutritional status in children up to 2 years according to Urban or Rural Area, Cuba Anthropometric indicators of nutritional status in children up to 2 years according to Urban or Rural Area, Cuba Background and Objective.” [Online]. Available: https://www.researchgate.net/publication/265728181
- “Peraturan Menteri Kesehatan Republik Indonesia,” 2020. Accessed: Nov. 15, 2024. [Online]. Available: http://hukor.kemkes.go.id/uploads/produk_hukum/PMK_No__2_Th_2020_ttg_Standar_Antropometri_Anak.pdf
- S. Khare, S. Kavyashree, D. Gupta, and A. Jyotishi, “Investigation of Nutritional Status of Children based on Machine Learning Techniques using Indian Demographic and Health Survey Data,” in Procedia Computer Science, Elsevier B.V., 2017, pp. 338–349. doi: 10.1016/j.procs.2017.09.087.
- T. E. Putri, R. T. Subagio, Kusnadi, and P. Sobiki, “Classification System of Toddler Nutrition Status using Naïve Bayes Classifier Based on Z- Score Value and Anthropometry Index,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Nov. 2020. doi: 10.1088/1742-6596/1641/1/012005.
- I. P. Putri, T. Terttiaavini, and N. Arminarahmah, “Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, pp. 257–265, Jan. 2024, doi: 10.57152/malcom.v4i1.1078.
- A. Ridwan and T. N. Sari, “The comparison of accuracy between Naïve Bayes clasifier and c4.5 algorithm in classifying toddler nutrition status based on anthropometry index,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Feb. 2021. doi: 10.1088/1742-6596/1764/1/012047.
- O. N. Chilyabanyama et al., “Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia,” Children, vol. 9, no. 7, Jul. 2022, doi: 10.3390/children9071082.
- WHO (World Health Organization), “Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age Methods and development.” Accessed: Nov. 16, 2024. [Online]. Available: https://www.who.int/publications/i/item/924154693X
References
M. L. W. Kinshella, S. E. Moore, and R. Elango, “The missing focus on women’s health in the First 1,000 days approach to nutrition,” Apr. 01, 2021, Cambridge University Press. doi: 10.1017/S1368980020003894.
A. J. Prendergast and J. H. Humphrey, “The stunting syndrome in developing countries,” Paediatr Int Child Health, vol. 34, no. 4, pp. 250–265, Nov. 2014, doi: 10.1179/2046905514Y.0000000158.
Kepala Biro Komunikasi dan Pelayanan Publik, “Membentengi Anak Dari Stunting.” [Online]. Available: https://link.kemkes.go.id/mediakom
M. A. B. Ndetu, P. Weraman, and P. Romeo, “Factors Associated with Stunting in Children Under Five: A Cross-Sectional Study from Ritaebang Health Center, West Solor, East Flores, Indonesia,” Journal of Health Promotion and Behavior, vol. 8, no. 1, pp. 1–7, 2023, doi: 10.26911/thejhpb.2023.08.01.01.
A. Khomsan et al., “Supplemental feeding and nutrition education to reduce stunting in Indonesian toddlers - The DASHAT programme,” Progress in Nutrition, vol. 26, no. 1, Mar. 2024, doi: 10.23751/pn.v26i1.15270.
Diskominfo Pringsewu, “6 Pemkab Pringsewu Bentuk Jejaring Skrining Layak Hamil^J ANC^J dan Stunting,” p. 1, Aug. 2024, Accessed: Nov. 15, 2024. [Online]. Available: https://pringsewukab.go.id/portal/berita/read/pemkab-pringsewu-bentuk-jejaring-skrining-layak-hamil-anc-dan-stunting
“7 Laporan Penyelengaraan Percepatan Penurunan Stunting Propinsi Lampung,” Jul. 2023, Accessed: Nov. 15, 2024. [Online]. Available: https://aksi.bangda.kemendagri.go.id/emonev/assets/uploads/laporan_pro/laporan_pro_18_periode_5_1691987988.pdf
G. María Pita-Rodríguez et al., “Anthropometric indicators of nutritional status in children up to 2 years according to Urban or Rural Area, Cuba Anthropometric indicators of nutritional status in children up to 2 years according to Urban or Rural Area, Cuba Background and Objective.” [Online]. Available: https://www.researchgate.net/publication/265728181
“Peraturan Menteri Kesehatan Republik Indonesia,” 2020. Accessed: Nov. 15, 2024. [Online]. Available: http://hukor.kemkes.go.id/uploads/produk_hukum/PMK_No__2_Th_2020_ttg_Standar_Antropometri_Anak.pdf
S. Khare, S. Kavyashree, D. Gupta, and A. Jyotishi, “Investigation of Nutritional Status of Children based on Machine Learning Techniques using Indian Demographic and Health Survey Data,” in Procedia Computer Science, Elsevier B.V., 2017, pp. 338–349. doi: 10.1016/j.procs.2017.09.087.
T. E. Putri, R. T. Subagio, Kusnadi, and P. Sobiki, “Classification System of Toddler Nutrition Status using Naïve Bayes Classifier Based on Z- Score Value and Anthropometry Index,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Nov. 2020. doi: 10.1088/1742-6596/1641/1/012005.
I. P. Putri, T. Terttiaavini, and N. Arminarahmah, “Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, pp. 257–265, Jan. 2024, doi: 10.57152/malcom.v4i1.1078.
A. Ridwan and T. N. Sari, “The comparison of accuracy between Naïve Bayes clasifier and c4.5 algorithm in classifying toddler nutrition status based on anthropometry index,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Feb. 2021. doi: 10.1088/1742-6596/1764/1/012047.
O. N. Chilyabanyama et al., “Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia,” Children, vol. 9, no. 7, Jul. 2022, doi: 10.3390/children9071082.
WHO (World Health Organization), “Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age Methods and development.” Accessed: Nov. 16, 2024. [Online]. Available: https://www.who.int/publications/i/item/924154693X